PODCAST · education
AI Bites: The Academic Series
by Jack Lakkapragada
Welcome to AI Bites. This podcast features AI-generated deep dives into the world’s most prestigious computer science curricula.Based on personal study notes and publicly available course material from Stanford University (CS124, CS221, and more), these episodes use Google’s NotebookLM to transform dense academic topics into conversational summaries. Perfect for learning on the go, whether you're commuting or at the gym.Disclaimer: This is an independent, AI-generated study resource and is not officially affiliated with Stanford University.
Episodes
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VIDEO SHORT | MIT 6.036: Why AI Uses Random Noise to Learn Faster
Why do the world's most powerful AI models rely on random noise to find optimal solutions? Because pure mathematical certainty gets trapped in local valleys! Discover why Stochastic Gradient Descent takes instant steps on single random data points—and how this "noisy bouncing" helps AI escape…
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VIDEO | MIT 6.036: Visualizing Gradient Descent & SGD Loss Landscapes
Watch the geometry of optimization come to life in Week 5 of MIT 6.036. We visually animate parameter trajectories rolling down 3D loss surfaces, demonstrate how overshooting step sizes cause wild oscillations, compare convex bowls against non-convex local traps, and visually map out the…
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Title: EP 61 | MIT 6.036: Gradient Descent & Optimization in Practice
Welcome to Week 5 of MIT 6.036! Now that we've framed machine learning as loss minimization, how do we actually find the parameters that sit at the bottom of the bowl? We explore the geometry of loss landscapes, analyze step-size dynamics, derive analytical gradients for Logistic Regression, and...
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VIDEO SHORT | MIT 6.036: Why Step Functions Break AI Optimization
Why do modern AI models output smooth probabilities instead of plain yes-or-no decisions? Because step functions break calculus! Discover why $0\text{--}1$ loss results in zero gradients everywhere, and how the Sigmoid function allows gradient-based algorithms to learn smoothly in under 60…
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VIDEO | MIT 6.036: Visualizing Logistic Regression & Loss Landscapes
Watch the mathematics of optimization come alive in Week 4 of MIT 6.036. We graphically demonstrate why traditional $0\text{--}1$ step-loss fails calculus due to flat zero gradients, animate the smooth continuous Sigmoid curve $\sigma(z) = \frac{1}{1 + e^{-z}}$, and visually step through the...
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EP 60 | MIT 6.036: Logistic Regression & ML as Optimization
Welcome to Week 4 of MIT 6.036! This week we transition from heuristic rules to the formal framework of Machine Learning as Optimization. We explore why traditional step-function loss fails calculus, introduce smooth Sigmoid probabilities, and derive the Negative Log-Likelihood loss function. Key...
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VIDEO SHORT | MIT 6.036: Feature Representation
How does AI draw a straight line through data that isn't straight? It cheats by adding another dimension. Learn the foundational feature transformation trick that powers modern machine learning in under 60 seconds. Key Topics: The limitations of linear classifiers. The $\phi(x) = [x, x^2]$…
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VIDEO | MIT 6.036: Feature Representation
Watch the math come to life in Week 3 of MIT 6.036. When a straight line fails to separate our data, we don't change the line—we change the space. We visually animate the math behind feature transformations, showing how flat 1D data bends into 2D parabolas, and break down the matrix mathematics…
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EP 59 | MIT 6.036: Feature Representation
Welcome to Week 3 of MIT 6.036! What happens when a linear classifier encounters a problem that can't be solved with a straight line? You change the shape of the universe. This week, we explore the "High-Dimensional Transformation Trick," tackle the infamous XOR problem, and learn why feature...
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VIDEO SHORT | MIT 6.036: The Perceptron Algorithm
It sounds like a 1960s robot, but the Perceptron is actually the grandfather of modern neural networks. Learn how this elegant, error-correcting algorithm guarantees a perfect decision boundary in under 60 seconds.Key Topics: Frank Rosenblatt and the 1960s Perceptron. The genius of error-driven...
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VIDEO | MIT 6.036: The Perceptron Algorithm
Watch the math come to life in Week 2 of MIT 6.036. We take the Perceptron algorithm out of the textbook and put it onto a 2D coordinate plane. See exactly how a decision boundary shifts, rotates, and corrects itself every time it makes a mistake, culminating in a visual breakdown of the famous...
