docs

PyTorch Training Documentation

Complete study guide for PyTorch deep learning fundamentals

Study Path Overview

This documentation follows a hybrid structure:

• docs/ (you are here) - Conceptual learning and theory
• module-X/ - Hands-on labs and practice code

Quick Start

1. Choose your module below
2. Read the conceptual documentation in docs/module-X/
3. Practice with labs in module-X/ folder

---

Module 1: Deep Learning Foundations with PyTorch

Goal: Build conceptual understanding of deep learning and master PyTorch tensor fundamentals

Study Path

Part 1: Conceptual Foundation

Order	Topic	Description	Lab Location
1	Introduction to Deep Learning	The paradigm shift, when to use DL, when to avoid it	N/A (Conceptual)
2	Neural Network Anatomy	Key components and the foundational workflow	N/A (Conceptual)
3	Learning Methodology	Strategies for effective deep learning study	N/A (Conceptual)

Part 2: PyTorch Technical Essentials

Order	Topic	Description	Lab Location
4	PyTorch Essentials	What is PyTorch, setup, 2.0 features	module-01/pytorch-fundamentals/
5	Tensor Foundations	Creation, critical attributes (shape, rank, device)	module-01/pytorch-fundamentals/
6	Tensor Operations	Math operations, matrix multiplication, aggregation	module-01/pytorch-fundamentals/
7	Tensor Manipulation	Reshaping, indexing, device management	module-01/pytorch-fundamentals/

Module 1 Documentation

Part 1: Conceptual Foundation

• Introduction to Deep Learning
• Neural Network Anatomy
• Learning Methodology

Part 2: PyTorch Technical Essentials

• PyTorch Essentials
• Tensor Foundations
• Tensor Operations
• Tensor Manipulation
• Exercises Quick Reference

---

Module 2: PyTorch Workflow Fundamentals

Goal: Learn the complete PyTorch workflow for building and training models

Study Path

Order	Topic	Description	Lab Location
1	Data Preparation	Loading and preprocessing data with DataLoader	module-02/pytorch-workflow/
2	Building Models	Creating neural networks with nn.Module	module-02/pytorch-workflow/
3	Training Loop	Loss functions, optimizers, and training loops	module-02/pytorch-workflow/
4	Saving & Loading	Model checkpointing and inference	module-02/pytorch-workflow/

Module 2 Documentation

• Introduction & Setup
• Data Preparation
• Building Models
• Training Loop
• Saving & Loading
• Exercises Quick Reference

---

Module 3: Neural Network Classification

Goal: Build and evaluate classification models with PyTorch

Study Path

Order	Topic	Description	Lab Location
1	Classification Basics	Binary and multi-class classification concepts	module-03/neural-network-classification/
2	Architecture Design	Designing neural network architectures for classification	module-03/neural-network-classification/
3	Training & Evaluation	Training classifiers and evaluating performance	module-03/neural-network-classification/
4	Model Deployment	Saving and using trained models for inference	module-03/neural-network-classification/

Module 3 Documentation

• Classification Introduction
• Architecture Components
• Data Preparation
• Building Models
• Training & Evaluation
• Improving Models
• Evaluation Metrics
• Exercises Quick Reference

---

Study Tips

For Each Module

1. Read first - Start with the conceptual guide in docs/
2. Practice second - Run the lab exercises in module-X/
3. Experiment - Modify code and observe changes
4. Review - Re-read documentation with practical context

For Hands-on Skills

1. Complete all lab exercises - Don't skip!
2. Break things intentionally - Learn to troubleshoot
3. Build variations - Modify exercises to solve new problems
4. Document your learnings - Keep notes

Example Study Workflow

# 1. Read the conceptual guide
cat docs/module-01/tensor-basics.md

# 2. Navigate to the lab
cd module-01/pytorch-fundamentals

# 3. Run the exercises
python 01_tensor_creation.py

# 4. Experiment and learn
python 02_tensor_operations.py

# 5. Build your own variation
# Try creating your own tensor operations

---

Additional Resources

External References

PyTorch Official Resources:

• PyTorch Documentation
• PyTorch Tutorials
• PyTorch GitHub Repository

Deep Learning Fundamentals:

• Deep Learning with PyTorch Book
• PyTorch YouTube Channel

Python & ML Prerequisites:

• NumPy Documentation
• Python Machine Learning Book

Internal Tools

• module-01/pytorch-fundamentals/ - Tensor operations practice
• module-02/pytorch-workflow/ - Complete workflow labs
• module-03/neural-network-classification/ - Classification model labs

---

Progress Tracking

Track your progress by checking off completed modules:

Module 1: Deep Learning Foundations

• Introduction to Deep Learning
• Neural Network Anatomy
• Learning Methodology
• PyTorch Essentials
• Tensor Foundations
• Tensor Operations
• Tensor Manipulation

Module 2: PyTorch Workflow Fundamentals

• Data Preparation
• Building Models
• Training Loop
• Saving & Loading Models

Module 3: Neural Network Classification

• Classification Basics
• Architecture Design
• Training & Evaluation
• Model Deployment

---

Last Updated: January 2026