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

Sliding Window Attention (Longformer) Explained

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

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

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

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

LLM Tokenizers Explained: BPE Encoding, WordPiece and SentencePiece

Hyperparameters Tuning: Grid Search vs Random Search

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

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

Spearman Correlation Explained in 3 Minutes

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

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

Kullback-Leibler (KL) Divergence Mathematics Explained

Covariance and Correlation Explained

Eigendecomposition Explained

Multi-Head Attention (MHA), Multi-Query Attention (MQA), Grouped Query Attention (GQA) Explained

Kabsch-Umeyama Algorithm - How to Align Point Patterns

How to Fine-tune Large Language Models Like ChatGPT with Low-Rank Adaptation (LoRA)

Discrete Fourier Transform (DFT and IDFT) Explained in Python

XGBoost Explained in Under 3 Minutes

Why Batch Normalization (batchnorm) Works

Object Detection Part 8: Grounding DINO, Open-Set Object Detection

Gaussian Mixture Models (GMM) Explained

Fourier Transform Formula Explained