HeyJaredAI Bites: The Academic Series
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…
- Indexed episodes, last 90 days
- 12
- Latest publication
- Aug 30, 2026
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- Earliest in this view
- Jul 10, 2026
Latest episodes
VIDEO SHORT | MIT 6.036: Is Your AI Learning or Memorizing? (opens the original)
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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 intelligence. Disclaimer: Note: This is an AI-generated discussion created using Google's NotebookLM, Gemini, and o
VIDEO | MIT 6.036: Visualizing Linear Classifiers & ML Foundations (opens the original)
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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. Plotting feature vectors and the spatial geometry of decision boundaries. Step-by-step matrix mu
EP 57 | MIT 6.036: Foundations of ML & Linear Classifiers (opens the original)
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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. Key Topics: The core differences between Machine Learning, Statistics, and Social Sciences. The 6 Characteris
VIDEO | CS224N: The Complete Course in 20 Minutes (opens the original)
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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-to-sequence bottlenecks. The Transformer & Pretraining Revolution: Self-attention mechanics, BPE toke
SHORT | CS224N: Open areas in NLP (opens the original)
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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 $100M+ at massive pre-training runs is hitting a data wall. ProRL & Entropy Control: A visual breakdown of how prolonged reinforcement learning lets a 1.5B parameter model out-reason models 4.5$\times$ its size. The Future of Intelligence: Why AI capability isn't about how big yo
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