DataMListic

Covariance Explained #statistics #datascience #machinelearning #maths #mathematics

Byte-Pair Encoding (BPE) Tokenizer

Two Towers Architecture

Sliding Window Attention (Longformer) Explained

RCNN Explained

AI Reading List (by Ilya Sutskever) - Part 3

LLM Tokenizers Explained: BPE Encoding, WordPiece and SentencePiece

Hyperparameters Tuning: Grid Search vs Random Search

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

Why The Reset Gate is Necessary in GRUs

ROUGE Score Explained

AI Reading List -Item 5

AI Reading List - Item 1

Hyperparameters search - grid search vs random search

AI Reading List - Part 4

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

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

Attention Mechanism Explained

Fast RCNN Explained

Faster RCNN Explained

AdamW Optimizer Explained | L2 Regularization vs Weight Decay

The Bitter Lesson (in AI)...

XGBoost Explained in Under 3 Minutes

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

BART Model Explained

AI Reading List - Part 3

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

AI Reading List (by Ilya Sutskever) - Part 2

Joint, Marginal and Conditional Probability Explained

AI Reading List (by Ilya Sutskever) - Part 4

AI Reading List (by Ilya Sutskever) - Part 5

ROUGE Score Explained

BLEU Score Explained

BLEU Score Explained

AI Reading List - Part 2

AI Reading List (by Ilya Sutskever) - Part 1

Marginal, Joint and Conditional Probabilities Explained

Spearman Correlation Explained

Why ReLU Is Better Than Other Activation Functions | Tanh Saturating Gradients

Gated Recurrent Unit (GRU) Equations Explained

Cross-Validation Explained

Linear Regression vs Maximum Likelihood

Covariance Explained

Why Batch Normalization (batchnorm) Works

Word Error Rate (WER) Explained

Gradient Boosting with Regression Trees Explained

Z-Test Explained

Object Detection Part 7: Detection Transformers (DETR), Object Queries

Why We Don't Use the Mean Squared Error (MSE) Loss in Classification

Why We Divide by N-1 in the Sample Variance (The Bessel's Correction)

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