08:02

t-SNE - Explained

08:04

The Illusion of Thinking - Paper Walkthrough

05:18

Perception Encoder - Paper Walkthrough

05:15

SAM2: Segment Anything in Images and Videos - Paper Walkthrough

05:05

MMaDA: Multimodal Large Diffusion Language Models - Paper Walkthrough

05:12

Anthropic Claude 4 - System Card Walkthrough

04:52

Google's AlphaEvolve - Paper Walkthrough

10:28

Hidden Markov Models (HMM) Part 2 - The Viterbi Algorithm

05:53

Hidden Markov Models (HMM) Part 1 - Introduction

05:44

An Introduction to Graph Neural Networks

09:33

Gaussian Processes

08:15

Bayesian Optimization

04:22

The RBF Kernel

09:03

The Kernel Trick

08:07

The Curse of Dimensionality

04:27

Cross-Entropy - Explained

04:13

Weights Initialization in Neural Networks

03:59

Dropout Regularization - Explained

03:41

Recommender Systems - Part 3: Issues & Solutions

04:11

Recommender Systems - Part 2: Collaborative Filtering

03:38

Recommender Systems - Part 1: Content-Based Recommendations

02:37

Why L1 Regularization Produces Sparse Weights

04:11

Overfitting vs Underfitting - Explained

04:24

Confidence Intervals Explained

03:45

Z-Test Explained

04:04

L1 vs L2 Regularization

05:36

Poisson Distribution - Explained

08:01

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

09:12

T-Test Explained

03:58

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

03:59

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

04:28

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

03:33

Covariance Matrix - Explained

08:10

The Bitter Lesson in AI...

05:40

Marginal, Joint and Conditional Probabilities Explained

04:49

Least Squares vs Maximum Likelihood

03:50

AI Reading List (by Ilya Sutskever) - Part 5

04:27

AI Reading List (by Ilya Sutskever) - Part 4

04:48

AI Reading List (by Ilya Sutskever) - Part 3

05:02

AI Reading List (by Ilya Sutskever) - Part 2

04:31

AI Reading List (by Ilya Sutskever) - Part 1

08:03

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

05:40

Singular Value Decomposition (SVD) Explained

03:27

ROUGE Score Explained

05:48

BLEU Score Explained

03:38

Cross-Validation Explained

03:51

Sliding Window Attention (Longformer) Explained

03:36

BART Explained: Denoising Sequence-to-Sequence Pre-training

20:28

RLHF: Training Language Models to Follow Instructions with Human Feedback - Paper Explained

27:43

Chain-of-Verification (COVE) Reduces Hallucination in Large Language Models - Paper Explained

13:59

The Era of 1-bit LLMs: All Large Language Models are in 1.58 Bits - Paper Explained

05:14

LLM Tokenizers Explained: BPE Encoding, WordPiece and SentencePiece

03:15

Hyperparameters Tuning: Grid Search vs Random Search

26:16

Jailbroken: How Does LLM Safety Training Fail? - Paper Explained

02:44

Word Error Rate (WER) Explained - Measuring the performance of speech recognition systems

03:15

Spearman Correlation Explained in 3 Minutes

03:40

Two Towers vs Siamese Networks vs Triplet Loss - Compute Comparable Embeddings

08:11

LLM Prompt Engineering with Random Sampling: Temperature, Top-k, Top-p

03:21

Kullback-Leibler (KL) Divergence Mathematics Explained

04:36

Covariance and Correlation Explained