AI Bites: The Academic Series
Jack Lakkapragada 79 episodes Oct 23, 2026
AI Bites: The Academic Series offers AI-generated deep dives into some of the world’s most prestigious computer science curricula. The episodes draw on personal study notes and publicly available course material from Stanford University courses such as CS124 and CS221. Using Google’s NotebookLM, the show turns dense academic topics into conversational summaries designed for learning on the go. It is an independent, AI-generated study resource and is not officially affiliated with Stanford University.
Episodes
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 local traps and generalize better in under 60 seconds.Key Topics:Batch Gradient Descent vs.
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 bouncing, noisy trajectory of Stochastic Gradient Descent vs. full Batch GD.Key Topics:Visualizing 3D loss land
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 examine why Stochastic Gradient Descent (SGD) uses random noise to outpace traditional Batch GD.Key
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 seconds.Key Topics:The mathematical flaw of $0\text{--}1$ step loss.Smooth gradients via the Sigmoid curve $\
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 derivation of Negative Log-Likelihood (NLL) loss.Key Topics:Visualizing $0\text{--}1$ loss step functions v
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 Topics:Framing ML as Loss Minimization plus $L_2$ Regularization.Why $0\text{--}1$ Loss fails (zero gra
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 $\phi(x) = [x, x^2]$ parabola trick.Why data representation matters more than the algorithm.Disclaimer: Note: This is an AI-gener
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 of encoding discrete categories like text and blood types into numeric vectors.Key Topics:Animating
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 engineering—like transforming blood types into factored vectors—is the true secret weapon of
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 learning.The linear separability guarantee.Disclaimer: Note: This is an AI-generated discussion created us
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 Convergence Proof.Key Topics:Animating the mathematical update rule.Visualizing the geometric "tug-
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 step-by-step through the elegant mathematical proof that guarantees it will find a perfect boundary.Ke
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 Error.The danger of model overfitting.Why generalization is the holy grail of artificial intellige
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 what is negative.Key Topics:Visual maps of Supervised, Unsupervised, and Reinforcement Learning pipelines
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 into the elegant 2D geometry of Linear Classifiers to understand how algorithms draw boundaries.
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 & Recurrent Networks: From distributional semantics and Word2Vec to backpropagation, RNNs, and sequence
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 $100M+ at massive pre-training runs is hitting a data wall.ProRL & Entropy Control: A visual breakdown of how prolonged reinforcement learning lets a 1.5B parameter mod
EP 56 | CS224N: Open Frontiers in NLP & The Smart Scaling Era
Welcome to the grand finale of CS224N! In our final episode, featuring insights from Professor Yejin Choi, we tackle the biggest open frontier in AI: what happens when high-quality web data runs out? We explore why the era of brute-force scaling is officially over, and how small language models (1.5B–7B parameters) are using "smart scaling" to out-reason 100B+ giants.Key Topics:The End o
SHORT VIDEO | CS224N: Multimodality
A bite-sized, visual breakdown of CS224N's guest lecture on Multimodal Deep Learning! In this NotebookLM Video Short, we look at how models fake visual intelligence, and the craziest new frontier in AI.Key Topics:Evaluation Cheats: A visual look at how models cheat on visual tests (like the "pizza slice" flaw), and why state-of-the-art models completely fail basic compositional logic on the Winogr
EP 55 | CS224N: Multimodality
What happens when an AI can see, hear, and even smell? In this episode, featuring insights from former Meta FAIR and Hugging Face researcher Douwe Kiela, we break out of the text-only box. We explore the impending "data ceiling" of the internet, how neural networks mathematically fuse different senses together, and the mind-bending frontier of olfactory embeddings.Key Topics:The Multimod
SHORT VIDEO | CS224N: Social Impacts of NLP
A bite-sized, visual breakdown of CS224N Lecture 16! In this NotebookLM Video Short, we pull back the curtain on why models are mathematically forced to lie, and how AI is subtly homogenizing human thought.Key Topics:The Good-Turing Proof: A visual explanation of the missing mass math ($p_0$) that physically forces perfectly calibrated models to output plausible falsehoods.The Creativity Paradox:
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 True Positive vs. False Positive graph, proving why popular AI explanation tools (like SHAP) are no b
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 frameworks being built to keep these systems aligned.Key Topics:The Hallucination Math: Why scalin
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 tools and look at how researchers are extracting brand-new strategies from AI to teach the World Chess C
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 opaque token chunks.The Multilingual Tax: A direct comparison showing how English-biased tokeniz
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 socio-economic inequalities baked into modern AI pricing.Key Topics:The BPE Algorithm: Ho
