Siraj Raval

I've built an app called VectorFunnel that automatically scores leads for marketing & sales teams! I used React for the frontend, Node.js for the backend, PostgreSQL for the database, and Tensorflow.js for scoring each lead in an excel spreadsheet. There are a host of other tools that I used like ClearBit's data API and various Javascript frameworks. If you have no idea what any of that is, that's ok I'll show you! In this video, I'll explain how I built the app so that you can understand how all these parts fit together. The learning goal here is to give you enough of an idea of how these tools work to be able to formulate a plan for your own marketing startup MVP (minimum viable product). Enjoy! Code for this video: https://github.com/llSourcell/Watch_Me_Build_a_Marketing_Startup Please Subscribe! And like. And comment. That's what keeps me going. Want more education? Connect with me here: Twitter: https://twitter.com/sirajraval Facebook: https://www.facebook.com/sirajology instagram: https://www.instagram.com/sirajraval I also have an ongoing challenge for any Wizard brave enough to start their own startup. See details + rewards here: https://github.com/llSourcell/Build-an-AI-Startup-with-PyTorch/blob/master/README.md Make Money with Tensorflow 2.0: https://youtu.be/WS9Nckd2kq0 How to Make Money with Tensorflow: https://www.youtube.com/watch?v=HhqhFbwiaig 7 Ways to Make Money with Machine Learning: https://www.youtube.com/watch?v=mrRfpiAwad0&t=2s Watch me Build an AI Startup: https://www.youtube.com/watch?v=NzmoPqte4V4&t=1823s Intro to Tensorflow: https://www.youtube.com/watch?v=2FmcHiLCwTU&list=PL2-dafEMk2A7EEME489DsI468AB0wQsMV Join us in the Wizards Slack channel: http://wizards.herokuapp.com/ Hit the Join button above to sign up to become a member of my channel for access to exclusive live streams! Join us at the School of AI: https://theschool.ai/ Signup for my newsletter for exciting updates in the field of AI: https://goo.gl/FZzJ5w And please support me on Patreon: https://www.patreon.com/user?u=3191693

What does the field of Artificial Intelligence look like in 2040? It's a really hard question to answer since there are still so many unanswered questions about the nature of reality and computing. In this episode, I'll make my best predictions about AI hardware, AI software, and the societal impact of AI in 2040. We'll cover quantum mechanics, neuromorphic computing, DNA storage, decentralized computing, basic income, and mind-body machines. Enjoy! Code for this video: https://github.com/llSourcell/quantum_machine_learning_LIVE/blob/master/Demo.ipynb Please Subscribe! And like. And comment. That's what keeps me going. Want more education? Connect with me here: Twitter: https://twitter.com/sirajraval Facebook: https://www.facebook.com/sirajology instagram: https://www.instagram.com/sirajraval More learning resources: https://www.youtube.com/watch?v=HhXg6568I3E&t=5s https://www.youtube.com/watch?v=5YxzWnbqaJI https://www.youtube.com/watch?v=WTnxE0wjZaM&t=2s https://www.youtube.com/watch?v=vrdVlMqK5vc https://www.youtube.com/watch?v=e_BOJS1BLj8&t=943s https://www.youtube.com/watch?v=DmzWsvb-Un4 https://www.youtube.com/watch?v=bSw-wcB6GZw&t=1s https://www.youtube.com/watch?v=LhtnECml-KI https://www.youtube.com/watch?v=AAO4oq2M_48 https://www.youtube.com/watch?v=Ewf_gBWBH2A Join us in the Wizards Slack channel: http://wizards.herokuapp.com/ Hit the Join button above to sign up to become a member of my channel for access to exclusive live streams! Join us at the School of AI: https://theschool.ai/ Signup for my newsletter for exciting updates in the field of AI: https://goo.gl/FZzJ5w And please support me on Patreon: https://www.patreon.com/user?u=3191693

I've built an app called NeuralFund that uses Tensorflow 2.0 to make automated investment decisions. I used Tensorflow 2.0 to train a transformer network on time series data that i downloaded using the Yahoo Finance API. Then, I used Tensorflow Serving + Flask to create a simple web app around it. I'll explain what the important parts you should know in Tensorflow 2.0 are, then I'll guide you through my code & thought process of building an AI startup using it. Enjoy! Code for this video: https://github.com/llSourcell/Make_Money_with_Tensorflow_2.0 Please Subscribe! And like. And comment. That's what keeps me going. Want more education? Connect with me here: Twitter: https://twitter.com/sirajraval Facebook: https://www.facebook.com/sirajology instagram: https://www.instagram.com/sirajraval How to Make Money with Tensorflow: https://www.youtube.com/watch?v=HhqhFbwiaig 7 Ways to Make Money with Machine Learning: https://www.youtube.com/watch?v=mrRfpiAwad0&t=2s Watch me Build an AI Startup: https://www.youtube.com/watch?v=NzmoPqte4V4&t=1823s Intro to Tensorflow: https://www.youtube.com/watch?v=2FmcHiLCwTU&list=PL2-dafEMk2A7EEME489DsI468AB0wQsMV Join us in the Wizards Slack channel: http://wizards.herokuapp.com/ Hit the Join button above to sign up to become a member of my channel for access to exclusive live streams! Join us at the School of AI: https://theschool.ai/ Signup for my newsletter for exciting updates in the field of AI: https://goo.gl/FZzJ5w And please support me on Patreon: https://www.patreon.com/user?u=3191693

I've built an automated therapist app called MindRelaxr using PyTorch and a host of other tools (Dialogflow, Tensorflow Lite, Firebase, ONNX, Paypal, and Android Studio). I'm going to show you how I integrated these tools together to build a paid service that uses AI generated Cognitive Behavioral Therapy techniques to help people reduce their depression and anxiety. This app uses a sentiment analysis model trained in PyTorch as well as Google's cloud natural language processing service 'dialogflow' to provide low cost therapy. Enjoy! Code for this video (includes the #AIStartupChallenge details): https://github.com/llSourcell/Build-an-AI-Startup-with-PyTorch Please Subscribe! And like. And comment. That's what keeps me going. Want more education? Connect with me here: Twitter: https://twitter.com/sirajraval Facebook: https://www.facebook.com/sirajology instagram: https://www.instagram.com/sirajraval 7 Ways to Make Money with Machine Learning: https://www.youtube.com/watch?v=mrRfpiAwad0&t=1s Start an AI Startup: https://www.youtube.com/watch?v=NzmoPqte4V4&t=1820s Write a Research Paper: https://www.youtube.com/watch?v=S47RIVkr978&t=2s How to Teach AI: https://www.youtube.com/watch?v=tczjZOLVjJM Interview Preparation: https://www.youtube.com/watch?v=5KB5KAak6tM&t=1s https://www.youtube.com/watch?v=OHhoLhYW2cg&t=9s https://www.youtube.com/watch?v=nMK94JlKRb4&t=2s Programming Competitions: https://www.youtube.com/watch?v=TffGdSsWKlA Automated Trading: https://www.youtube.com/watch?v=F2f98pNj99k&t=6s https://www.youtube.com/watch?v=ftMq5ps503w&t=6s https://www.youtube.com/watch?v=05NqKJ0v7EE&t=2s Join us in the Wizards Slack channel: http://wizards.herokuapp.com/ Hit the Join button above to sign up to become a member of my channel for access to exclusive live streams! Join us at the School of AI: https://theschool.ai/ Signup for my newsletter for exciting updates in the field of AI: https://goo.gl/FZzJ5w And please support me on Patreon: https://www.patreon.com/user?u=3191693

Algorithms govern so much of our lives and dating is no exception! In this video, I frame dating as a data science pipeline and demo how to use AI algorithms to help facilitate discovery, first impressions, and even intimacy. I'll also explain how collaborative filtering and text generation are being used today to match people together with code examples. I hope this gives you a deeper insight into the role technology is currently playing in human relationships. Enjoy! Code for this video: https://github.com/llSourcell/AI_for_Dating Please Subscribe! And like. And comment. That's what keeps me going. Want more education? Connect with me here: Twitter: https://twitter.com/sirajraval Facebook: https://www.facebook.com/sirajology instagram: https://www.instagram.com/sirajraval More Learning resources: https://www.youtube.com/watch?v=XdM6ER7zTLk&t=774s https://www.youtube.com/watch?v=9gBC9R-msAk http://www.cs.cmu.edu/~ribeiro/pdf/Tu_Ribeiro_Towsley_TR2014.pdf http://web.cs.ucla.edu/~yzsun/papers/snam2016.pdf https://aimm.online/ https://mashable.com/article/future-online-dating/#ua2Paxq2eZqp Join us in the Wizards Slack channel: http://wizards.herokuapp.com/ Hit the Join button above to sign up to become a member of my channel for access to exclusive live streams! Join us at the School of AI: https://theschool.ai/ Signup for my newsletter for exciting updates in the field of AI: https://goo.gl/FZzJ5w And please support me on Patreon: https://www.patreon.com/user?u=3191693

I'm going to build a medical imaging classification app called SmartMedScan! The potential customers for this app are medical professionals that need to scale and improve the accuracy of their diagnoses using AI. From ideation, to logo design, to integrating features like payments and AI into a single app, I'll show you my 10 step process. I hope that by seeing my thought process and getting familiar with the sequence of steps I'll demonstrate,, you too will be as inspired as I am to use this technology to do something great for the world. Enjoy! Code for this video: https://github.com/llSourcell/AI_Startup_Prototype Please Subscribe! And like. And comment. That's what keeps me going. Want more education? Connect with me here: Twitter: https://twitter.com/sirajraval Facebook: https://www.facebook.com/sirajology instagram: https://www.instagram.com/sirajraval More Learning resources: https://www.youtube.com/watch?v=FTr3n7uBIuE https://www.youtube.com/watch?v=9bbS-trc8ys&t=608s https://www.youtube.com/watch?v=mrRfpiAwad0 https://www.youtube.com/watch?v=HhqhFbwiaig&t=563s Join us in the Wizards Slack channel: http://wizards.herokuapp.com/ Hit the Join button above to sign up to become a member of my channel for access to exclusive live streams! Join us at the School of AI: https://theschool.ai/ Signup for my newsletter for exciting updates in the field of AI: https://goo.gl/FZzJ5w And please support me on Patreon: https://www.patreon.com/user?u=3191693

Natural Language Processing is a field of Artificial Intelligence dedicated to enabling computers to understand and communicate in human language. NLP is only a few decades old, but we've made significant progress in that time. I'll cover how its changed over the years, then show you how you can easily build an NLP app that can either classify or summarize text. This is incredibly powerful technology that anyone can freely use, I'll show you how to do it. Enjoy! Code for this video: https://github.com/llSourcell/bert-as-service Please Subscribe! And like. And comment. That's what keeps me going. Want more education? Connect with me here: Twitter: https://twitter.com/sirajraval Facebook: https://www.facebook.com/sirajology instagram: https://www.instagram.com/sirajraval More Learning resources: https://www.youtube.com/watch?v=0n95f-eqZdw http://mlexplained.com/2019/01/30/an-in-depth-tutorial-to-allennlp-from-basics-to-elmo-and-bert/ https://towardsdatascience.com/beyond-word-embeddings-part-2-word-vectors-nlp-modeling-from-bow-to-bert-4ebd4711d0ec https://gluon-nlp.mxnet.io/examples/sentence_embedding/bert.html Join us in the Wizards Slack channel: http://wizards.herokuapp.com/ Join us at the School of AI: https://theschool.ai/ And please support me on Patreon: https://www.patreon.com/user?u=3191693 Signup for my newsletter for exciting updates in the field of AI: https://goo.gl/FZzJ5w Hit the Join button above to sign up to become a member of my channel for access to exclusive content!

