PyTorch is currently one of the most popular deep learning frameworks. It is easy to use, flexible, and supports dynamic graphs, so it is favored by researchers and engineers. If you want to learn PyTorch, here is a learning path for getting started with PyTorch.
- Learn the basics:
- Learning the Python programming language: PyTorch uses Python as the main programming language, so you need to learn the Python programming language first.
- Learn basic mathematical knowledge: Deep learning requires the use of basic mathematical knowledge, such as linear algebra, calculus, etc.
- Learn the basic concepts and operations of PyTorch:
- Basic concepts of PyTorch: Understand the basic concepts of PyTorch, such as tensors, automatic differentiation, calculation graphs, etc.
- Basic operations of PyTorch: Learn the basic operations of PyTorch, such as tensor operations, automatic derivation, model building, etc.
- Practice items:
- Official tutorials: PyTorch officially provides many tutorials that can help you get started with PyTorch quickly. It is recommended to start with the official tutorials.
- Kaggle competition: Kaggle is a well-known machine learning competition platform. By participating in the Kaggle competition, you can exercise your PyTorch practical ability.
- Other practical projects: You can choose some practical deep learning application fields, such as computer vision, natural language processing, etc., to practice actual projects.
- Delve into:
- PyTorch source code: An in-depth understanding of PyTorch source code can help you understand the internal implementation and mechanism of PyTorch.
- Deep learning papers: Reading papers in related fields can help you understand the latest deep learning progress and research directions. It is recommended to start reading from classic papers, such as AlexNet, VGG, ResNet, etc.
The above is a basic learning route for getting started with PyTorch. It is recommended to learn and practice step by step in the above order. Of course, deep learning is a field widely used in real-world scenarios. It is recommended to maintain an attitude of learning and updating knowledge, pay attention to the latest research progress and practical applications, and continuously expand your deep learning knowledge and skills.