A complete guide to getting started with deep learning in Python - Learning neural network development with PyTorch
Series Overview
This series is a practical educational content consisting of 5 chapters to master deep learning fundamentals with PyTorch.
PyTorch is a cutting-edge deep learning framework developed by Facebook. With its intuitive Python-like coding style, dynamic computation graphs, and powerful automatic differentiation capabilities, it is widely used from research to production. This series systematically teaches you from PyTorch fundamentals to practical neural network development.
Features:
- ✅ Practice-focused: Learn with over 50 working code examples
- ✅ From fundamentals to applications: From Tensor basics to neural network construction
- ✅ Comparative explanations: Clarify differences from TensorFlow and NumPy
- ✅ Implementation exercises: Build practical skills with 5-7 exercises per chapter
- ✅ GPU support: Practice high-speed computation using CUDA
Total learning time: 100-120 minutes (including code execution and exercises)
Learning Objectives
By completing this series, you will acquire the following skills:
- PyTorch fundamentals: Creating and manipulating Tensors, GPU computation
- Automatic differentiation system: Gradient computation and backpropagation using autograd
- Neural network construction: Model definition using nn.Module
- Training loop implementation: Practical use of data loaders, loss functions, and optimization methods
- Practical development skills: Model saving/loading, debugging, performance optimization
How to Learn
Recommended Learning Order
🎯 Complete Master Course (All chapters recommended)
Target: Deep learning beginners, those who want to learn PyTorch from scratch
Approach: Chapter 1 → Chapter 2 → Chapter 3 → Chapter 4 → Chapter 5
Duration: 100-120 minutes
Outcome: PyTorch mastery, neural network construction skills, practical development expertise
⚡ Fast Track Course (For experienced learners)
Target: Those with other framework experience who want to efficiently learn PyTorch features
Approach: Chapter 1 (overview) → Chapter 3 (autograd) → Chapter 4 (nn.Module) → Chapter 5 (practice)
Duration: 70-80 minutes
Outcome: Understanding PyTorch-specific features, immediately applicable skills
Chapter Details
Chapter 1: PyTorch Fundamentals
Learning Content
- What is PyTorch - Comparison with TensorFlow
- Installation and environment setup
- Tensor fundamentals
- PyTorch's three main components
- How dynamic computation graphs work
- Your first PyTorch program
Chapter 2: Tensor OperationsComing Soon
Learning Content
- Tensor creation and initialization
- Shape operations (reshape, view, transpose)
- Indexing and slicing
- Mathematical and matrix operations
- Broadcasting
- Data movement between CPU/GPU
Chapter 3: Automatic Differentiation (Autograd)Coming Soon
Learning Content
- What is automatic differentiation
- Computation graphs and gradient calculation
- torch.Tensor and requires_grad
- How to use the backward() method
- Gradient accumulation and initialization
- Techniques for stopping differentiation
Chapter 4: Neural Network ConstructionComing Soon
Learning Content
- nn.Module basics
- Layer definition and composition
- Loss functions and optimization methods
- Training loop implementation
- How to use DataLoader
- Model saving and loading
Chapter 5: Practical ProjectComing Soon
Learning Content
- Image classification task implementation
- CNN model construction
- Data augmentation and batch normalization
- Training visualization and monitoring
- Hyperparameter tuning
- Model deployment and inference
Prerequisites
To get the most out of this series, the following prerequisite knowledge is recommended:
Required
- ✅ Python basics: Using variables, functions, classes, lists, and dictionaries
- ✅ NumPy basics: Array manipulation, basic numerical computation
- ✅ Machine learning overview: Basic concepts of supervised learning, loss functions, gradient descent
Recommended
- 💡 Linear algebra basics: Vectors, matrices, matrix multiplication
- 💡 Calculus basics: Partial derivatives, chain rule
- 💡 Pandas basics: Data loading and preprocessing
For beginners: We recommend completing the "Machine Learning Introduction Series" first.
Frequently Asked Questions (FAQ)
Q1: What are the differences between PyTorch and TensorFlow?
A: PyTorch adopts dynamic computation graphs, enabling intuitive Python-like coding. TensorFlow uses static computation graphs (TF 2.0+ also supports Eager Execution) and has strengths in deployment. PyTorch is widely used in research, while TensorFlow is common in production, though you can choose based on use case.
Q2: Is a GPU environment required?
A: CPU is fine for the learning phase. However, for large-scale models or image processing, having a GPU (CUDA-enabled) significantly speeds up training. You can practice without environment setup using Google Colab's free GPU.
Q3: How much learning time is required?
A: 100-120 minutes for all chapters. At a pace of one chapter per day (20-30 minutes), you can complete it in 5 days. Concentrated learning over the weekend is also possible. Understanding deepens by actually writing code as you progress.
Q4: Is there compatibility with NumPy?
A: Yes. PyTorch Tensors and NumPy ndarrays can be easily converted to each other. It's easy to migrate existing NumPy code to PyTorch, and many APIs are similar to NumPy.
Q5: What should I learn after this series?
A: We recommend proceeding to series that delve deeper into specific architectures, such as "Convolutional Neural Networks (CNN) Introduction," "Recurrent Neural Networks (RNN) Introduction," or "Transformer Introduction."
Q6: Will I reach a level usable in practice?
A: This series solidifies PyTorch fundamentals. For practical work, you'll additionally need domain-specific expertise (images, text, audio, etc.), deployment, and optimization techniques. However, with these fundamentals, the bridge to practical application becomes smoother.
Let's Get Started!
Ready to dive into the world of deep learning with PyTorch? Start with Chapter 1 and master this cutting-edge deep learning framework!
Update History
- 2025-12-01: v1.0 Initial release (Chapter 1 only)
Take your first step toward becoming an AI engineer with PyTorch!