Nvidia is the inventor of the GPU! This tech company based in Silicon Valley has played a huge role in the deep learning revolution (which has relied primarily on GPUs for computing), and its transformed many industries. In this video, I interview Bryan Catanzaro, the Vice President of Applied Deep Learning Research at NVIDIA. Bryan got his PhD in AI from Berkeley, invented a language called Copperhead, and is an expert in parallel programming theory. Nvidia invited me to their annual conference, so I took the opportunity to ask Bryan 67 questions while walking through the halls. Enjoy! Please Subscribe! And like. And comment. That's what keeps me going. Want more education? Connect with me here: Twitter: https://twitter.com/sirajraval Facebook: https://www.facebook.com/sirajology instagram: https://www.instagram.com/sirajraval More Learning resources: https://ctnzr.io/ https://developer.nvidia.com/deep-learning https://www.youtube.com/watch?v=vOppzHpvTiQ&list=PL2-dafEMk2A7YdKv4XfKpfbTH5z6rEEj3 https://www.youtube.com/watch?v=Cr6VqTRO1v0&t=342s Join us in the Wizards Slack channel: http://wizards.herokuapp.com/ Join us at the School of AI: https://theschool.ai/ And please support me on Patreon: https://www.patreon.com/user?u=3191693 Signup for my newsletter for exciting updates in the field of AI: https://goo.gl/FZzJ5w Hit the Join button above to sign up to become a member of my channel for access to exclusive content!

Discrete Math is a subject everyone interested in Computer Science needs to understand. It consists of math branches like graph theory, set theory, number theory, & combinatorics. It helps create databases, algorithms, & security structures. In this video, I'll explain the most relevant topics in Discrete Math one by one as we try to decrypt the password for a SQL database. Along the way, we'll use discrete math in various ways. I wanted to see if I could summarize an important course I took in college in a single video. Enjoy! Code for this video: https://github.com/llSourcell/DiscreteMath Please Subscribe! And Like. And comment. Thats what keeps me going. Want more education? Connect with me here: Twitter: https://twitter.com/sirajraval instagram: https://www.instagram.com/sirajraval Facebook: https://www.facebook.com/sirajology More learning resources: http://discrete.openmathbooks.org/home.php https://cse.buffalo.edu/~rapaport/191/S09/whatisdiscmath.html https://www.cs.odu.edu/~toida/nerzic/content/intro2discrete/intro2discrete.html https://www.youtube.com/watch?v=YzfdL58virc Join us at the School of AI: https://theschool.ai/ Join us in the Wizards Slack channel: http://wizards.herokuapp.com/ Please support me on Patreon: https://www.patreon.com/user?u=3191693 Signup for my newsletter for exciting updates in the field of AI: https://goo.gl/FZzJ5w

I recently flew to Singapore to give a keynote on AI in healthcare and attend a panel discussion on the same topic hosted by School of AI and sponsored by Accenture. School of AI is a nonprofit with a goal of giving everyone on Earth a world-class AI education for free. This was a launch event for our first global hackathon called Health Hack that will be hosted in over 30 cities around the world by our Deans (community representatives). In this video, you'll learn about automated diagnostics, synthetic biology, drug discovery, and the ethical implications of AI. Enjoy! Link to the hackathon (RSVP at the city closest to you if there is one): https://www.theschool.ai/hackathon/ Please Subscribe! And Like. And comment. Thats what keeps me going. Want more education? Connect with me here: Twitter: https://twitter.com/sirajraval instagram: https://www.instagram.com/sirajraval Facebook: https://www.facebook.com/sirajology More learning resources: https://www.youtube.com/watch?v=DCcmFXXAHf4&t=248s https://www.youtube.com/watch?v=hY9Bc3mtphs&t=462s https://rockhealth.com/reports/demystifying-ai-and-machine-learning-in-healthcare/ https://healthcare.ai/ https://www.nvidia.com/en-us/deep-learning-ai/industries/healthcare/ Join us at the School of AI: https://theschool.ai/ Join us in the Wizards Slack channel: http://wizards.herokuapp.com/ Please support me on Patreon: https://www.patreon.com/user?u=3191693 Signup for my newsletter for exciting updates in the field of AI: https://goo.gl/FZzJ5w Hit the Join button above to sign up to become a member of my channel for access to exclusive content!

Unsupervised learning is the most exciting subfield of machine learning! Finding structure in unstructured data automatically sounds like a dream come true, no need to have a label! In this video, I'll demonstrate 2 types of unsupervised learning techniques; k means clustering and principal component analysis. We'll use these techniques on neural data from a patient suffering from seizures to see if we can locate the part of their brain in need of surgery to save their life. You'll laugh, you'll cry, but most importantly, you'll learn. Enjoy! Code for this video: https://github.com/llSourcell/spike_sorting Please Subscribe! And Like. And comment. Thats what keeps me going. Want more education? Connect with me here: Twitter: https://twitter.com/sirajraval instagram: https://www.instagram.com/sirajraval Facebook: https://www.facebook.com/sirajology More learning resources: https://blog.algorithmia.com/introduction-to-unsupervised-learning/ http://deeplearning.stanford.edu/tutorial/ https://towardsdatascience.com/unsupervised-learning-with-python-173c51dc7f03 https://medium.com/machine-learning-for-humans/unsupervised-learning-f45587588294 Join us at the School of AI: https://theschool.ai/ Join us in the Wizards Slack channel: http://wizards.herokuapp.com/ Please support me on Patreon: https://www.patreon.com/user?u=3191693 Signup for my newsletter for exciting updates in the field of AI: https://goo.gl/FZzJ5w Hit the Join button above to sign up to become a member of my channel for access to exclusive content!

Is it possible to use machine learning without needing to code? The answer is yes! Uber's AI lab recently open sourced python library called Ludwig that they've been using internally for 2 years. The tagline is that it allows anyone to use deep learning without coding. It will require some configuration and unix commands to setup, but I'll show you how in this video. I'll also talk about other code-free tools like Azure ML Studio, DataRobot, and DeepCognition. Enjoy! Code for this video: https://github.com/llSourcell/ludwig Please Subscribe! And Like. And comment. Thats what keeps me going. Want more education? Connect with me here: Twitter: https://twitter.com/sirajraval instagram: https://www.instagram.com/sirajraval Facebook: https://www.facebook.com/sirajology More learning resources: https://uber.github.io/ludwig/ https://azure.microsoft.com/en-us/services/machine-learning-studio/ https://www.datarobot.com/ https://deepcognition.ai/ Join us at the School of AI: https://theschool.ai/ Join us in the Wizards Slack channel: http://wizards.herokuapp.com/ Please support me on Patreon: https://www.patreon.com/user?u=3191693 Signup for my newsletter for exciting updates in the field of AI: https://goo.gl/FZzJ5w Hit the Join button above to sign up to become a member of my channel for access to exclusive content! #MachineLearning #SirajRaval

The AI research lab DeepMind created an algorithm that beat a top professional StarCraft 2 player for the first time! This is a huge achievement since this is an incredibly complex game that requires long term planning, game theory, and cooperative play. Their algorithm used a mixture of techniques that in the field of deep reinforcement learning. I'll explain how each of these techniques works, and how they all work together in unison. This is an exciting time for the field. Enjoy! Code for this video: https://github.com/llSourcell/pysc2 Please Subscribe! And Like. And comment. Thats what keeps me going. Want more education? Connect with me here: Twitter: https://twitter.com/sirajraval instagram: https://www.instagram.com/sirajraval Facebook: https://www.facebook.com/sirajology More learning resources: https://deepmind.com/blog/alphastar-mastering-real-time-strategy-game-starcraft-ii/ https://www.vox.com/future-perfect/2019/1/24/18196177/ai-artificial-intelligence-google-deepmind-starcraft-game https://medium.freecodecamp.org/an-intro-to-advantage-actor-critic-methods-lets-play-sonic-the-hedgehog-86d6240171d http://www.dcsc.tudelft.nl/~bdeschutter/pub/rep/10_003.pdf Join us at the School of AI: https://theschool.ai/ Join us in the Wizards Slack channel: http://wizards.herokuapp.com/ Please support me on Patreon: https://www.patreon.com/user?u=3191693 Signup for my newsletter for exciting updates in the field of AI: https://goo.gl/FZzJ5w Hit the Join button above to sign up to become a member of my channel for access to exclusive content!

