🔮 Deep Learning & Neural Architectures: Sub-Curriculum Index
Welcome to the Deep Learning & Neural Architectures curriculum. This track covers the foundational architectures that power modern artificial intelligence: backpropagation, deep feedforward nets, convolutional neural networks (CNNs) in computer vision, sequence models in NLP, Reinforcement Learning with Human Feedback (RLHF), generative models (VAEs, Diffusion), Transformer self-attention, and vision-language multimodal models.
Every guide in this series strictly follows a two-part learning format:
- ⚡ Quick Dive: Architecture diagrams, activation functions, tensor shape transformations, and PyTorch one-liners.
- 📖 Extended Guide: Mathematical derivations, backprop computational graphs, attention mechanisms, and PyTorch model implementations.
📚 Curriculum Roadmap
| # | Guide | Primary Topics Covered |
|---|---|---|
| 01 | Deep Learning Fundamentals | Universal approximation theorem, deep vs. shallow networks, vanishing/exploding gradients, and normalization layers. |
| 02 | Neural Networks & Backpropagation | Computational graphs, chain rule automatic differentiation, loss functions, and optimizers (SGD, Adam, AdamW). |
| 03 | NLP Foundations & Word Vectors | Tokenization (BPE, WordPiece), Word2Vec, GloVe, Recurrent Neural Networks (RNNs), and LSTMs. |
| 04 | Computer Vision & CNNs | Spatial convolution kernels, pooling layers, ResNet skip connections, object detection (YOLO), and Vision Transformers (ViT). |
| 05 | Reinforcement Learning & RLHF | Markov Decision Processes (MDP), Q-Learning, Policy Gradients (PPO), Reward Modeling, and RLHF for LLM alignment. |
| 06 | Generative Models: VAEs & Diffusion | Latent space representations, Variational Autoencoders (VAEs), Generative Adversarial Networks (GANs), and Denoising Diffusion (DDPM). |
| 07 | Transformers & Attention Mechanisms | Scaled Dot-Product Attention, Multi-Head Attention (MHA), Rotary Position Embeddings (RoPE), and FlashAttention. |
| 08 | Multimodal Architectures | Contrastive Language-Image Pretraining (CLIP), cross-attention fusion, image projection layers, and vision-language LLMs (LLaVA). |