Deep Learning Study Guide¶

Introduction¶

This folder provides a comprehensive guide to deep learning theory and practice, combining conceptual lessons with from-scratch model implementations. PyTorch is the primary framework, and the curriculum follows a pedagogical approach that integrates four levels of learning:

Level 1 (NumPy Scratch): Build models from raw NumPy to understand fundamental mechanics
Level 2 (PyTorch Low-Level): Implement using PyTorch primitives (tensors, autograd) without high-level APIs
Level 3 (Paper Reproduction): Read original papers and reproduce key architectures
Level 4 (Code Analysis): Analyze production-quality implementations from frameworks and research repos

This merged approach ensures you understand both the "why" and the "how" of deep learning, from mathematical foundations to practical deployment.

Target Audience¶

Learners who have completed the Machine_Learning folder
Readers comfortable with Python, NumPy, and basic ML concepts (gradient descent, overfitting, train/test splits)
Anyone seeking a rigorous, implementation-focused deep learning education

Learning Roadmap¶

┌──────────────┐     ┌──────────────┐     ┌──────────────┐     ┌──────────────┐
│  Foundations │────▶│     CNN      │────▶│   Sequence   │────▶│ Transformers │
│   L01-L06    │     │   L07-L12    │     │   L13-L15    │     │   L16-L22    │
└──────────────┘     └──────────────┘     └──────────────┘     └──────────────┘
                                                                        │
                                                                        ▼
┌──────────────┐     ┌──────────────┐     ┌──────────────┐     ┌──────────────┐
│   Practical  │◀────│   Advanced   │◀────│  Generative  │◀────│   Training   │
│   L39-L42    │     │   L34-L38    │     │   L28-L33    │     │   L23-L27    │
└──────────────┘     └──────────────┘     └──────────────┘     └──────────────┘

Recommended Path: 1. Start with Foundations (L01-L06) to master PyTorch basics and backpropagation 2. Progress through CNN (L07-L12) for computer vision fundamentals 3. Learn Sequence Models (L13-L15) for temporal data 4. Master Transformers (L16-L22), the backbone of modern NLP and vision 5. Study Training Essentials (L23-L27) for optimization, loss functions, and normalization 6. Explore Generative Models (L28-L33) for GANs, VAEs, and Diffusion 7. Dive into Advanced topics (L34-L38) for multimodal learning and modern architectures 8. Apply knowledge with Practical projects (L39-L42)

