Top-down, code-first course teaching practical deep learning, neural network training dynamics, transfer learning, and PyTorch foundations.
Created by Jeremy Howard, fast.ai's Practical Deep Learning for Coders takes a top-down pedagogical approach—getting models training early and progressively peeling back the layers to implement stochastic gradient descent, matrix multiplications, activations, and training loops in pure PyTorch and Excel/notebooks.
Course status is tracked independently from roadmap topic checkboxes so you can mark external courses without altering your topic mastery percentage.
Phase 3 Practical Complement to Karpathy Zero to Hero (Modules 3.1, 3.3 & 3.4)
All video lectures, Jupyter notebooks, open-source book chapters (fastbook), and forums are 100% free without ads or paywalls.
Status: Verified (2026-11-09)
Review these foundations before starting so you get the most out of this material:
In Phase 3: Deep Learning & Neural Networks
Use Part 1 Lessons 3–5 for practical intuition on SGD, learning rates, matrix multiplication, and building neural nets from the foundations.
Neurons, feedforward layers, modern activations (ReLU, GELU, SiLU), forward/backward passes, initialization, normalization, and residual streams.
PyTorch tensors, GPU acceleration, autograd mechanics, nn.Module architecture, DataLoaders, checkpointing, and mixed precision.
Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), LSTMs, Autoencoders, Generative Models, and Reinforcement Learning fundamentals.
A code-first introduction to neural networks covering automatic differentiation, backpropagation, language models, and GPT implementations.
Official 'Learn the Basics' tutorial series covering PyTorch Tensors, Datasets/DataLoaders, `nn.Module`, `torch.autograd`, optimization loops, and model checkpointing.
Free interactive textbook combining mathematical explanations, code, and practical deep-learning concepts.