OpenAI has the entire AI community debating its decision to not release the fully trained version of its powerful new text generator model dubbed GPT-2. I'm going to explain how GPT-2 works using code, math, and animations. We'll discuss its potential applications (both good and bad), ways of preventing misuse, and at the end of the video I'll give my take on whether OpenAI was justified in doing so. The transformer architecture is quickly replacing recurrent networks for sequence learning, and OpenAI's GPT-2 is the latest example of using it at scale. Enjoy! Code for this video: https://github.com/openai/gpt-2 Please Subscribe! And Like. And comment. Thats what keeps me going. Want more education? Connect with me here: Twitter: https://twitter.com/sirajraval instagram: https://www.instagram.com/sirajraval Facebook: https://www.facebook.com/sirajology More learning resources: https://medium.com/@asierarranz/i-have-created-a-website-to-query-the-gpt-2-openai-model-11dd30e1c8b0 https://blog.openai.com/better-language-models/ http://jalammar.github.io/illustrated-transformer/ https://mchromiak.github.io/articles/2017/Sep/12/Transformer-Attention-is-all-you-need/#.XHVUts9KiLI Web Demo of GPT-2: https://www.askskynet.com/ Gradient Descent: https://www.youtube.com/watch?v=XdM6ER7zTLk&t=774s Fakebox: https://machinebox.io/docs/fakebox Privacy tools: https://github.com/OpenMined/PySyft/tree/master/examples/tutorials Join us at the School of AI: https://theschool.ai/ Join us in the Wizards Slack channel: http://wizards.herokuapp.com/ Please support me on Patreon: https://www.patreon.com/user?u=3191693 Signup for my newsletter for exciting updates in the field of AI: https://goo.gl/FZzJ5w

Data Science coding challenge time! The popular Data Science competition website Kaggle has an ongoing competition to solve the problem of earthquake prediction. Given a dataset of seismographic activity from a laboratory simulation, participants are asked to create a predictive model for earthquakes. In this video, I'll attempt the challenge as a way to teach 3 concepts; the Data Science mindset, Categorical Boosting, and Support Vector Regression models. I'll be coding this using python from start to finish in the online Google colab environment. Enjoy! Code for this video: https://github.com/llSourcell/Kaggle_Earthquake_challenge Please Subscribe! And Like. And comment. Thats what keeps me going. Want more education? Connect with me here: Twitter: https://twitter.com/sirajraval instagram: https://www.instagram.com/sirajraval Facebook: https://www.facebook.com/sirajology Join us at the School of AI: https://theschool.ai/ More learning resources: https://www.kaggle.com/c/LANL-Earthquake-Prediction/data https://www.analyticsvidhya.com/blog/2017/08/catboost-automated-categorical-data/ https://blog.griddynamics.com/xgboost-vs-catboost-vs-lightgbm-which-is-best-for-price-prediction/ https://towardsdatascience.com/catboost-vs-light-gbm-vs-xgboost-5f93620723db https://accio.github.io/machinelearning/2018/05/30/catboost.html http://kernelsvm.tripod.com/ https://www.saedsayad.com/support_vector_machine_reg.htm https://medium.com/coinmonks/support-vector-regression-or-svr-8eb3acf6d0ff https://scikit-learn.org/stable/modules/generated/sklearn.svm.SVR.html Join us in the Wizards Slack channel: http://wizards.herokuapp.com/ Please support me on Patreon: https://www.patreon.com/user?u=3191693 Signup for my newsletter for exciting updates in the field of AI: https://goo.gl/FZzJ5w

Everyone needs to understand regression! Its a useful data science technique that allows us to understand the relationship between different variables. In this video, we'll play the role of a newly hired data analyst at a genetics company trying to find the relationship between advertising mediums (TV, newspaper, radio) and ticket sales to our newly opened theme park. Along the way, we'll learn about 5 types of regression models (linear, non-linear, multiple, lasso, and ridge). Expect math, code, and layers of explanation. Enjoy! Code for this video: https://github.com/llSourcell/ISL-Ridge-Lasso Please Subscribe! And Like. And comment. Thats what keeps me going. Want more education? Connect with me here: Twitter: https://twitter.com/sirajraval instagram: https://www.instagram.com/sirajraval Facebook: https://www.facebook.com/sirajology More learning resources: https://www.youtube.com/watch?v=XdM6ER7zTLk https://www.analyticsvidhya.com/blog/2017/06/a-comprehensive-guide-for-linear-ridge-and-lasso-regression/ http://statisticsbyjim.com/regression/choose-linear-nonlinear-regression/ https://hbr.org/2015/11/a-refresher-on-regression-analysis http://blog.minitab.com/blog/adventures-in-statistics-2/how-to-interpret-regression-analysis-results-p-values-and-coefficients Join us at the School of AI: https://theschool.ai/ Join us in the Wizards Slack channel: http://wizards.herokuapp.com/ Please support me on Patreon: https://www.patreon.com/user?u=3191693 Signup for my newsletter for exciting updates in the field of AI: https://goo.gl/FZzJ5w

There are so many different ways to visualize data! We're going to learn about the major types of visualizations (relationships, correlations, comparisons) as well as discuss some of the many powerful visualization tools that are available to us. Google Charts, Tableau, there are a lot! We'll break down the visualization process along many dimensions, revealing the subtleties that can make or break a chart. Using the plotly library. we'll visualize a Medium blog post dataset many different ways. Enjoy! Code for this video: https://github.com/llSourcell/Data_Visualization Please Subscribe! And Like. And comment. Thats what keeps me going. Want more education? Connect with me here: Twitter: https://twitter.com/sirajraval instagram: https://www.instagram.com/sirajraval Facebook: https://www.facebook.com/sirajology Join us at the School of AI: https://theschool.ai/ More learning resources: https://medium.com/@Infogram/18-data-visualization-resources-for-education-and-inspiration-529c6f528983 https://machinelearningmastery.com/data-visualization-methods-in-python/ http://newcoder.io/dataviz/intro/ https://www.kaggle.com/benhamner/python-data-visualizations https://towardsdatascience.com/data-science-with-python-intro-to-data-visualization-and-matplotlib-5f799b7c6d82 https://realpython.com/python-data-visualization-bokeh/ Join us in the Wizards Slack channel: http://wizards.herokuapp.com/ Please support me on Patreon: https://www.patreon.com/user?u=3191693 Signup for my newsletter for exciting updates in the field of AI: https://goo.gl/FZzJ5w

Statistics is crucial to Data Science! In fact, the phrase Data Science was first used in a Statistics conference title. In this video, I'll cover 3 key concepts from Statistics that every Data Scientist needs to know. Statistical features, probability distributions, and Bayesian statistics will be explained using code, theory, and animations. Our specific application will be finding an optimal credit score for someone using Lending Club's loan data. Expect a musical interlude. Enjoy! Code for this video: https://github.com/llSourcell/LoanDefault-Prediction Please Subscribe! And Like. And comment. Thats what keeps me going. Want more education? Connect with me here: Twitter: https://twitter.com/sirajraval instagram: https://www.instagram.com/sirajraval Facebook: https://www.facebook.com/sirajology Join us at the School of AI: https://theschool.ai/ More learning resources: https://www.kdnuggets.com/2018/12/introduction-statistics-data-science.html https://towardsdatascience.com/the-5-basic-statistics-concepts-data-scientists-need-to-know-2c96740377ae https://www.kdnuggets.com/2017/11/10-statistical-techniques-data-scientists-need-master.html https://www.learndatasci.com/tutorials/data-science-statistics-using-python/ https://towardsdatascience.com/probability-and-statistics-explained-in-the-context-of-deep-learning-ed1509b2eb3f https://www.datascience.com/blog/statistics-data-science-interview https://www.datasciencecentral.com/profiles/blogs/29-statistical-concepts-explained-in-simple-english-part-1 Join us in the Wizards Slack channel: http://wizards.herokuapp.com/ Please support me on Patreon: https://www.patreon.com/user?u=3191693 Signup for my newsletter for exciting updates in the field of AI: https://goo.gl/FZzJ5w

This past week Kaggle invited me to attend an event they hosted called Kaggle Days in Paris, France. Kaggle Days are a global series of offline events for seasoned data scientists and Kagglers. It was a lot of fun! I met some really interesting people from all over the world and the venue they booked is the biggest startup campus in the world (Station F). In this video, I'll introduce Kaggle Days, interview 5 top data scientists, and conclude with a demo from a top Kaggler on inventory prediction. Enjoy! Code for this video: https://github.com/llSourcell/invent-prediction Please Subscribe! And Like. And comment. Thats what keeps me going. Want more education? Connect with me here: Twitter: https://twitter.com/sirajraval instagram: https://www.instagram.com/sirajraval Facebook: https://www.facebook.com/sirajology More learning resources: https://kaggledays.com/ https://www.youtube.com/watch?v=suRd3UzdBeo https://www.youtube.com/watch?v=P1lgTATSVYA Join us at the School of AI: https://theschool.ai/ Join us in the Wizards Slack channel: http://wizards.herokuapp.com/ Please support me on Patreon: https://www.patreon.com/user?u=3191693 Signup for my newsletter for exciting updates in the field of AI: https://goo.gl/FZzJ5w

Welcome to Data Lit! This 3 month course is an intro to data science for beginners. In this video, i'll explain how a popular data science technique called sentiment analysis works using a real-world scenario. We'll play the role of a data scientist working at a startup making a personal healthcare device. Using sentiment analysis, we'll understand how consumers feel about a competitors product. That'll help us make decisions on how to promote our own product, and what feature we can focus on the most. Using Python, Twitter, and Google Colab, anyone can do this process in just a few minutes. Enjoy! Code for this video: https://github.com/llSourcell/Sentiment_Analysis Please Subscribe! And Like. And comment. Thats what keeps me going. Want more education? Connect with me here: Twitter: https://twitter.com/sirajraval instagram: https://www.instagram.com/sirajraval Facebook: https://www.facebook.com/sirajology Join us at the School of AI: https://theschool.ai/ More learning resources: https://towardsdatascience.com/sentiment-analysis-with-python-part-1-5ce197074184 https://www.geeksforgeeks.org/twitter-sentiment-analysis-using-python/ https://www.datacamp.com/community/tutorials/simplifying-sentiment-analysis-python https://www.kaggle.com/ngyptr/python-nltk-sentiment-analysis https://pythonspot.com/python-sentiment-analysis/ https://www.analyticsvidhya.com/blog/2018/07/hands-on-sentiment-analysis-dataset-python/ Join us in the Wizards Slack channel: http://wizards.herokuapp.com/ Please support me on Patreon: https://www.patreon.com/user?u=3191693 Signup for my newsletter for exciting updates in the field of AI: https://goo.gl/FZzJ5w