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 small model can beat an industry giant just by "thinking" longer.Key Topics:Speculative Decoding
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.Key Topics:Decoding the Text: Why generation isn't magic, it's an algorithm. We contra
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 hitting 100% on a test doesn't mean what you think it means.Key Topics:The Benchmark SAGA: How the
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 inner workings of Retrieval-Augmented Generation (RAG) and the anatomy of truly autonomous Language Agent
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, pushing the boundaries of what you can train on a single consumer GPU while making AI an environment
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 inadvertently teaches it to lie.Key Topics:The Alignment Problem: Why a raw foundational mod
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 emergent behaviors that happen when we scale them up.Key Topics:The Context Problem & Subwords: Why s
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 models to scale massively, process data in parallel, and understand context like never befor
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 machines to learn facts, logic, and arithmetic.Key Topics:Language Modeling & n-grams: The core con
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.Key Topics:Evaluating Word Vectors: The core trade-offs between Intrinsic subtask testing (like word analo
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 relationships.Key Topics:Moving Past One-Hot Encodings: Why simply assigning a random 1 or 0 to a word fai
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 language models.Key Topics:The Early Days: The struggles of symbolic, rule-based systems and manua
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 course deep dives:The Foundation: Moving past RNNs into the Self-Attention revolutio
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:The Course Recap: A quick refresher on Architecture, Pre-training, Fine-tuning, and Agentic capabiliti
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 Topics:The Benchmark Problem: Why traditional multiple-choice tests are saturating and failing to capture tru
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.Key Topics:Tool Use: How models learn to trigger external APIs, run code, or browse the web to gather inf
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 Learning: How models can learn to do new tasks on the fly just from the examples provided in your prompt.Cha
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 Fine-Tuning (SFT): Teaching the model how to follow specific instructions and format its answers correctly
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 Data Diet: How trillions of words are scraped, cleaned, and curated to form the foundation of
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 parameters and training data fundamentally changes the capability of a Transformer.Pre-training vs. Fine-tuning: T
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 "reading" the input data and "writing" the output prediction.Self-Attention & Multi
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 Era: Why older sequence models (like RNNs and LSTMs) bottlenecked when trying to process long paragraphs of
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.Watch or listen for the "Best Of" our course deep dives:The Architecture: How Attenti
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:The Time Dimension: How "Temporal Attention" allows a model to track where an object is moving ac
EP 27 | CS25: World Models for Medicine
Can an AI learn the "physics" of the human body? In this episode, we dive into the concept of World Models in medicine. We explore how Transformers are moving beyond simply analyzing static medical records to actually simulating and predicting disease progression and treatment outcomes over time.Key Topics:Defining World Models: What it means for an AI to build an internal representation
EP 26 | CS25: Transformers in Diffusion Models
Transformers aren't just for text anymore. This episode unpacks the massive shift in visual AI: merging the power of Transformers with Diffusion models. We break down how the architecture behind text generation is now the engine driving state-of-the-art image and video creation.Key Topics:The Evolution of Visual AI: Moving away from traditional U-Nets and fully embracing Diffusion Transformers
EP 25 | CS25: The Biology of LLMs
What happens when you treat the building blocks of life as a language? In this episode, we explore how the exact same Transformer architecture used for ChatGPT is being applied to biology. DNA, RNA, and proteins are essentially biological sequences, and AI is learning to "read" them to revolutionize medicine and research.Key Topics:Nature's Language: How amino acids and DNA sequences
EP 24 | CS25: Artificial General Intelligence (AGI)
We wrap up Week 1 by zooming out to the endgame of AI research: Artificial General Intelligence (AGI). What separates our current generative tools from true, human-level reasoning? We discuss the theoretical hurdles, scaling laws, and what the leap from "narrow AI" to AGI might actually look like.Key Topics:Defining AGI: What it means for a system to match or exceed human cognitive capab
EP 23 | CS25: Reinforcement Learning (RL)
Description:Having covered the base architecture, we now look at how these models learn to behave. This episode explores Reinforcement Learning (RL) within the context of modern foundation models, focusing on how AI transitions from simply predicting text to making optimized decisions.Key Topics:RL Fundamentals: Agents, environments, and reward functions.Beyond Next-Word Prediction: How models are
EP 22 | CS25: Overview of Transformers
