Deep Learning Overview

What is Deep Learning

Deep Learning is the engine of the current AI revolution. From GPT to Stable Diffusion, from AlphaFold to autonomous driving — all built on deep learning. It's a branch of machine learning using multi-layer neural networks to learn hierarchical representations.

Why Learn Deep Learning

Core weapon of AI. Understanding deep learning is essential for understanding LLMs, multimodal AI, and generative AI.

Interview must-know. CNN, RNN, Transformer, attention mechanisms — high-frequency AI interview topics.

Foundation for research. Deep learning is essential for AI research.

Core Knowledge Modules

Neural Network Fundamentals

• Perceptron and MLP
• Backpropagation
• Activation functions: ReLU, Sigmoid, Tanh, GELU
• Optimization: SGD, Momentum, Adam, AdamW
• Regularization: Dropout, Batch Normalization

Convolutional Neural Networks (CNN)

• Convolution, pooling
• Classic architectures: LeNet, AlexNet, VGG, ResNet

Recurrent Neural Networks (RNN)

• RNN, LSTM, GRU

Transformer Architecture

• Self-Attention
• Multi-Head Attention
• BERT, GPT, ViT

Generative Models

• VAE, GAN, Diffusion Models

How to Learn

Start with Andrew Ng, then read d2l. Andrew Ng builds the framework; d2l helps you implement in PyTorch.

Implement from scratch with PyTorch. Don't just call high-level APIs.

Do the CS231n labs. Implement CNNs from scratch.

Resources in This Module