Which deep learning framework should you use? In this video I'll compare 10 deep learning frameworks across a wide variety of metrics. PyTorch, Tensorflow, MXNet, Chainer, CNTK, Sonnet, DeepLearning4J, CoreML, ONNX, we've got a lot to cover in this video! Using code, programmatic features, and theory, I'll navigate this field ultimately coming to some clear conclusions. Enjoy! Code for this video: https://github.com/llSourcell/DeepLearningFrameworks Please Subscribe! And Like. And comment. Thats what keeps me going. Want more education? Connect with me here: Twitter: https://twitter.com/sirajraval instagram: https://www.instagram.com/sirajraval Facebook: https://www.facebook.com/sirajology More learning resources: https://www.netguru.com/blog/deep-learning-frameworks-comparison https://towardsdatascience.com/deep-learning-framework-power-scores-2018-23607ddf297a https://www.exastax.com/deep-learning/a-comparison-of-deep-learning-frameworks/ https://skymind.ai/wiki/comparison-frameworks-dl4j-tensorflow-pytorch https://www.marutitech.com/top-8-deep-learning-frameworks/ Join us at the School of AI: https://theschool.ai/ Join us in the Wizards Slack channel: http://wizards.herokuapp.com/ Please support me on Patreon: https://www.patreon.com/user?u=3191693 Signup for my newsletter for exciting updates in the field of AI: https://goo.gl/FZzJ5w

Welcome to AI Humanities! I'm going to review 10 books that I've recently read in this video. I've formed these books into a sequential curriculum that serve as an "AI Humanities" elective class at School of AI. Literature Humanities was one of the few classes at Columbia I enjoyed, and I'm taking the structure that I learned there and evolving it into one dedicated to AI. These books have all helped me immensely in my brainstorming and ideation phases. I hope you find them useful too. Enjoy! Curriculum for this video: https://github.com/llSourcell/AI_Humanities Please Subscribe! And Like. And comment. Thats what keeps me going. Want more education? Connect with me here: Twitter: https://twitter.com/sirajraval instagram: https://www.instagram.com/sirajraval Facebook: https://www.facebook.com/sirajology Pre-requisite book: Hands on Machine Learning : https://amzn.to/2FDEH4K Books: → The Master Algorithm by Pedro Domingos | https://amzn.to/2FDCer0 → Life 3.0 by Max Tegmark | https://amzn.to/2RZH0ER → Superintellgence by Nick Bostrom | https://amzn.to/2CwiscR → Beyond Good and Evil by Fredrich Nietzsche | https://amzn.to/2DpvxGy → 12 Rules for Life by Jordan Peterson | https://amzn.to/2U7gHd9 → 10 Reasons to Delete Your Social Media by Jaron Lanier | https://amzn.to/2MsHzC9 → The Gene by Siddharth Mukerjee | https://amzn.to/2DokWf9 → The Physics of God by Joseph Selbie | https://amzn.to/2CBAsCL → AI Superpowers by Kai-Fu Lee https://amzn.to/2T3C1Ad → The 3 Body Problem by Cixin Lee https://amzn.to/2T0CBie Join us at the School of AI: https://theschool.ai/ Join us in the Wizards Slack channel: http://wizards.herokuapp.com/ Please support me on Patreon: https://www.patreon.com/user?u=3191693 Signup for my newsletter for exciting updates in the field of AI: https://goo.gl/FZzJ5w

Make the data lit! This lyrics of this music video are actually educational and they serve as an introductory lecture on AI. This video also acts as a teaser trailer for my upcoming, free 3 month Data Science course for beginners titled "Data Lit" at School of AI (Jan 28 start date). 2019 is our year to shine Wizards, I hope my song gets you as hype as I am to make a huge impact in the world using the power of AI and data. Enjoy! Please Subscribe! And like. And comment. That's what keeps me going. Sign up for the "Data Lit" course at School of AI: https://www.theschool.ai/courses/data-lit/ Want more education? Connect with me here: Twitter: https://twitter.com/sirajraval Facebook: https://www.facebook.com/sirajology instagram: https://www.instagram.com/sirajraval Lyrics: School of AI 2019! Shoutout to my Wizards, this ones for you It started with hello world hello engineers And now we’re , world-wide yo and this the premiere So u gotta sit down tight and let me teach you a lesson I call it intro to AI, this is my confession Lesson one starts simple gotta get that data Don’t even mess with the thetas until we get that data And if we open the file, it might look like a haze, But if we keep it algorithmic we can set it ablaze Hello! Algebra ops its input times weight Its a Matrix of nums, add a bias, activate And when it learns from the data it’ll tell us things Like how to cure a disease or how to spend our money Or even, generate a game you won’t believe, Its like build train test done, NLP Damn! Bad drivers let me show you my friend Cause you can drive it hands free and still have so much to spend (repeat 2x) Make the Data Lit, Make the Data Lit Make the Data, Make the Data Lit, Make the Data Lit, Learn from it Make the Data Lit, Make the Data Lit, Make the Data, Make the Data Lit, Learn from it From Paris to Mexico, and New York to Seoul We’re called the School of AI and we keep it under control Listen, If you need some support, I got an SVM It can split up the data like its for us and them Models can generate and models can classify Models can allocate, my model is ride or die Numbers keep defining all the rules of the game And all these matrix operations got my feeling insane Yeah, Turn it up, go, accel-le-erate And if we go tryna quantum boost then its 10x the rate You can hyper-learn it all if theres a will a way Just bare in mind ,if you over-fit you’ll break your display I train my bot when Im feeling low, I train my bot till it paints Van Gogh I train my bot to help visualize, I train my bot till it harmonize (repeat 2x) Make the Data Lit, Make the Data Lit Make the Data, Make the Data Lit, Make the Data Lit, Learn from it Make the Data Lit, Make the Data Lit, Make the Data, Make the Data Lit, Learn from it The games called called evolution. We use technology to evolve. Light the way Wizards, it starts with us. --------- Join us in the Wizards Slack channel: http://wizards.herokuapp.com/ And please support me on Patreon: https://www.patreon.com/user?u=3191693 Signup for my newsletter for exciting updates in the field of AI: https://goo.gl/FZzJ5w #DataLit #SchoolOfAI #SirajRaval

DeepMind, an AI lab & complete outsider to the field of molecular biology, beat top pharmaceutical companies with 100K+ employees like Pfizer, Novartis, etc. at predicting protein structures. This is huge! DeepMind didn't yet release the paper or code, so I browsed the CASP reports and different related papers to reverse engineer the architecture as best as I could. What I ended up doing is forking a related research papers code and repurposing it, since its very similar (2 residual networks were used). In this episode, i explain the different components of its architecture, why the protein folding problem is so important, and give some programmatic examples. Enjoy! Code for this video (with coding challenge): https://github.com/llSourcell/DeepFolding Please Subscribe! And Like. And comment. Thats what keeps me going. Want more education? Connect with me here: Twitter: https://twitter.com/sirajraval instagram: https://www.instagram.com/sirajraval Facebook: https://www.facebook.com/sirajology More learning resources: https://deepmind.com/blog/alphafold/ https://moalquraishi.wordpress.com/2018/12/09/alphafold-casp13-what-just-happened/ https://www.reddit.com/r/MachineLearning/comments/a2oaiy/r_alphafold_using_ai_for_scientific_discovery/ https://medium.com/syncedreview/deepmind-alphafold-delivers-unprecedented-progress-on-protein-folding-789fcc1420ea https://news.ycombinator.com/item?id=18587612 Join us at the School of AI: https://theschool.ai/ Join us in the Wizards Slack channel: http://wizards.herokuapp.com/ Please support me on Patreon: https://www.patreon.com/user?u=3191693 Signup for my newsletter for exciting updates in the field of AI: https://goo.gl/FZzJ5w

This won the best paper award at NeurIPS (the biggest AI conference of the year) out of over 4800 other research papers! Neural Ordinary Differential Equations is the official name of the paper and in it the authors introduce a new type of neural network. This new network doesn't have any layers! Its framed as a differential equation, which allows us to use differential equation solvers on it to approximate the underlying function of time series data. Its very cool and will ultimately allow us to learn from irregular time series datasets more efficiently, which applies to many different industries. I'll cover all the prerequisites in this video and point to helpful resources down below. Enjoy! Code for this video: https://github.com/llSourcell/Neural_Differential_Equations/ Please Subscribe! And Like. And comment. Thats what keeps me going. Want more education? Connect with me here: Twitter: https://twitter.com/sirajraval instagram: https://www.instagram.com/sirajraval Facebook: https://www.facebook.com/sirajology More learning resources: https://www.youtube.com/watch?v=q555kfIFUCM&lc=z23fvvbypqe3jzrc2acdp432igdvd11cmfijivtyfvxw03c010c https://www.youtube.com/watch?v=h3l4qz76JhQ&t=9s https://www.youtube.com/watch?v=WUvTyaaNkzM&list=PLZHQObOWTQDMsr9K-rj53DwVRMYO3t5Yr https://towardsdatascience.com/paper-summary-neural-ordinary-differential-equations-37c4e52df128 https://arxiv.org/abs/1806.07366 https://blog.acolyer.org/2019/01/09/neural-ordinary-differential-equations/ https://rkevingibson.github.io/blog/neural-networks-as-ordinary-differential-equations/ Join us at the School of AI: https://theschool.ai/ Join us in the Wizards Slack channel: http://wizards.herokuapp.com/ Please support me on Patreon: https://www.patreon.com/user?u=3191693 Signup for my newsletter for exciting updates in the field of AI: https://goo.gl/FZzJ5w

Kubernetes is one of the highest velocity open source projects in history. Its a tool that enables developers to manage 'containerized' apps in the cloud easily. In this tutorial video, I'll deploy an image classifier app built in python to the cloud using Kubernetes. It's a 3 step process, and along the way I'll explain key concepts surrounding Docker, Google Cloud, and scalability. Enjoy! Code for this video: https://github.com/llSourcell/kubernetes Please Subscribe! And Like. And comment. Thats what keeps me going. Want more education? Connect with me here: Twitter: https://twitter.com/sirajraval instagram: https://www.instagram.com/sirajraval Facebook: https://www.facebook.com/sirajology More learning resources: https://www.youtube.com/watch?v=FTr3n7uBIuE https://www.youtube.com/watch?v=cAICT4Al5Ow https://www.youtube.com/watch?v=VKoLGnq15RM https://www.youtube.com/watch?v=BlPFE7R8psk https://blog.containership.io/k8svsdocker/ https://medium.com/analytics-vidhya/deploy-your-first-deep-learning-model-on-kubernetes-with-python-keras-flask-and-docker-575dc07d9e76 Join us at the School of AI: https://theschool.ai/ Join us in the Wizards Slack channel: http://wizards.herokuapp.com/ Please support me on Patreon: https://www.patreon.com/user?u=3191693 Signup for my newsletter for exciting updates in the field of AI: https://goo.gl/FZzJ5w