We are kicking off a brand new course with the architecture that changed everything: CS25. In this episode, we break down the fundamental mechanics of Transformers. If you've ever wondered how modern large language models actually process information, this is where it starts.Key Topics:The Attention Mechanism: How models learn to weigh the importance of different words in a sequence.Moving Pas
EP 21 | Duke ML for PMs in 10 Minutes (The Full Recap)
Short on time? We’ve distilled the entire Duke University "Machine Learning Foundations for Product Managers" course into a single 10-minute recap. This is the ultimate PM "Cram Session" for bridging the gap between business strategy and data science.Watch or listen for the "Best Of" our course deep dives:The Core Vocabulary: Features, labels, and the 3 types of ML.Th
EP 20 | Duke ML for PMs: Tree Models & Ensembles
What happens when your data doesn't fit neatly into a straight line? We move to Tree Models. This episode explores how algorithms can mimic human decision-making through a series of "If/Then" splits, and how combining them creates incredibly powerful predictive engines.Key Topics:Decision Trees: Understanding roots, nodes, and leaves to visualize exactly how a model reaches its concl
EP 19 | Duke ML for PMs: Linear Models
Sometimes the best solution is the simplest one. In this episode, we unpack Linear Models—the most interpretable and transparent tools in a Product Manager's AI toolkit. We break down how the math works in plain English so you can explain your model's decisions to any stakeholder.Key Topics:Linear vs. Logistic Regression: The difference between predicting a continuous number (like price) a
EP 18 | Duke ML for PMs: Model Evaluation & Business Metrics
A model can have 99% accuracy and still fail your users. In this episode, we tackle Model Evaluation from the Product Manager's perspective. We bridge the gap between technical model metrics (what engineers care about) and product/business metrics (what stakeholders care about).Key Topics:The Accuracy Trap: Why "Accuracy" is often a misleading metric, especially with imbalanced datas
EP 17 | Duke ML for PMs: The Modeling Process
Building a model is about more than just data—it’s about a repeatable process. This episode walks through the lifecycle of a machine learning project, focusing on the strategic decisions a Product Manager must navigate to ensure a model is production-ready.Key Topics:The 5-Step Process: From problem definition and data collection to model evaluation.Feature & Algorithm Selection: How PMs influ
EP 16 | Duke ML for PMs: Machine Learning Fundamentals
We kick off a new course from Duke University designed specifically for those leading AI products. In this episode, we strip away the code and focus on the core vocabulary and intuition every Product Manager needs to collaborate effectively with data scientists.Key Topics:Defining ML for Business: What machine learning actually is and—more importantly—what it is not.The PM’s Vocabulary: Breaking d
EP 15 | CS21SI: AI for Social Good in 10 Minutes (The Full Recap)
Short on time? We’ve distilled the entire Stanford CS21SI: AI for Social Good course into a single 10-minute video. This "Cram Session" covers the journey from ethical frameworks to technical execution across four major domains of social impact.Watch to see the "Best Of" our course deep dives:Education: The Minerva High School case study and the pitfalls of "Magic Box"
EP 14 | CS21SI Finale: Reinforcement Learning & The Future of Conservation
Description:We’ve reached the finish line for Stanford’s CS21SI! In our final episode, we move beyond passive observation and into the world of sequential decision-making. We explore how Reinforcement Learning (RL) allows AI to learn through trial and error—and why that’s a game-changer for protecting our planet's most endangered species.Key Topics:From Perception to Action: Why "Social G
EP 13 | CS21SI: Computer Vision & Human Agency
We’re exploring how machines "see" and how that vision can be used to restore human agency. In this installment of Stanford’s CS21SI, we move from the technical mechanics of Convolutional Neural Networks (CNNs) to life-changing applications in healthcare, accessibility, and conservation.Key Topics:The Social Model of Disability: Reframing disability not as a medical failure, but as a lim
Title: EP 12 | CS21SI: NLP, Transformers & Automated Disinformation
Description:How does AI decide what is "true"? This week, we dive into Natural Language Processing (NLP). We trace the evolution from RNNs to Transformers and discuss the massive threat Large Language Models (LLMs) pose to our information ecosystems and democracy.Key Topics:The Disinformation Threat: How GPT-class models can generate coherent, grammatical, and completely fictional "
Title: EP 11 | CS21SI: Deep Learning for social good
We’re moving from basic ML into the technical engine of modern AI: Deep Learning. In this week of Stanford’s CS21SI, we explore how neural networks can model complex, non-linear realities—specifically applying them to the escalating global wildfire crisis.Key Topics:The Neural Analogy: Understanding neurons, forward/backward passes, and backpropagation through the lens of social impact.Wildfire Ca
EP 10 | CS21SI Week 1: ML Basics & The Ethics of AI
We’re kicking off a brand new series! This week we dive into CS21SI, a course that bridges the gap between technical AI development and its real-world impact. We start at the beginning: how machines learn and the ethical guardrails we need to build alongside them.Key Topics:Intro to Machine Learning: A high-level look at how models learn from data patterns.AI Ethics: Moving beyond the code to disc
CS124 in 7 minutes
A quick and easy way to understand CS124 in a quick video byte. Generated by Google NotebookLM using my personal class notes.