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EP 58 | MIT 6.036: The Perceptron Algorithm
Welcome to Week 2 of MIT 6.036! This week, we graduate from random guessing to one of the most historically significant learning algorithms in computer science: The Perceptron. We explore its biological roots in the 1940s, break down the brilliant simplicity of its geometric update rule, and walk...
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VIDEO SHORT | MIT 6.036: Is Your AI Learning or Memorizing?
If a machine memorizes all the training data but fails on new inputs, did it actually learn anything? Discover the "Homework vs. Exam" analogy that defines the most critical engineering challenge in all of machine learning: generalization over memorization. Key Topics: Training Error vs. Test...
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VIDEO | MIT 6.036: Visualizing Linear Classifiers & ML Foundations
See the math come to life in our first visual breakdown of MIT 6.036. We illustrate the fundamental architecture of machine learning models before dropping into a 2D coordinate plane to manually draw out a separating hyperplane, proving exactly how linear algorithms decide what is positive and…
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EP 57 | MIT 6.036: Foundations of ML & Linear Classifiers
Welcome to MIT 6.036! We kick off our machine learning journey by tackling a deep philosophical paradox: how can we reliably predict the future using only data from the past? Join us as we dissect the "problem of induction," explore the six core characteristics of ML problem classes, and jump…
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VIDEO | CS224N: The Complete Course in 20 Minutes
The ultimate high-yield visual recap of Stanford’s CS224N (Natural Language Processing with Deep Learning)! In just 20 minutes, we cover the full 15-module arc of the course—from the birth of word vectors to modern reasoning models and the smart scaling era. Key Topics Covered: Word Embeddings &...
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SHORT | CS224N: Open areas in NLP
The grand finale of CS224N! In this final NotebookLM Video Short, we take a visual look at the "David vs. Goliath" revolution happening in AI reasoning. Key Topics: Smart Scaling vs. Brute-Force: Why the era of throwing $100M+ at massive pre-training runs is hitting a data wall. ProRL & Entropy...
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EP 56 | CS224N: Open Frontiers in NLP & The Smart Scaling Era
Welcome to the grand finale of CS224N! In our final episode, featuring insights from Professor Yejin Choi, we tackle the biggest open frontier in AI: what happens when high-quality web data runs out? We explore why the era of brute-force scaling is officially over, and how small language models...
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SHORT VIDEO | CS224N: Multimodality
A bite-sized, visual breakdown of CS224N's guest lecture on Multimodal Deep Learning! In this NotebookLM Video Short, we look at how models fake visual intelligence, and the craziest new frontier in AI. Key Topics: Evaluation Cheats: A visual look at how models cheat on visual tests (like the...
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EP 55 | CS224N: Multimodality
What happens when an AI can see, hear, and even smell? In this episode, featuring insights from former Meta FAIR and Hugging Face researcher Douwe Kiela, we break out of the text-only box. We explore the impending "data ceiling" of the internet, how neural networks mathematically fuse different...
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SHORT VIDEO | CS224N: Social Impacts of NLP
A bite-sized, visual breakdown of CS224N Lecture 16! In this NotebookLM Video Short, we pull back the curtain on why models are mathematically forced to lie, and how AI is subtly homogenizing human thought. Key Topics: The Good-Turing Proof: A visual explanation of the missing mass math ($p_0$)...
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SHORT VIDEO | CS224N: Interpretability
A bite-sized, visual breakdown of CS224N's guest lecture with Dr. Been Kim! In this NotebookLM Video Short, we look at the mathematical failure of our current interpretability tools and how researchers are extracting alien concepts from AI. Key Topics: The Saliency Illusion: A visual look at the...
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EP 54 | CS224N: Social and broader impacts of NLP
We are stepping away from optimizer tricks to tackle the downstream social and cognitive impacts of language models. Featuring Professor Yejin Choi's lecture, we explore the mathematical inevitability of AI hallucinations, how AI is quietly homogenizing human creativity, and the Constitutional AI...
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EP 53 | CS224N: Model Interpretability & Editing
How do we communicate with an AI that thinks in alien, superhuman concepts? In this episode, featuring insights from Google Brain’s Dr. Been Kim, we explore the massive gap between what we think machines know and what they actually know. We expose the fatal flaws in our current interpretability...