2018 has been an eventful year for AI to say the least! We've seen advances in generative models, the AlphaGo victory, several data breach scandals, and so much more. I'm going to briefly review AI in 2018 before giving 10 predictions on where the space is going in 2019. Prepare yourself, my predictions range from more Kubernetes infused ML pipelines to the first business use case of generative modeling of 3D worlds. Happy New Year and enjoy! Code for this video: https://github.com/llSourcell/kubernetes Please Subscribe! And Like. And comment. Thats what keeps me going. Want more education? Connect with me here: Twitter: https://twitter.com/sirajraval instagram: https://www.instagram.com/sirajraval Facebook: https://www.facebook.com/sirajology More learning resources: https://www.youtube.com/watch?v=vOppzHpvTiQ&list=PL2-dafEMk2A7YdKv4XfKpfbTH5z6rEEj3 https://www.youtube.com/watch?v=T5pRlIbr6gg&list=PL2-dafEMk2A6QKz1mrk1uIGfHkC1zZ6UU https://www.youtube.com/watch?v=xRJCOz3AfYY&list=PL2-dafEMk2A7mu0bSksCGMJEmeddU_H4D https://www.youtube.com/watch?v=2FmcHiLCwTU&list=PL2-dafEMk2A7EEME489DsI468AB0wQsMV https://www.youtube.com/watch?v=i_McNBDP9Qs&list=PL2-dafEMk2A5FZ-MnPMpp3PBtZcINKwLA https://www.youtube.com/watch?v=mrRfpiAwad0&t=327s Join us at the School of AI: https://theschool.ai/ Join us in the Wizards Slack channel: http://wizards.herokuapp.com/ Please support me on Patreon: https://www.patreon.com/user?u=3191693 Signup for my newsletter for exciting updates in the field of AI: https://goo.gl/FZzJ5w

The blockchain space has seen its fair share of issues in 2018. This technology enabled anyone, anywhere to create their own market programmatically. The result was incredible innovation, many high profile scams, and a lot of hard lessons on what doesn't work. I authored a book on this space because I believed in the potential for this technology and still do. I'm going to share my vision for 2019 using the lessons learned from 2018, then demonstrate some businesses who've provided real value to people using this technology. Enjoy! Code for this video: https://github.com/llSourcell/pyquarkchain Please Subscribe! And Like. And comment. Thats what keeps me going. Want more education? Connect with me here: Twitter: https://twitter.com/sirajraval instagram: https://www.instagram.com/sirajraval Facebook: https://www.facebook.com/sirajology More learning resources: https://www.theschool.ai/courses/decentralized-application/ https://medium.com/prysmatic-labs/how-to-scale-ethereum-sharding-explained-ba2e283b7fce https://www.youtube.com/watch?v=gSQXq2_j-mw https://www.youtube.com/watch?v=-_Qs0XdPpw8 https://www.youtube.com/watch?v=GWOdAAFoSFE https://www.youtube.com/watch?v=vVsIHCTGjsE https://www.youtube.com/watch?v=LZEHOlZY2To https://www.youtube.com/watch?v=aPVmd7SyKfQ Join us at the School of AI: https://theschool.ai/ Join us in the Wizards Slack channel: http://wizards.herokuapp.com/ Please support me on Patreon: https://www.patreon.com/user?u=3191693 Signup for my newsletter for exciting updates in the field of AI: https://goo.gl/FZzJ5w

Data Science is becoming more and more popular as a career choice since it offers both lucrative salaries and the opportunity to have high impact. The Data Science interview process is challenging, but with dedicated practice you can succeed. In this video, I'll outline the 7 steps to pass any Data Science Interview. We'll go over topics like studying techniques, portfolio optimization, and interviewing tips, all of which are prominent in the modern Data Science interview pipeline. I've listed all of the resources I've mentioned both in the video description and in the associated GitHub readme. Enjoy! Plan for this video: https://github.com/llSourcell/Data_Science_Interview_Guide Please Subscribe! And Like. And comment. Thats what keeps me going. Want more education? Connect with me here: Twitter: https://twitter.com/sirajraval instagram: https://www.instagram.com/sirajraval Facebook: https://www.facebook.com/sirajology Step 1 - Ask yourself "Why" Investor Insights for Inspiration https://thesyndicate.vc/the-top-5-startup-technology-angel-investing-and-venture-capital-podcasts-in-order/ Investors to follow on Twitter http://www.venturearchetypes.com/faq/Investors-On-Twitter-List.html Data Stories Podcasts http://datastori.es/ Step 2 - Create and Execute a Study Plan Learn Data Science in 3 Months https://www.youtube.com/watch?v=9rDhY1P3YLA Chromebook Data Science https://leanpub.com/universities/set/jhu/chromebook-data-science Open Source University https://github.com/ossu/data-science Practice Technical Interviews http://interviewing.io Use Flashcards https://www.brainscape.com/subjects/data-science Find a Mentor https://www.sharpestminds.com Step 3 - Build a Portfolio Design a resume + personal website https://www.youtube.com/watch?v=nMK94JlKRb4 And have 3 projects on Github, one should have a web presence Step 4 - Start Pitching for Jobs - https://www.angel.co - Ask friends on Social Media - https://www.ventureloop.com/ventureloop/job_search.php (VC Portfolio companies job listings) - https://news.ycombinator.com/ - Find recruiters using "site:linkedin.com quora technical recruiter" but replace quora with your company Scheduling tool https://calendly.com/sirajraval/ Step 5 - Complete the Interview Study Data Science Interview Questions on Glassdoor https://www.glassdoor.com/Interview/data-scientist-interview-questions-SRCH_KO0,14.htm ---------------- Join us at the School of AI: https://theschool.ai/ Join us in the Wizards Slack channel: http://wizards.herokuapp.com/ Please support me on Patreon: https://www.patreon.com/user?u=3191693 Signup for my newsletter for exciting updates in the field of AI: https://goo.gl/FZzJ5w #DataScienceInterview #SirajRaval #MachineLearning

Geometric Deep Learning is able to draw insights from graph data. That includes social networks, sensor networks, the entire Internet, and even 3D Objects (if we consider point cloud data to be a graph). I'll explain how it works via a demo of me using a graph convolutional network to classify people by their interest in sports teams as well as a 3D object classification demo. At its core, it comes down to being able to learn from non-Euclidean data. Euclid's laws help define certain types of data, so I'll cover some geometry background as well. Enjoy! Code for this video: https://github.com/llSourcell/pytorch_geometric Please Subscribe! And Like. And comment. Thats what keeps me going. Want more education? Connect with me here: Twitter: https://twitter.com/sirajraval instagram: https://www.instagram.com/sirajraval Facebook: https://www.facebook.com/sirajology More learning resources: http://sungsoo.github.io/2018/02/01/geometric-deep-learning.html http://geometricdeeplearning.com/ https://arxiv.org/abs/1611.08097 http://3ddl.stanford.edu/CVPR17_Tutorial_Intrinsic_CNNs_compressed.pdf https://github.com/rusty1s/pytorch_geometric https://pemami4911.github.io/paper-summaries/deep-learning-theory/2017/11/19/geometric-deep-learning.html Join us at the School of AI: https://theschool.ai/ Join us in the Wizards Slack channel: http://wizards.herokuapp.com/ Please support me on Patreon: https://www.patreon.com/user?u=3191693 Signup for my newsletter for exciting updates in the field of AI: https://goo.gl/FZzJ5w Hiring? Need a Job? See our job board!: https://www.theschool.ai/jobs/ Need help on a project? See our consulting group: https://www.theschool.ai/consulting-group/

Learning the tools that preserve user privacy is going to become an increasingly important skillset for all aspiring data scientists to learn in the coming months. Legal frameworks like GDPR are being proposed all around the world as people realize how valuable their data is, so data scientists need to accept that they'll have to handle data differently than in the past. In this video, I'll demo 3 important privacy techniques; differential privacy, secure multi party computation, and federated learning. We'll use these techniques to train a mode built with Python l to predict diabetes while keep user data anonymous. Enjoy! Code for this video: https://github.com/OpenMined/PySyft/tree/master/examples/tutorials Please Subscribe! And Like. And comment. Thats what keeps me going. Want more education? Connect with me here: Twitter: https://twitter.com/sirajraval instagram: https://www.instagram.com/sirajraval Facebook: https://www.facebook.com/sirajology More learning resources: https://www.openmined.org/ https://iamtrask.github.io/2017/03/17/safe-ai/ https://mortendahl.github.io/ https://florian.github.io/federated-learning/ https://towardsdatascience.com/whats-new-in-deep-learning-research-understanding-federated-learning-b14e7c3c6f89 http://www.cleverhans.io/privacy/2018/04/29/privacy-and-machine-learning.html Join us at the School of AI: https://theschool.ai/ Join us in the Wizards Slack channel: http://wizards.herokuapp.com/ Please support me on Patreon: https://www.patreon.com/user?u=3191693 Signup for my newsletter for exciting updates in the field of AI: https://goo.gl/FZzJ5w Hiring? Need a Job? See our job board!: https://www.theschool.ai/jobs/ Need help on a project? See our consulting group: https://www.theschool.ai/consulting-group/

