🔥 PyTorch Mastery — Beginner to Production Engineer

The Complete PyTorch Reference — From Zero to Production

Learn every major PyTorch concept with hands-on notebooks, common bugs, and exercises — all in one place.

Part of the Mastery Series: 

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Why PyTorch Mastery?

Most PyTorch tutorials give you either:

• 🔴 Copy-paste code with no explanation of why it works, or
• 🔴 Dense academic papers with no practical implementation

These notebooks bridge that gap. Every notebook gives you:

✅ Plain English explanation → ✅ Visual intuition → ✅ Runnable code → ✅ Common bugs → ✅ Exercises

Whether you are a complete beginner or an experienced engineer brushing up before an interview, this is your one-stop reference.

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Who is this for?

🎓 Students	👨‍💻 Engineers	🔥 Interview Prep
Learning PyTorch from scratch with one structured, progressive path	Quickly look up syntax, parameters, and best practices mid-project	Concepts explained clearly + common interview Q&A covered in every notebook

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Repository Structure

pytorch-mastery/
│
├── 00_setup/                              # Environment, CUDA/CPU/MPS detection
│
├── 01_fundamentals/                       # Core PyTorch building blocks
│   ├── tensors.ipynb                      # Creation, dtypes, devices, broadcasting
│   ├── autograd.ipynb                     # Autograd, computation graph, backward()
│   ├── nn_module.ipynb                    # nn.Module, parameters, state_dict
│   ├── datasets_dataloaders.ipynb         # Dataset, DataLoader, collate, samplers
│   ├── training_loop_from_scratch.ipynb   # Full training loop: batch, loss, backward, step
│   └── optimizers_losses.ipynb            # SGD/Adam/AdamW, loss functions, LR schedulers
│
├── 02_core_deep_learning/                 # Classical deep learning
│   ├── 01_mlp/                            # Fully connected nets, activations
│   ├── 02_cnn/                            # Convolutions, pooling, image classifiers
│   ├── 03_rnn_lstm_gru/                   # Sequence modeling, packed sequences
│   ├── 04_regularization/                 # Dropout, weight decay, label smoothing
│   ├── 05_initialization/                 # Xavier/Kaiming init
│   ├── 06_debugging_and_visualization/    # Loss curves, gradient norms, hooks
│   └── 07_transfer_learning/              # Freezing, feature extraction, fine-tuning
│
├── 03_advanced_architectures/             # Transformers, GANs, VAEs, diffusion
├── 04_llm_finetuning/                     # LoRA, QLoRA, RLHF, quantization
├── 05_production_engineering/             # AMP, DDP, serving, profiling
└── 06_end_to_end_projects/                # Full pipelines: data → train → serve

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📚 Notebooks Quick Access

Click a notebook name to view it on GitHub, or the Colab badge to open it directly in Google Colab — no setup required.

🔥 01 — Fundamentals

Notebook	What You'll Learn	Open
tensors.ipynb	Creation, dtypes, devices, broadcasting, views vs copies, in-place ops
autograd.ipynb	requires_grad, computation graph, backward(), grad_fn, detaching, no_grad
nn_module.ipynb	nn.Module, parameters vs buffers, state_dict, custom layers
datasets_dataloaders.ipynb	Dataset, DataLoader, collate functions, samplers, multi-worker loading
training_loop_from_scratch.ipynb	Full training loop: batches, loss, backward(), optimizer step, eval
optimizers_losses.ipynb	SGD/Adam/AdamW internals, loss functions, LR schedulers

🧠 02 — Core Deep Learning

Notebook	What You'll Learn	Open
mlp.ipynb	Fully connected nets, activation functions, forward pass	🔜 Coming soon
cnn.ipynb	Convolutions, pooling, receptive fields, image classifiers	🔜 Coming soon
rnn_lstm_gru.ipynb	Sequence modeling, vanishing gradients, packed sequences	🔜 Coming soon
regularization.ipynb	Dropout, weight decay, label smoothing, early stopping	🔜 Coming soon
initialization.ipynb	Xavier/Kaiming, why bad init kills training	🔜 Coming soon
debugging.ipynb	Loss curves, gradient norms, hooks, TensorBoard	🔜 Coming soon
transfer_learning.ipynb	Freezing/unfreezing, feature extraction vs fine-tuning	🔜 Coming soon