CS221 in 7 minutes
A quick video bite on CS221 and what the whole course is about - generated by Google NotebookLM from my class notes.
EP 09 | CS221 Week 6: Bayesian Learning, Logic & Final Wrap-up
We’ve reached the finish line! 🏁 In our final installment of the Stanford CS221 series, we tackle the last pieces of the AI puzzle: moving from probabilistic reasoning to actual learning, and revisiting the power of symbolic logic.Key Topics:Deep Dive into Bayesian Learning: How we move beyond just using networks to actually learning their parameters and structures from data.Logic Problems: A retu
EP 08 | CS221 Week 5: Markov Networks, Bayesian Networks & Inference
We are moving from logic to probability! This week in Stanford’s CS221, we transition from the rigid rules of Constraint Satisfaction Problems (CSPs) into the flexible world of Probabilistic Graphical Models. We explore how AI handles uncertainty and models complex relationships between variables.Key Topics:Transition to Markov Networks: Moving beyond constraints to undirected graphical models tha
EP 07 | CS221 Week 4: Games & Constraint Satisfaction Problems
How does AI play to win? This week in Stanford’s CS221, we shift gears from simple pathfinding to navigating adversarial environments and solving complex logic puzzles with strict boundaries.Key Topics:Games: Exploring strategic decision-making, game trees (Minimax/Expectimax), and how AI predicts an opponent’s next move in competitive scenarios.Introduction to CSPs: An entry into Constraint Satis
EP 06 | CS221 Week 3: Structured Perceptrons, A* Search & Markov Models
We’re leveling up the complexity this week in Stanford’s CS221. We move from basic search into "smart" search and start exploring how AI handles sequences and uncertainty through Markov models.Key Topics:• Structured Perceptron: Moving beyond simple classification to predicting complex, structured outputs (like tags in a sentence or parts of an image).• A Search:* The "gold standard
EP 05 | CS221 Week 2: Optimization & The Search for Solutions
We’re diving deeper into the engine room of Artificial Intelligence this week. We continue our journey through Stanford’s CS221, moving from basic ML foundations into the math of optimization and the logic of search algorithms.Key Topics:• Machine Learning 2 & 3: A deep dive into loss functions, gradient descent, and how we actually "train" a model to improve.• Search Problems 1 &
EP 04 | CS221 Week 1: AI History, Ethics & ML Foundations
New course alert! We are moving from NLP into Stanford’s flagship AI course: CS221 (Principles of AI). We kick things off by looking at the "big picture"—where AI came from, the ethics of what we’re building, and the start of our Machine Learning journey.Key Topics:• Course Introduction: Defining the "Principles" of Artificial Intelligence.• AI History: Tracing the path from ea
EP 03 | CS124 Week 3: Speech, Recommendations & Networks
In our third installment, we look at how NLP and AI interact with the broader world, from human speech recognition to the algorithms that decide what shows up in your social feed.Key Topics:• Speech Recognition: Converting audio to data.• Recommendation Systems: The AI behind "You might also like..."• Social Networks & Web Links: How PageRank organizes the internet.Note: This is an A
EP 02 | CS124 Week 2: Embeddings, LLMs & Transformers
Things get "deep" this week. We trace the evolution of how AI understands the meaning of words—moving from simple vectors to the revolutionary Transformer architecture that powers today’s LLMs.Key Topics:• Vector & Contextual Embeddings: Mathematical meaning.• Neural Networks: Biological inspiration.• LLMs & Transformers: The breakthrough that changed everything.Note: This is an
EP 01 | CS124 Week 1: Words, N-grams & Information Retrieval
We kick off our deep dive into Stanford’s CS124 with the building blocks of NLP. This episode covers how computers "read" and organize language before moving into the math behind search.Key Topics:• Words & Tokens: How we break down language.• N-gram Language Modeling: Predicting sequences.• Logistic Regression: The classification workhorse.• Information Retrieval: How search engines
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