We are School of AI! Our mission is to offer a world-class AI education to anyone on Earth for free. Our doors are open to all those who wish to learn. We are a learning community that spans almost every country dedicated to teaching our students how to make a positive impact in the world using AI technology. In this video, I make a request for applications to sign-up to be a School of AI Residential Advisor. Residential Advisors provide quality co-living housing for School of AI Wizards in their respective cities. We’re looking for people who either own, lease, rent, or in some way manage a co-living property with at least 5 beds, but the more the better. The type of property can be a big house, an apartment complex, a hostel, or even an existing college dorm. Signup here: https://docs.google.com/forms/d/e/1FAIpQLSdctYUsnVwrIPZy6NT-zFcwL-nj_kCq2WDPQoA-FdWILExtIQ/viewform Please Subscribe! And like. And comment. That's what keeps me going. Want more education? Connect with me here: Twitter: https://twitter.com/sirajraval instagram: https://www.instagram.com/sirajraval Facebook: https://www.facebook.com/sirajology More Learning Resources: https://www.youtube.com/watch?v=8yu8rtXThy8&t=4s https://www.youtube.com/watch?v=_7TvMA_w8xw Join us in the Wizards Slack channel: http://wizards.herokuapp.com/ Sign up for the next course at School of AI: https://www.theschool.ai James Ingallinera's Linkedin: https://www.linkedin.com/in/james-ingallinera-4172a021/ And please support me on Patreon: https://www.patreon.com/user?u=3191693 Signup for my newsletter for exciting updates in the field of AI: https://goo.gl/FZzJ5w School of AI t-shirt: https://teespring.com/school-of-ai

This video is sponsored by IBM. Watson Discovery is an IBM Cloud service which allows you to unlock hidden value in data to find answers, monitor trends, and surface patterns using the world’s most advanced cloud-native insight engine. Watson Discovery ships with natural language processing built-in and can even be taught to understand terms that are specific to your domain. By automating the ingestion and processing of your data in a fully managed cloud service, it removes the complexity from your workflow and allows you to spend less time wrangling your data and more time building! Ready to get started with Watson Discovery? Sign up for your IBM Cloud Account: https://ibm.biz/ibm-cloud-signup Watson Discovery Documentation: https://ibm.biz/watson-discovery-docs Mining insights from data breaches (TUTORIAL): https://ibm.biz/discovery-insights-tutorial Create a stock information app (TUTORIAL): https://ibm.biz/stock-app-tutorial Code (+ Challenge) for this video: https://github.com/llSourcell/IBM_Watson_Discovery Please Subscribe! And Like. And comment. Thats what keeps me going. Want more education? Connect with me here: Twitter: https://twitter.com/sirajraval instagram: https://www.instagram.com/sirajraval Facebook: https://www.facebook.com/sirajology Join us at the School of AI: https://theschool.ai/ Join us in the Wizards Slack channel: http://wizards.herokuapp.com/ Please support me on Patreon: https://www.patreon.com/user?u=3191693 Signup for my newsletter for exciting updates in the field of AI: https://goo.gl/FZzJ5w Hiring? Need a Job? See our job board!: https://www.theschool.ai/jobs/

A Chinese scientist recently claimed to have created the first genetically modified human embryos. Using a technology called CRISPR, he made twin baby girls resistant to HIV before they were born. This opened up serious debate across the world on the implications of this technology. I'm going to take this opportunity to give an in-depth analysis of biotechnology, and and answer some really hard questions. Can we really program biology, and if so how? What are the implications of designer babies? Should humans live forever? I really put my heart into this video, I hope you find it useful and inspiring. Enjoy! Code for this video: https://github.com/llSourcell/Elevation Please Subscribe! And Like. And comment. Thats what keeps me going. Want more education? Connect with me here: Twitter: https://twitter.com/sirajraval instagram: https://www.instagram.com/sirajraval Facebook: https://www.facebook.com/sirajology More learning resources: https://github.com/topics/synthetic-biology?l=python https://discoverysedge.mayo.edu/2018/07/24/the-gene-editing-tool-crispr-explained/ https://www.vox.com/2018/7/23/17594864/crispr-cas9-gene-editing https://ghr.nlm.nih.gov/primer/genomicresearch/genomeediting https://biopython.org/ https://www.nytimes.com/2018/11/30/world/asia/gene-editing-babies-china.html Join us at the School of AI: https://theschool.ai/ Join us in the Wizards Slack channel: http://wizards.herokuapp.com/ Please support me on Patreon: https://www.patreon.com/user?u=3191693 Signup for my newsletter for exciting updates in the field of AI: https://goo.gl/FZzJ5w Hiring? Need a Job? See our job board!: https://www.theschool.ai/jobs/ Need help on a project? See our consulting group: https://www.theschool.ai/consulting-group/ #CRISPR #SirajRaval

D-Wave’s Leap service is the first cloud based quantum application environment that gives developers real-time access to a live quantum computer. In this video, we’ll use the Leap service to build a simple application to monitor a crop health sensor network. I've been collaborating with D-Wave on this, and this video will go over several existing use cases, detail how quantum computing uses quantum mechanics, and demo using it for a real-world application. Entanglement, superposition, tunneling, there are some crucial concepts for us to understand to be able to harness quantum technology and through animations, code, and explanations I hope you too get excited about its potentials. Enjoy! Code examples: https://www.dwavesys.com/ Coding Challenge: Create a simple app using Leap that solves a problem. Submit your github repository to [email protected] by December 9th at midnight PST. I'll give the top 2 entries a video shoutout a week after that! Please Subscribe! And Like. And comment. Thats what keeps me going. Want more education? Connect with me here: Twitter: https://twitter.com/sirajraval instagram: https://www.instagram.com/sirajraval Facebook: https://www.facebook.com/sirajology More learning resources: https://www.youtube.com/watch?v=yB6tnJdttjw https://www.techrepublic.com/article/d-wave-quantum-computers-the-smart-persons-guide/ https://uwaterloo.ca/institute-for-quantum-computing/quantum-computing-101 https://www.youtube.com/watch?v=LhtnECml-KI&vl=en https://www.youtube.com/watch?v=DmzWsvb-Un4 Join us at the School of AI: https://theschool.ai/ Join us in the Wizards Slack channel: http://wizards.herokuapp.com/ Please support me on Patreon: https://www.patreon.com/user?u=3191693 Signup for my newsletter for exciting updates in the field of AI: https://goo.gl/FZzJ5w Hiring? Need a Job? See our job board!: https://www.theschool.ai/jobs/ Need help on a project? See our consulting group: https://www.theschool.ai/consulting-group/ This video was sponsored by D-Wave

I've laid out the blueprint for a brain computer interface called "The Link". This is a device that uses DNA for processing and storage, temporal interference to alter brain states, and infrared spectroscopy to read brain states. Its powered using your body heat, noninvasive, and waterproof. This device is just a thought experiment, but real companies are working on this technology today. What does this kind of technology entail for privacy? Will there be an intelligence disparity? What role does AI play in all of this? What does it even mean to be human? I tackle these hard questions and give a technical overview of this device in this video. Enjoy! Please Subscribe! And Like. And comment. Thats what keeps me going. Want more education? Connect with me here: Twitter: https://twitter.com/sirajraval instagram: https://www.instagram.com/sirajraval Facebook: https://www.facebook.com/sirajology Code for this video: https://github.com/llSourcell/the_link More learning resources: http://kernel.co https://osfund.co/ https://www.sciencedaily.com/news/mind_brain/brain-computer_interfaces/ https://www.sciencedaily.com/news/mind_brain/brain-computer_interfaces/ https://www.bloomberg.com/news/features/2017-09-07/brain-computer-interfaces-are-already-here https://medicalxpress.com/news/2018-10-brain-computer-interface.html https://medium.com/@devdevcharlie/experimenting-with-brain-computer-interfaces-in-javascript-8d6cb891fda8 Join us at the School of AI: https://theschool.ai/ Join us in the Wizards Slack channel: http://wizards.herokuapp.com/ Please support me on Patreon: https://www.patreon.com/user?u=3191693 Signup for my newsletter for exciting updates in the field of AI: https://goo.gl/FZzJ5w Hiring? Need a Job? See our job board!: https://www.theschool.ai/jobs/ Need help on a project? See our consulting group: https://www.theschool.ai/consulting-group/

The Chinese state run news agency Xinhua recently revealed the first Artificial Intelligence news anchor. They're now able to generate video of a newscaster using a model trained on real newscaster data and use it to disseminate information 24/7. When I saw this, I knew it was time to start studying China's role in the AI revolution in-depth. In this video, I'll cover China's power structure, generative adversarial networks, its startup scene, Confucianism, the social credit scoring system, algorithmic policing, surveillance, privacy, autonomous weapons, and convolutional neural networks. A lot of different topics to cover, but I hope this video provides a coherent narrative around the use of AI in China and how it plays into the global AI community. Enjoy! Code for this video: https://github.com/llSourcell/Talking-Face-Generation-DAVS Please Subscribe! And Like. And comment. Thats what keeps me going. Want more education? Connect with me here: Twitter: https://twitter.com/sirajraval instagram: https://www.instagram.com/sirajraval Facebook: https://www.facebook.com/sirajology More learning resources: https://www.npr.org/2018/10/31/662436265/china-tests-a-social-credit-score https://www.theverge.com/2018/11/8/18074806/ai-news-anchor-china-xinhua-digital-composite https://skymind.ai/wiki/generative-adversarial-network-gan https://towardsdatascience.com/build-your-own-convolution-neural-network-in-5-mins-4217c2cf964f https://machinelearningmastery.com/multi-class-classification-tutorial-keras-deep-learning-library/ https://www.youtube.com/watch?v=PhCM3qoRZHE https://www.youtube.com/watch?v=yz6dNf7X7SA Join us at the School of AI: https://theschool.ai/ Join us in the Wizards Slack channel: http://wizards.herokuapp.com/ Please support me on Patreon: https://www.patreon.com/user?u=3191693 Signup for my newsletter for exciting updates in the field of AI: https://goo.gl/FZzJ5w Hiring? Need a Job? See our job board!: https://www.theschool.ai/jobs/ Need help on a project? See our consulting group: https://www.theschool.ai/consulting-group/

