PODCAST · education
Machine Learning Teeta
by Aly Abdurrazek
We get lost in the details. Always find ourselves not knowing where to begin or where we are heading... Lets take a bird's view on things. So we would have a better idea on how to get to where we want.This podcast is a bird's eye view on all things machine learning. Explained to people in a hurry. ( You and me :)
Episodes
-
GPT 4O from openai
We comment on the latest announcement by openai for their latest gpt4o model. What are the model capabilities and what are the strategy behind developing such a model and what does it mean to all of us?
-
Be indifferent
Non-technical episode. Becoming indifferent to the fruits of your work or to the praise you might get from people around you is key to success. Let's see how.
-
Llama 3, context window and LSTMs
We discuss three main things. First the release of llama 3. Model by meta. Second, we answer the question as to why LLMs with longer context windows are not always better. Then finally, we explain when LSTMs should be used over Transformers. Enjoy!
-
Managing machine learning projects
Three points that you need to attend to, to become a good manager and to be able to make 'good' decisions that people follow. Listen to this episode to know what they are.
-
What is up with AI
Open AI k, GTC, and the AI revolution. And what can we do about it?
-
Artificial intelligence Value stream
AI value is created via a sequence of activities, from chip making, all through building and training foundation models, and building tools, to the application development. In this episode, we discuss these stages and what it means to the industry.
-
Introduction to RAG systems (Retrieval Augmented Generation)
What are RAG systems ? Why do they matter? What are the LLM problems that are solved by RAG systems? What are the tools to create a RAG system and what does a typical system look like? Find the answers in today's episode.
-
Bias and variance
Simply, what are the bias and variance? Why is there a tradeoff between them? Listen to this episode to learn about this fundamental topic which is usually asked about during data science and machine learning job interviews.
-
Design Of Experiments in less than 30 minutes
What is design of experiments? Student t-test? ANOVA? These are really simple to explain. Listen to this episode to learn about them.
-
Hardware for deep learning, sequel
What hardware do I need to run ML deep learning models? For prototyping.
-
Hardware for deep learning
How is the computationally intensive deep learning accomplished on modern hardware computers ? A basic introduction.
-
Topic modeling (2); evaluation metrics
How can we compare one topic model to another? How can we evaluate any one of them?
-
Non linear classifiers; decision trees intro
Now is time to discuss non linear classifiers, which provide flexibility in modeling more complex patterns in data. We start by a conceptual intro to decision trees in this short episode
-
Machine learning - Bayes decision theory
The bird's eye view on classifiers. In this episode, we walk thru the landscape and start with linear classifiers. In the next episode, we will go thru non-linear classifiers isA.
-
Data analysis process
We describe the 5 steps of data analysis. Speak of common steps and the pitfalls.
-
Topic modeling part 1
Topic modelling part 1, LSA
-
NLP systems.
How do you build a NLP systems? What are the system level parameters you should consider ? How to choose one algorithm over another? And how to evaluate NLP systems. This episode is about answering these questions.
We're indexing this podcast's transcripts for the first time — this can take a minute or two. We'll show results as soon as they're ready.
No matches for "" in this podcast's transcripts.
No topics indexed yet for this podcast.
Loading reviews...
ABOUT THIS SHOW
We get lost in the details. Always find ourselves not knowing where to begin or where we are heading... Lets take a bird's view on things. So we would have a better idea on how to get to where we want.This podcast is a bird's eye view on all things machine learning. Explained to people in a hurry. ( You and me :)
HOSTED BY
Aly Abdurrazek
Loading similar podcasts...