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Video Short: Tokenization & Multilinguality
A bite-sized, visual breakdown of CS224N Lecture 14! In this new NotebookLM Video Short, we pull back the curtain on the invisible preprocessing layer of modern AI: Tokenization. Key Topics: The "Strawberry" Problem: A visual look at why ChatGPT can't count letters or spell backwards due to…
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EP 52 | CS224N: Tokenization & Multilinguality
Language models do not actually read text—they read tokens. In this episode, we explore the invisible preprocessing layer that Andrej Karpathy says is "at the heart of much weirdness of LLMs." We demystify the Tokenization problem, explain why your AI can't count letters, and discuss the massive...
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EP 51 | CS224N: AI Reasoning (Part 2)
Today, we are pushing the absolute limits of how Language Models generate text. We move beyond basic architecture to explore how engineers are making AI insanely fast, teaching models to recover from their own mistakes, expanding context windows so AI can read entire books, and proving that a…
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EP 50 | CS224N: Reasoning Part 1
How does a language model actually "think"? In this episode, we dive into the fascinating mechanics of AI reasoning. We move past basic text prediction to explore how modern models generate complex, multi-step logic, self-correct their own mistakes, and fundamentally change how we scale compute....
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EP 49 | CS224N: Benchmarking and Evaluation
We spend so much time building massive AI models, but how do we actually know if they are any good? In this episode, we tackle the multi-billion-dollar scientific bottleneck: evaluation. We explore why the science of measuring models is lagging far behind the engineering of building them, and why...
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EP 48 | CS224N: RAG and Language Agents
Up until now, we’ve looked at Language Models as isolated brains trapped in a box. In this episode, we cross the threshold into the absolute bleeding edge of AI: giving models a search engine to browse the web, memory to remember past conversations, and tools to execute code. We break down the...
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EP 47 | CS224N: Efficient Adaptation
We know how to build and align massive foundational models, but what if you don't have a $100 million supercomputer? In this episode, we tackle the practical wall of modern AI: compute costs. We explore how researchers are circumventing astronomical expenses to adapt massive models efficiently,...
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EP 46 | CS224N: Post-training
How do we turn a raw, chaotic text-predictor into a helpful, conversational AI assistant? In this episode, we dive into the massive pipeline of Post-training. We explore the transition from Instruction Fine-Tuning to complex Reinforcement Learning, and why teaching an AI to be "helpful" sometimes...
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EP 45 | CS224N: Pre-training
If the Transformer architecture gave us the engine for modern AI, this episode is all about the fuel. We are diving into the single most consequential paradigm shift in modern NLP: Pre-training. We explore how we train these massive models, the distinct architectures we use, and the surprising...
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EP 44 | CS224N: Transformers
Last week, we saw how RNNs struggled with the "Bottleneck Problem" and sequential processing. This week, we explore the architecture that solved it and changed natural language processing forever: the Transformer. We break down how dropping recurrence in favor of pure attention mechanisms allowed...
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EP 43 | CS224N: Language Models and RNNs
We are continuing our journey through Stanford's CS224N by exploring the absolute foundation of modern natural language processing. In this episode, we break down Language Models and Recurrent Neural Networks (RNNs), unpacking how the simple task of predicting the next word ultimately taught...
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EP 42 | CS224N: Backpropagation and Neural Networks
We are looking under the hood of deep learning to understand the mathematical engine driving modern artificial intelligence: Backpropagation. In this episode, we break down how neural networks transition away from rigid linear boundaries to build complex, non-linear understandings of language.…
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EP 41 | CS224N: Word Vectors
How do you teach a computer the actual meaning of a word? In this episode, we dive into the fundamental building block of modern NLP: Word Vectors. We break down how algorithms map words into a dimensional space, allowing machines to mathematically understand context, similarity, and semantic...
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EP 40 | CS224N: History of NLP
Welcome to a brand new series! We are diving into Stanford's CS224N. To understand where AI is today, we first need to understand how we got here. In this episode, we trace the evolution of Natural Language Processing from early rigid experiments to the deep learning revolution that powers modern...
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EP 39 | CME295 in 15 Minutes (The Full Recap)
Short on time? We’ve distilled the entire Stanford CME295 course into a single, high-energy video recap. This "Cram Session" takes you on a complete journey from the absolute basics of natural language processing to the cutting edge of Large Language Models. Watch or listen for the "Best Of" our...