File List¶

Lesson	Filename	Difficulty	Description
Block 1: Foundations
L01	`01_Tensors_and_Autograd.md`	⭐	Tensor operations, autograd, computational graphs
L02	`02_Neural_Network_Basics.md`	⭐⭐	Activation functions, loss, forward/backward pass
L03	`03_Backpropagation.md`	⭐⭐	Chain rule, gradient flow, vanishing/exploding gradients
L04	`04_Training_Techniques.md`	⭐⭐	Regularization, dropout, batch normalization, early stopping
L05	`05_Impl_Linear_Logistic.md`	⭐⭐	Implementation: Linear & Logistic regression from scratch
L06	`06_Impl_MLP.md`	⭐⭐	Implementation: Multilayer Perceptron (NumPy → PyTorch)
Block 2: Convolutional Neural Networks
L07	`07_CNN_Basics.md`	⭐⭐	Convolution, pooling, feature maps, LeNet
L08	`08_CNN_Advanced.md`	⭐⭐⭐	ResNet, Inception, skip connections, 1x1 convolutions
L09	`09_Transfer_Learning.md`	⭐⭐⭐	Pretrained models, fine-tuning, domain adaptation
L10	`10_Impl_CNN_LeNet.md`	⭐⭐⭐	Implementation: LeNet-5 on MNIST
L11	`11_Impl_VGG.md`	⭐⭐⭐	Implementation: VGG-16 architecture
L12	`12_Impl_ResNet.md`	⭐⭐⭐	Implementation: Residual Networks (ResNet-18/34)
Block 3: Sequence Models
L13	`13_RNN_Basics.md`	⭐⭐⭐	Recurrent networks, hidden states, sequence-to-sequence
L14	`14_LSTM_GRU.md`	⭐⭐⭐	Long Short-Term Memory, Gated Recurrent Units
L15	`15_Impl_LSTM_GRU.md`	⭐⭐⭐	Implementation: LSTM/GRU for text classification
Block 4: Attention and Transformers
L16	`16_Attention_Transformer.md`	⭐⭐⭐⭐	Self-attention, multi-head attention, Transformer architecture
L17	`17_Attention_Deep_Dive.md`	⭐⭐⭐⭐	Query/Key/Value, scaled dot-product, positional encoding
L18	`18_Impl_Transformer.md`	⭐⭐⭐⭐	Implementation: Transformer encoder/decoder from scratch
L19	`19_Impl_BERT.md`	⭐⭐⭐⭐	Implementation: BERT pretraining (masked LM)
L20	`20_Impl_GPT.md`	⭐⭐⭐⭐	Implementation: GPT-style autoregressive model
L21	`21_Vision_Transformer.md`	⭐⭐⭐⭐	Patch embeddings, ViT architecture, DeiT
L22	`22_Impl_ViT.md`	⭐⭐⭐⭐	Implementation: Vision Transformer for image classification
Block 5: Training Essentials
L23	`23_Training_Optimization.md`	⭐⭐⭐	Learning rate schedules, gradient clipping, mixed precision
L24	`24_Loss_Functions.md`	⭐⭐⭐	Cross-entropy, focal loss, contrastive loss, triplet loss
L25	`25_Optimizers.md`	⭐⭐⭐	SGD, Adam, AdamW, learning rate warmup
L26	`26_Normalization_Layers.md`	⭐⭐⭐	Batch norm, layer norm, group norm, instance norm
L27	`27_TensorBoard.md`	⭐⭐	Logging, visualization, hyperparameter tracking
Block 6: Generative Models
L28	`28_Generative_Models_GAN.md`	⭐⭐⭐	Generator, discriminator, adversarial training
L29	`29_Impl_GAN.md`	⭐⭐⭐	Implementation: DCGAN for image generation
L30	`30_Generative_Models_VAE.md`	⭐⭐⭐	Variational autoencoders, latent space, reparameterization
L31	`31_Impl_VAE.md`	⭐⭐⭐	Implementation: VAE on MNIST/CIFAR-10
L32	`32_Diffusion_Models.md`	⭐⭐⭐⭐	DDPM, score-based models, denoising process
L33	`33_Impl_Diffusion.md`	⭐⭐⭐⭐	Implementation: Denoising Diffusion Probabilistic Model
Block 7: Multimodal and Advanced Topics
L34	`34_CLIP_Multimodal.md`	⭐⭐⭐⭐	Contrastive learning, vision-language models
L35	`35_Impl_CLIP.md`	⭐⭐⭐⭐	Implementation: CLIP-style image-text alignment
L36	`36_Self_Supervised_Learning.md`	⭐⭐⭐⭐	SimCLR, MoCo, BYOL, contrastive pretraining
L37	`37_Modern_Architectures.md`	⭐⭐⭐⭐	EfficientNet, ConvNeXt, Swin Transformer, NFNet
L38	`38_Object_Detection.md`	⭐⭐⭐⭐	RCNN, YOLO, RetinaNet, DETR, anchor-free methods
Block 8: Practical and Deployment
L39	`39_Practical_Image_Classification.md`	⭐⭐⭐⭐	End-to-end project: dataset, training, evaluation, deployment
L40	`40_Practical_Text_Classification.md`	⭐⭐⭐⭐	End-to-end NLP project: tokenization, fine-tuning, inference
L41	`41_Model_Saving_Deployment.md`	⭐⭐⭐	ONNX export, TorchScript, model serving (Flask, TorchServe)
L42	`42_Reinforcement_Learning_Intro.md`	⭐⭐⭐	DQN basics, policy gradients, bridge to RL topic

Total: 42 lessons (28 concept lessons + 14 implementation lessons)

Implementation Philosophy: The 4-Level Approach¶

This curriculum integrates theory with hands-on coding through a 4-level progression:

