Linear Regression

Monte Carlo Simulation - Explained

Lagrange Multipliers - Explained

Variational Autoencoder - Explained

Central Limit Theorem - Explained

Degrees of Freedom - Explained

Qwen3 - Paper Walkthrough

Gamma Function - Explained

Variational Inference - Explained

Forward-Backward Algorithm | Hidden Markov Models Part 3

Magistral - Paper Walkthrough

Student's t-Distribution - Explained

Why Larger Language Models Do In-context Learning Differently? - Paper Walkthrough

DeepSeek-R1 - Paper Walkthrough

t-SNE - Explained

The Illusion of Thinking - Paper Walkthrough

Perception Encoder - Paper Walkthrough

SAM2: Segment Anything in Images and Videos - Paper Walkthrough

MMaDA: Multimodal Large Diffusion Language Models - Paper Walkthrough

Anthropic Claude 4 - System Card Walkthrough

Google's AlphaEvolve - Paper Walkthrough

The Viterbi Algorithm | Hidden Markov Models Part 2

Introduction to HMMs | Hidden Markov Models Part 1

An Introduction to Graph Neural Networks

Gaussian Processes

Bayesian Optimization

RBF Kernel Explained: Mapping Data to Infinite Dimensions

The Kernel Trick

The Curse of Dimensionality

Cross-Entropy - Explained

Why Neural Networks Need Random Weight Initialization

Dropout in Neural Networks - Explained

Real-World Challenges in Recommender Systems - Recommender Systems Part 3

Collaborative Filtering - Recommender Systems Part 2

Content-Based Recommendations - Recommender Systems Part 1

Why L1 Regularization Produces Sparse Weights (Geometric Intuition)

Overfitting vs Underfitting - Explained

Confidence Intervals Explained

Z-Test Explained

L1 vs L2 Regularization

Poisson Distribution - Explained

Basic Probability Distributions Explained: Bernoulli, Binomial, Categorical, Multinomial

T-Test Explained

AI Weekly Brief - Week 2: Llama 3.2, OpenAI Voice Mode, Mira Murati leaves OpenAI

AI Weekly Brief - Week 2: LlamaCoder, Eureka, YouTube GenAI, Pixtral 12B

AI Weekly Brief - Week 1: OpenAI o1-preview, DataGemma, AlphaProteo

Covariance Matrix - Explained

The Bitter Lesson in AI...

Marginal, Joint and Conditional Probabilities Explained

Least Squares vs Maximum Likelihood

AI Reading List (by Ilya Sutskever) - Part 5

AI Reading List (by Ilya Sutskever) - Part 4

AI Reading List (by Ilya Sutskever) - Part 3

AI Reading List (by Ilya Sutskever) - Part 2

AI Reading List (by Ilya Sutskever) - Part 1

Vector Database Search - Hierarchical Navigable Small Worlds (HNSW) Explained

Singular Value Decomposition (SVD) Explained

ROUGE Score Explained

BLEU Score Explained

Cross-Validation Explained