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EP 38 | CME295: Recap & Future Trends
We have reached the end of Stanford's CME295! In this course finale, we zoom out to summarize the entire journey—from the underlying Transformer architecture to the massive engineering feat of training and tuning LLMs. Then, we look ahead to the absolute cutting edge of AI research. Key Topics…
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EP 37 | CME295: LLM Evaluations
If an AI can write a poem, code a website, and pass the bar exam, how do we actually measure its performance? This episode tackles the notoriously difficult science of LLM Evaluation. We look at why standard testing benchmarks are breaking down and how researchers are trying to keep up. Key…
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EP 36 | CME295: Agentic LLMs
What happens when an AI stops just answering questions and starts taking action? In this episode, we explore Agentic LLMs. We break down how language models are evolving from passive text-generators into autonomous agents capable of planning, using tools, and interacting with the digital world.…
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EP 35 | CME295: LLM Reasoning
Can a language model actually think, or is it just a sophisticated autocomplete? In this episode, we dive into the fascinating—and highly debated—topic of LLM reasoning. We explore how to unlock complex problem-solving capabilities without changing the underlying model. Key Topics: In-Context...
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EP 34 | CME295: LLM Tuning
A raw, pre-trained base model isn't very useful—it just wants to endlessly complete sentences. This episode covers the crucial second step: Tuning. We look at how developers take a chaotic text-generator and mold it into a helpful, safe, and conversational assistant. Key Topics: Supervised...
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EP 33 | CME295: LLM Training
Building a massive language model from scratch requires an astronomical amount of data and compute. In this episode, we explore the "pre-training" phase. We break down the sheer scale of the engineering required to teach a model the fundamental statistical rules of human language. Key Topics: The...
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EP 32 | CME295: Large Language Models (LLMs)
What happens when you take the Transformer architecture and scale it up with massive amounts of compute and data? You get Large Language Models (LLMs). We wrap up Week 1 by connecting the base architecture to the modern AI tools you use every day. Key Topics: Scaling Up: How adding more…
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EP 31 | CME295: Transformer Architecture
Now that we know why Transformers work, it’s time to look under the hood. In this episode, we strip the architecture down to its studs, exploring the specific structural components that allow these models to understand complex language. Key Topics: Encoders and Decoders: The difference between...
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EP 30 | CME295: Introduction to Transformers
We are kicking off our brand new CME295 series by going back to the breakthrough that started the current AI era. If you want a clear, foundational understanding of what a Transformer actually is and why it replaced older AI models, this is your starting point. Key Topics: The Pre-Transformer…
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EP 29 | CS25 in 10 Minutes (The Full Recap)
Short on time? We’ve distilled the entire Stanford CS25: Transformers United course into a single 10-minute video. This "Cram Session" covers the journey of the Transformer architecture from a text-processing breakthrough to the engine driving the future of biology, visual media, and reasoning....
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EP 28 | CS25: Transformers for Video Generation
Generating a single image is hard; generating a coherent video means mastering the dimension of time. In our CS25 finale, we break down how Transformers are being engineered to understand motion, physics, and object permanence to generate high-fidelity video content frame-by-frame. Key Topics…
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ABOUT THIS SHOW
Welcome to AI Bites. This podcast features AI-generated deep dives into the world’s most prestigious computer science curricula.Based on personal study notes and publicly available course material from Stanford University (CS124, CS221, and more), these episodes use Google’s NotebookLM to transform dense academic topics into conversational summaries. Perfect for learning on the go, whether you're commuting or at the gym.Disclaimer: This is an independent, AI-generated study resource and is not officially affiliated with Stanford University.
HOSTED BY
Jack Lakkapragada
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How many episodes does AI Bites: The Academic Series have?
AI Bites: The Academic Series currently has 79 episodes available on PodParley. New episodes are automatically indexed when they're published to the podcast feed.
What is AI Bites: The Academic Series about?
Welcome to AI Bites. This podcast features AI-generated deep dives into the world’s most prestigious computer science curricula.Based on personal study notes and publicly available course material from Stanford University (CS124, CS221, and more), these episodes use Google’s NotebookLM to transform...
How often does AI Bites: The Academic Series release new episodes?
AI Bites: The Academic Series has 79 episodes. Check the episode list to see recent publication dates and frequency.
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You can listen to AI Bites: The Academic Series on PodParley by clicking any episode. We provide an embedded audio player for direct listening, and you can also subscribe via your preferred podcast app using the RSS feed.
Who hosts AI Bites: The Academic Series?
AI Bites: The Academic Series is created and hosted by Jack Lakkapragada.
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