Una buona lettura. #deeplearning #ai
I bought this (little) book, studied it, and bookmarked my favorite visuals of AI math by hand ✍️ 📘 "The Little Book of Deep Learning" by Prof. François Fleuret. Link to the book is in the comment.👇 == Table of Content == I Foundations 1 Machine Learning - 1.1 Learning from data - 1.2 Basis function regression - 1.3 Under and overfitting - 1.4 Categories of models 2 Efficient Computation - 2.1 GPUs, TPUs, and batches - 2.2 Tensors 3 Training - 3.1 Losses - 3.2 Autoregressive models - 3.3 Gradient descent - 3.4 Backpropagation - 3.5 The value of depth - 3.6 Training protocols - 3.7 The benefits of scale II Deep Models 4 Model Components - 4.1 The notion of layer - 4.2 Linear layers - 4.3 Activation functions - 4.4 Pooling - 4.5 Dropout - 4.6 Normalizing layers - 4.7 Skip connections - 4.8 Attention layers - 4.9 Token embedding - 4.10 Positional encoding 5 Architectures - 5.1 Multi-Layer Perceptrons - 5.2 Convolutional networks - 5.3 Attention models III Applications 6 Prediction - 6.1 Image denoising - 6.2 Image classification - 6.3 Object detection - 6.4 Semantic segmentation - 6.5 Speech recognition - 6.6 Text-image representations - 6.7 Reinforcement learning 7 Synthesis - 7.1 Text generation - 7.2 Image generation 8 The Compute Schism - 8.1 Prompt Engineering - 8.2 Quantization - 8.3 Adapters - 8.4 Model merging #deeplearning #aibyhand [REPOST♻️] Share this book with others!