A team of researchers at Google Brain and Georgia Tech developed an AI that learned how to dress itself using various types of clothing. They demonstrated their technology by presenting a video that shows an animated figure gracefully putting on clothing, and the most interesting part is that it learned how to do this by itself. The technique they used was called Trust Region Policy Optimization and its one of the techniques at the forefront of AI research. I'll admit that this was one of the most difficult papers I've ever read, if not the most difficult. The Differentiable Neural Computer paper would be a close 2nd. Using code, animations, theory, and memes, I'll explain how this algorithm works and demonstrate how we can use it to improve our lives in the areas of gaming, elder-care, and factory automation. Code for this video: https://github.com/llSourcell/AI_Dresses_Itself Please Subscribe! And Like. And comment. Thats what keeps me going. Want more education? Connect with me here: Twitter: https://twitter.com/sirajraval Facebook: https://www.facebook.com/sirajology instagram: https://www.instagram.com/sirajraval More learning resources: https://www.cc.gatech.edu/~aclegg3/projects/learning-dress-synthesizing.pdf https://medium.com/@jonathan_hui/rl-trust-region-policy-optimization-trpo-explained-a6ee04eeeee9 http://178.79.149.207/posts/trpo.html https://www.depthfirstlearning.com/2018/TRPO https://towardsdatascience.com/introduction-to-various-reinforcement-learning-algorithms-part-ii-trpo-ppo-87f2c5919bb9 Join us at School of AI: https://theschool.ai/ Join us in the Wizards Slack channel: http://wizards.herokuapp.com/ Please support me on Patreon: https://www.patreon.com/user?u=3191693 Signup for my newsletter for exciting updates in the field of AI: https://goo.gl/FZzJ5w Hiring? Need a Job? See our job board!: https://www.theschool.ai/jobs/ Need help on a project? See our consulting group: https://www.theschool.ai/consulting-group/

Time series is the fastest growing category of data out there! It's a series of data points indexed in time order. Often, a time series is a sequence taken at successive equally spaced points in time. In this video, I'll cover 8 different time series techniques that will help us predict the price of gold over a period of 3 years. We'll compare the results of each technique, and even consider using a learning technique. From Holts Winter Method to Vector Auto Regression to Reinforcement Learning, we've got a lot to cover here. Enjoy! Code for this video: https://github.com/llSourcell/Time_Series_Prediction Please Subscribe! And Like. And comment. Thats what keeps me going. Want more education? Connect with me here: Twitter: https://twitter.com/sirajraval Facebook: https://www.facebook.com/sirajology instagram: https://www.instagram.com/sirajraval More learning resources: https://www.altumintelligence.com/articles/a/Time-Series-Prediction-Using-LSTM-Deep-Neural-Networks https://blog.statsbot.co/time-series-prediction-using-recurrent-neural-networks-lstms-807fa6ca7f https://towardsdatascience.com/bitcoin-price-prediction-using-time-series-forecasting-9f468f7174d3 https://www.datascience.com/blog/time-series-forecasting-machine-learning-differences https://www.analyticsvidhya.com/blog/2018/02/time-series-forecasting-methods/ https://www.youtube.com/watch?v=hhJIztWR_vo Join us at School of AI: https://theschool.ai/ Join us in the Wizards Slack channel: http://wizards.herokuapp.com/ Please support me on Patreon: https://www.patreon.com/user?u=3191693 Signup for my newsletter for exciting updates in the field of AI: https://goo.gl/FZzJ5w

Why was AlphaGo's Move 37 against Lee Sedol so significant? Why was it so important that I named my 10 week course on deep reinforcement learning on it? In this final video of my course, I'll explain what move 37 symbolized for humanity and detail 3 examples of how it will affect healthcare, design, and decision-making. We'll go through a code example of a Generative Adversarial Network and even discuss China ambitious 2030 AI initiative. Theres a lot that I cover in this video, I hope that it helps connect the dots. Enjoy! Code for this video: https://github.com/tensorflow/minigo Want more education? Connect with me here: Twitter: https://twitter.com/sirajraval Facebook: https://www.facebook.com/sirajology instagram: https://www.instagram.com/sirajraval This video is apart of Move 37, my course at School of AI: https://theschool.ai/ More learning resources: https://katbailey.github.io/post/was-alphagos-move-37-inevitable/ https://medium.com/@cristobal_esteban/move-37-a3b500aa75c2 https://disruptionhub.com/disrupted-humanity-move-37/ https://www.youtube.com/watch?v=UzYeqAJ2bA8 https://www.youtube.com/watch?v=hY9Bc3mtphs&t=640s Join us in the Wizards Slack channel: http://wizards.herokuapp.com/ Please support me on Patreon: https://www.patreon.com/user?u=3191693 Signup for my newsletter for exciting updates in the field of AI: https://goo.gl/FZzJ5w

Drones are being used in real world applications around the world! They can help detect anomalies in crop yields, provide companies with physical assets a real-time continuous data stream, and help secure locations by giving teams an aerial view. We'll learn how a dron can learn to navigate a novel, complex environment using an advanced reinforcement learning technique called Deep Deterministic Policy Gradients. Its a mixture of policy gradients and actor critic, I'll go through the dependencies before explaining the real algorithm. Enjoy! Code for this video: https://github.com/llSourcell/Drone_Flight_Controller Want more education? Connect with me here: Twitter: https://twitter.com/sirajraval Facebook: https://www.facebook.com/sirajology instagram: https://www.instagram.com/sirajraval This video is apart of Move 37, my course at School of AI: https://theschool.ai/ Drone 3D Environment: https://rds.theconstructsim.com/signup/ More learning resources: https://pemami4911.github.io/blog/2016/08/21/ddpg-rl.html https://towardsdatascience.com/introduction-to-various-reinforcement-learning-algorithms-i-q-learning-sarsa-dqn-ddpg-72a5e0cb6287 https://coach.nervanasys.com/algorithms/policy_optimization/ddpg/index.html https://towardsdatascience.com/policy-gradients-in-a-nutshell-8b72f9743c5d https://deepmind.com/research/publications/deterministic-policy-gradient-algorithms/ https://medium.com/@kinwo/solving-continuous-control-environment-using-deep-deterministic-policy-gradient-ddpg-agent-5e94f82f366d https://medium.freecodecamp.org/an-introduction-to-policy-gradients-with-cartpole-and-doom-495b5ef2207f Join us in the Wizards Slack channel: http://wizards.herokuapp.com/ Please support me on Patreon: https://www.patreon.com/user?u=3191693 Signup for my newsletter for exciting updates in the field of AI: https://goo.gl/FZzJ5w

Machine Learning is an amazing technology, but how are you supposed to earn a living from it? In this video, I'll break down 7 ways that anyone can earn money from anywhere in the world using machine learning. We''ll start by taking a look at whats called the "AI Value Chain" to learn who is currently making money in machine learning so that we can better chart out where we can contribute to the space. From startups, to competitions, to writing books, we've got a lot to cover in this video. Enjoy! Please Subscribe! And like. And comment. That's what keeps me going. Want more education? Connect with me here: Twitter: https://twitter.com/sirajraval Facebook: https://www.facebook.com/sirajology instagram: https://www.instagram.com/sirajraval More learning resources: https://www.youtube.com/watch?v=9bbS-trc8ys https://www.youtube.com/watch?v=x6GYD5WPda4 https://www.youtube.com/watch?v=CGTn0ceOaOM https://www.youtube.com/watch?v=7vunJlqLZok https://www.youtube.com/watch?v=SSu00IRRraY https://www.youtube.com/watch?v=HhqhFbwiaig https://www.kaggle.com/competitions Join us at School of AI: https://theschool.ai/ The 7 Ways are 1. Start a Startup 2. Contract Work 3. Job or Internship 4. Write a Book 5. Educational Content 6. Automated Trading Bot 7. Competitions Join us in the Wizards Slack channel: http://wizards.herokuapp.com/ And please support me on Patreon: https://www.patreon.com/user?u=3191693 Signup for my newsletter for exciting updates in the field of AI: https://goo.gl/FZzJ5w

I've created a 3 month curriculum to help you go from absolute beginner to proficient in the art of data science! This open source curriculum consists of purely free resources that I’ve compiled from across the Web and has no prerequisites, you don’t even have to have coded before. I’ve designed it for anyone who wants to improve their skills and find paid work ASAP, ether through a full-time position or contract work. You’ll be learning a host of tools like SQL, Python, Hadoop, and even data storytelling, all of which make up the complete data science pipeline. Curriculum for this video: https://github.com/llSourcell/Learn_Data_Science_in_3_Months Please Subscribe! And like. And comment. That's what keeps me going. Want more education? Connect with me here: Twitter: https://twitter.com/sirajraval Facebook: https://www.facebook.com/sirajology instagram: https://www.instagram.com/sirajraval Join us in the Wizards Slack channel: http://wizards.herokuapp.com/ And please support me on Patreon: https://www.patreon.com/user?u=3191693 Week 1 - Learn Python - EdX https://www.edx.org/course/introduction-python-data-science-2 - Siraj Raval https://www.youtube.com/watch?v=T5pRlIbr6gg&list=PL2-dafEMk2A6QKz1mrk1uIGfHkC1zZ6UU Week 2 - Statistics & Probability - KhanAcademy https://www.khanacademy.org/math/statistics-probability Week 3 - Data Pre-processing, Data Vis, Exploratory Data Analysis - EdX https://www.edx.org/course/introduction-to-computing-for-data-analysis Week 4 - Kaggle Project #1 Week 5-6 - Algorithms & Machine Learning - Columbia https://courses.edx.org/courses/course-v1:ColumbiaX+DS102X+2T2018/course/ Week 7 - Deep Learning - Part 1 and 2 of DL Book https://www.deeplearningbook.org/ - Siraj Raval https://www.youtube.com/watch?v=vOppzHpvTiQ&list=PL2-dafEMk2A7YdKv4XfKpfbTH5z6rEEj3 Week 8 - Kaggle Project #2 Week 9 - Databases (SQL + NoSQL) - Udacity https://www.udacity.com/course/intro-to-relational-databases--ud197 - EdX https://www.edx.org/course/introduction-to-nosql-data-solutions-2 Week 10 - Hadoop & Map Reduce + Spark - Udacity https://www.udacity.com/course/intro-to-hadoop-and-mapreduce--ud617 - Spark Workshop https://stanford.edu/~rezab/sparkclass/slides/itas_workshop.pdf Week 11 - Data Storytelling - Edx https://www.edx.org/course/analytics-storytelling-impact-1 Week 12- Kaggle Project #3 Signup for my newsletter for exciting updates in the field of AI: https://goo.gl/FZzJ5w