🏗️ 03 — Advanced Architectures

Notebook	What You'll Learn	Open
attention_transformers.ipynb	Self-attention, multi-head attention, encoder/decoder from scratch	🔜 Coming soon
vision_transformers.ipynb	Patch embeddings, ViT	🔜 Coming soon
resnet_efficientnet.ipynb	Skip connections, modern CNN backbones	🔜 Coming soon
gans.ipynb	Generator/discriminator training dynamics	🔜 Coming soon
vaes.ipynb	Latent variable models, reparameterization trick	🔜 Coming soon
gnn.ipynb	Message passing, GCN/GAT basics	🔜 Coming soon
diffusion_models.ipynb	Forward/reverse diffusion, denoising objective	🔜 Coming soon

🦙 04 — LLM Fine-tuning

Notebook	What You'll Learn	Open
huggingface_ecosystem.ipynb	transformers, datasets, tokenizers, accelerate	🔜 Coming soon
full_finetuning.ipynb	Full-parameter fine-tuning, memory math	🔜 Coming soon
lora_qlora.ipynb	LoRA, QLoRA, PEFT internals, adapter merging	🔜 Coming soon
prompt_instruction_tuning.ipynb	Instruction datasets, chat templates	🔜 Coming soon
rlhf_dpo.ipynb	Reward modeling, PPO vs DPO	🔜 Coming soon
quantization.ipynb	int8/int4, GPTQ/AWQ/bitsandbytes basics	🔜 Coming soon
inference_optimization.ipynb	KV cache, batching, speculative decoding basics	🔜 Coming soon
evaluation.ipynb	Perplexity, task benchmarks, eval harnesses	🔜 Coming soon

⚙️ 05 — Production Engineering

Notebook	What You'll Learn	Open
mixed_precision.ipynb	AMP, fp16/bf16, loss scaling	🔜 Coming soon
distributed_training.ipynb	DataParallel vs DDP vs FSDP, multi-GPU basics	🔜 Coming soon
experiment_tracking.ipynb	Weights & Biases / MLflow, checkpointing strategy	🔜 Coming soon
model_serving.ipynb	TorchServe, FastAPI serving, batching requests	🔜 Coming soon
onnx_torchscript.ipynb	Exporting models for production runtimes	🔜 Coming soon
testing_ml_code.ipynb	Unit tests for data pipelines, models, training steps	🔜 Coming soon
profiling_optimization.ipynb	torch.profiler, memory leaks, torch.compile	🔜 Coming soon
reproducibility_config.ipynb	Seeding, deterministic runs, Hydra/YAML configs	🔜 Coming soon

🚀 06 — End-to-End Projects

Project	What You'll Build	Open
image_classification	CNN pipeline: raw images → served model	🔜 Coming soon
text_classification	Transformer encoder + HF datasets	🔜 Coming soon
llm_finetuning	QLoRA on an instruction dataset, end to end	🔜 Coming soon
recommendation_system	Embeddings + two-tower model	🔜 Coming soon
multimodal	Image + text	🔜 Coming soon

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🧩 What's Inside Each Notebook

Every notebook follows this structure:

Section	What You Get
📖 Concept Overview	Plain English explanation of what it is, why it exists, when to use it
📐 Math / Intuition	The key equations and mental models behind the concept
🔢 Runnable Code	Numbered sections — minimal, heavily commented, actually executed
⚠️ Common Bugs	The mistakes everyone makes here + how to diagnose them
✏️ Exercises	Practice problems with a scratch code cell to test your understanding

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⚡ Quick Start

# 1. Clone the repo
git clone https://github.com/himanshu231204/PyTorch-Mastery.git
cd PyTorch-Mastery

# 2. Create environment
conda create -n pytorch-mastery python=3.10 -y
conda activate pytorch-mastery

# 3. Install dependencies
pip install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cu121
pip install jupyter matplotlib numpy pandas

# 4. Launch Jupyter
jupyter notebook

Or — skip all setup and click any Open in Colab badge above.

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🔧 When You're Stuck

1. Find the closest matching notebook in the tables above.
2. Jump to its Common Bugs section — most "why isn't this working" moments are already there.
3. Run the notebook's code cells and compare output to your own code.
4. For production issues (OOM, slow training, distributed weirdness) → check 05_production_engineering.

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📊 Status

This repo is being built incrementally, one notebook at a time.

• 01_fundamentals/tensors.ipynb
• 01_fundamentals/autograd.ipynb
• 01_fundamentals/nn_module.ipynb
• 01_fundamentals/datasets_dataloaders.ipynb
• 01_fundamentals/training_loop_from_scratch.ipynb
• 01_fundamentals/optimizers_losses.ipynb
• Everything else (structure exists, content coming — built and confirmed one notebook at a time)

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📄 License

MIT — use it however you want.

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Made with ❤️ for the PyTorch community. PRs and issues welcome!