Level	Description	Tools	Example Lessons
L1: NumPy Scratch	Build models using only NumPy (no PyTorch `nn.Module`). Implement forward/backward passes manually.	NumPy arrays, manual gradient computation	L05, L06
L2: PyTorch Low-Level	Use PyTorch tensors and autograd, but avoid `nn.Linear`, `nn.Conv2d`. Define custom modules.	`torch.Tensor`, `autograd`, custom `nn.Module`	L10, L11, L15
L3: Paper Reproduction	Read original papers (Attention Is All You Need, BERT, etc.) and reproduce architectures.	PyTorch, paper pseudocode	L18, L19, L20, L22
L4: Code Analysis	Study production implementations (Hugging Face Transformers, torchvision models) and understand design patterns.	GitHub repos, library source code	L37, L38, L41

Why This Approach? - L1 ensures you understand the math (no "magic" libraries) - L2 teaches PyTorch idioms while retaining low-level control - L3 bridges academic papers to code - L4 prepares you for real-world ML engineering

Prerequisites¶

Programming: Proficiency in Python (functions, classes, list comprehensions)
Mathematics: Linear algebra (matrix multiplication, eigenvalues), calculus (derivatives, chain rule), basic probability
Machine Learning: Familiarity with supervised learning, loss functions, gradient descent (see Machine_Learning folder)
Libraries: NumPy basics (array indexing, broadcasting)

Environment Setup¶

Installation¶

# Install PyTorch (CPU version)
pip install torch torchvision matplotlib numpy

# For GPU support (CUDA 11.8 example)
pip install torch torchvision --index-url https://download.pytorch.org/whl/cu118

# Optional: TensorBoard for visualization
pip install tensorboard

Verify Installation¶

import torch
print(torch.__version__)  # e.g., 2.1.0
print(torch.cuda.is_available())  # True if GPU available

Recommended Tools¶

IDE: VS Code with Python extension, Jupyter notebooks for experimentation
GPU: NVIDIA GPU recommended for L28-L42 (Google Colab free tier works for most lessons)

Machine_Learning: Prerequisite for understanding loss functions, regularization, evaluation metrics
LLM_and_NLP: Advanced NLP applications (BERT, GPT fine-tuning, LangChain)
Foundation_Models: Scaling laws, LoRA, quantization, RAG
Computer_Vision: Applied CV with OpenCV, object detection, SLAM
Reinforcement_Learning: DQN, PPO, policy gradients (builds on L42)
Math_for_AI: Matrix calculus, optimization theory, probability

Study Tips¶

Don't Skip Implementations: Typing out code (even if copying) builds muscle memory. Resist the urge to only read.
Experiment Liberally: Change hyperparameters, swap activation functions, break code intentionally to see error messages.
Read Papers Alongside Code: For L18-L22, read the original papers. Notation in papers matches variable names in code.
Debug with Small Data: Test models on tiny datasets (10 samples) to catch bugs before full training.
Visualize Activations: Use TensorBoard (L27) to inspect gradients, weights, and feature maps.
Join Communities: PyTorch forums, r/MachineLearning, Papers with Code discussions.

Learning Outcomes¶

After completing this folder, you will be able to:

✅ Implement neural networks from scratch using NumPy and PyTorch
✅ Explain backpropagation, gradient descent, and autograd internals
✅ Build and train CNNs for image classification (ResNet, VGG)
✅ Implement Transformers, BERT, and GPT from research papers
✅ Train generative models (GANs, VAEs, Diffusion Models)
✅ Apply transfer learning and fine-tuning to real-world datasets
✅ Optimize training with advanced techniques (mixed precision, gradient clipping, learning rate schedules)
✅ Deploy models using ONNX, TorchScript, and web frameworks
✅ Read and reproduce state-of-the-art deep learning papers

Next Steps¶

For NLP: Proceed to LLM_and_NLP for large language models, RAG, and prompt engineering
For Vision: Explore Computer_Vision for OpenCV, 3D vision, and SLAM
For Efficiency: Study Foundation_Models for quantization, LoRA, and model compression
For RL: Advance to Reinforcement_Learning for DQN, PPO, and game agents
For Production: Check MLOps for experiment tracking, model serving, and CI/CD

Additional Resources¶

Official Docs: PyTorch Tutorials, PyTorch Documentation
Books: Deep Learning (Goodfellow et al.), Dive into Deep Learning (d2l.ai)
Courses: Stanford CS230, Fast.ai Practical Deep Learning
Papers: Papers with Code for implementations and benchmarks

Happy Learning! Start with 01_Tensors_and_Autograd.md and build your deep learning expertise step by step.