Curiosity is something that all humans exhibit in some way throughout their lives. Recently, a team at Berkeley published a paper on Curiosity driven learning, and they demonstrated how it helped enable their AI agent to learn how to play the popular game Super Mario Brother very efficiently with the added benefit of curiosity to help Mario explore his options. I'll explain how it works in this video using code, animations, math, and the spoken word. This technology can be used to help make our systems more intelligent, and thus our applications more capable of helping other people. Enjoy! Code for this video: https://github.com/llSourcell/noreward-rl Please Subscribe! And like. And comment. That's what keeps me going. Want more education? Connect with me here: Twitter: https://twitter.com/sirajraval Facebook: https://www.facebook.com/sirajology instagram: https://www.instagram.com/sirajraval This video is apart of the Move 37 course at School of AI: https://www.theschool.ai More learning resources: https://pathak22.github.io/large-scale-curiosity/ https://pathak22.github.io/large-scale-curiosity/resources/largeScaleCuriosity2018.pdf https://alumni.berkeley.edu/california-magazine/winter-2017-power/super-curious-mario-teaching-ai-keep-asking-questions https://www.technologyreview.com/s/607886/curiosity-may-be-vital-for-truly-smart-ai/ https://www.youtube.com/watch?v=0Ey02HT_1Ho Join us in the Wizards Slack channel: http://wizards.herokuapp.com/ And please support me on Patreon: https://www.patreon.com/user?u=3191693 Signup for my newsletter for exciting updates in the field of AI: https://goo.gl/FZzJ5w #ArtificialCuriosity #SirajRaval #AI

Boston Dynamics released yet another incredible video of its bipedal humanoid robot, this time performing parkour by jumping on a series of boxes. In this video, i'll explain how it works at both a hardware and software level. Their real value lies in the specific type of software they are using, we don't know for sure what it is but we can take some educated guesses based on a combination of whats been revealed so far and what's worked in other humanoid robots. Prepare yourselves for some mechanical engineering and control theory Wizards, enjoy! Code for this video: https://github.com/llSourcell/Boston_Dynamics_Atlas_Explained Please Subscribe! And like. And comment. That's what keeps me going. Want more education? Connect with me here: Twitter: https://twitter.com/sirajraval Facebook: https://www.facebook.com/sirajology instagram: https://www.instagram.com/sirajraval More learning resources: https://www.bostondynamics.com/atlas https://www.cs.cmu.edu/~cga/drc/door-submitted.pdf http://gazebosim.org/tutorials?tut=drcsim_atlas_robot_interface&branch=issue_24_atlas_robot_interface_drcsim_4 http://www.ent.mrt.ac.lk/~rohan/teaching/EN5001/Reading/DORFCH1.pdf http://engineering.nyu.edu/mechatronics/smart/Archive/intro_to_rob/Intro2Robotics.pdf https://www.youtube.com/watch?v=LikxFZZO2sk Join us in the Wizards Slack channel: http://wizards.herokuapp.com/ School of AI: https://www.theschool.ai And please support me on Patreon: https://www.patreon.com/user?u=3191693 Signup for my newsletter for exciting updates in the field of AI: https://goo.gl/FZzJ5w

Meta learning describes the concept of 'learning to learn'. What if we could have AI learn how to optimize itself? An AI could learn the optimal hyper-parameters, architecture, and even dataset! Its a really interesting topic, and in this video I'll describe some meta learning techniques and focus on one in particular; deep neuro-evolution. We'll build an image classifier using a deep neuro-evolutionary algorithm. Enjoy! Code for this video: https://github.com/harvitronix/neural-network-genetic-algorithm Please Subscribe! And like. And comment. That's what keeps me going. Want more education? Connect with me here: Twitter: https://twitter.com/sirajraval Facebook: https://www.facebook.com/sirajology instagram: https://www.instagram.com/sirajraval More learning resources: http://nn.cs.utexas.edu/?neuroevolution-tutorial-ijcnn2013 https://blog.coast.ai/lets-evolve-a-neural-network-with-a-genetic-algorithm-code-included-8809bece164 https://towardsdatascience.com/paper-repro-deep-neuroevolution-756871e00a66 https://www.youtube.com/watch?v=lu5ul7z4icQ Join us in the Wizards Slack channel: http://wizards.herokuapp.com/ School of AI: https://www.theschool.ai And please support me on Patreon: https://www.patreon.com/user?u=3191693 Signup for my newsletter for exciting updates in the field of AI: https://goo.gl/FZzJ5w

A blockchain is a data structure that was first introduced by Satoshi Nakamoto in the Bitcoin protocol white paper almost a decade ago. Bitcoin's blockchain stores transaction data, but we can store any type of data in a blockchain. Ethereum, for example, enables users to store code snippets called 'smart contracts' in their blockchain. In this video, we'll build a simple blockchain in Python that uses proof-of-work consensus, just like the Bitcoin protocol does. There's a lot of misunderstanding around what the blockchain is and what it can do so I hope this demonstrates how simple it really is. Enjoy! Code for this video: https://github.com/llSourcell/Simple_Blockchain_in_5_Minutes Please Subscribe! And like. And comment. That's what keeps me going. Want more education? Connect with me here: Twitter: https://twitter.com/sirajraval Facebook: https://www.facebook.com/sirajology instagram: https://www.instagram.com/sirajraval How does this apply to AI? Watch this: https://www.youtube.com/watch?v=5Tr13l0O1Ws More learning resources: https://anders.com/blockchain/blockchain.html https://www.youtube.com/watch?v=wVVGv2bmxow&vl=en https://www.youtube.com/watch?v=LZEHOlZY2To https://www.youtube.com/watch?v=gSQXq2_j-mw https://github.com/llSourcell/Learn_Blockchain_in_2_months Join us in the Wizards Slack channel: http://wizards.herokuapp.com/ School of AI: https://www.theschool.ai And please support me on Patreon: https://www.patreon.com/user?u=3191693 Signup for my newsletter for exciting updates in the field of AI: https://goo.gl/FZzJ5w

Facebook is the worlds largest social network with over 2 billion monthly users at the time of this video. It's flagship product, the 'news feed', uses an AI algorithm to personalize the content that each user sees. This AI optimizes for a users attention, as thats what drives its ad revenue based business model. Its an incredibly powerful AI that knows users in ways no human could, synthesizing hundreds of thousands of data points from their browsing activity and web history. In the past few years, we've seen that this AI has had both positive and negative effects on society. I'm going to highlight the negative effects and present 3 possible solutions to fix the problem. Code for this video: https://github.com/llSourcell/Stream-Framework Please Subscribe! And like. And comment. That's what keeps me going. Want more education? Connect with me here: Twitter: https://twitter.com/sirajraval Facebook: https://www.facebook.com/sirajology instagram: https://www.instagram.com/sirajraval More learning resources: https://www.amazon.com/dp/B079DTVVG8/ref=dp-kindle-redirect?_encoding=UTF8&btkr=1 https://www.youtube.com/watch?v=VyQVlBQCX_Y https://www.youtube.com/watch?v=gSQXq2_j-mw https://bitlab.cas.msu.edu/papers/news_feed_fyi_chi17_lbw.pdf https://medium.com/recombee-blog/recommender-systems-explained-d98e8221f468 https://hbr.org/2018/09/a-blueprint-for-a-better-digital-society http://humanetech.com/problem#the-way-forward https://peepeth.com https://mastodon.social Join us in the Wizards Slack channel: http://wizards.herokuapp.com/ School of AI: https://www.theschool.ai And please support me on Patreon: https://www.patreon.com/user?u=3191693 Signup for my newsletter for exciting updates in the field of AI: https://goo.gl/FZzJ5w

Intel released a toolkit that allows developers to build applications that emulate human vision across multiple platforms. The OpenVINO toolkit is based on convolutional neural networks (CNN) and it extends workloads across Intel hardware (including accelerators) and maximizes performance. In this video, I'll explain how OpenVINO, or Open Visual Inference and Neural Network Optimization, enables us to use convolutional neural networks for computer vision on the edge. This video was sponsored by Intel. Enjoy! Code for this video: https://software.intel.com/en-us/openvino-toolkit/documentation/code-samples Please Subscribe! And like. And comment. That's what keeps me going. Want more education? Connect with me here: Twitter: https://twitter.com/sirajraval Facebook: https://www.facebook.com/sirajology instagram: https://www.instagram.com/sirajraval The School of AI: https://www.theschool.ai More learning resources: https://software.intel.com/en-us/openvino-toolkit https://venturebeat.com/2018/05/16/intel-launches-openvino-computer-vision-toolkit-for-edge-computing/ https://www.youtube.com/watch?v=M6Nyh2JDLQs https://software.intel.com/en-us/openvino-toolkit/documentation/get-started https://software.intel.com/en-us/openvino-toolkit/deep-learning-cv Join us in the Wizards Slack channel: http://wizards.herokuapp.com/ And please support me on Patreon: https://www.patreon.com/user?u=3191693 Signup for my newsletter for exciting updates in the field of AI: https://goo.gl/FZzJ5w #ComputerVision #SirajRaval #Intel

We can use deep reinforcement learning to optimize a SQL database, and in this video we'll optimize the ordering of a series of SQL queries such that it involves the minimum possible memory/computation footprint. Deep RL involves using a neural network to approximate reinforcement learning functions, like the Q (quality) function. After we frame our database as a Markov Decision Process, I'll use Python to build a Deep Q Network to optimize SQL queries. Enjoy! Code for this video: https://github.com/llSourcell/SQL_Database_Optimization Please Subscribe! And like. And comment. That's what keeps me going. Want more education? Connect with me here: Twitter: https://twitter.com/sirajraval Facebook: https://www.facebook.com/sirajology instagram: https://www.instagram.com/sirajraval More learning resources: https://rise.cs.berkeley.edu/blog/sql-query-optimization-meets-deep-reinforcement-learning/ https://mldb.ai/ https://docs.microsoft.com/en-us/sql/advanced-analytics/what-is-sql-server-machine-learning?view=sql-server-2017 https://towardsdatascience.com/machine-learning-in-your-database-the-case-for-and-against-bigquery-ml-4f2309282fda https://www.quora.com/Which-database-is-best-for-machine-learning Join us in the Wizards Slack channel: http://wizards.herokuapp.com/ School of AI: https://www.theschool.ai And please support me on Patreon: https://www.patreon.com/user?u=3191693 Signup for my newsletter for exciting updates in the field of AI: https://goo.gl/FZzJ5w