1. Learn the introductory knowledge of large models
Basic knowledge of deep learning: Understand the basic concepts, algorithms and models in deep learning, including neural networks, convolutional neural networks, recurrent neural networks, etc.
Programming ability: Master at least one programming language, such as Python, C++, etc., and be familiar with commonly used deep learning frameworks, such as TensorFlow, PyTorch, etc.
Mathematics foundation: Have basic knowledge of advanced mathematics, linear algebra, probability theory and mathematical statistics to better understand deep learning algorithms and models.
Basics of machine learning: Understand the basic concepts, algorithms and models in machine learning, such as classification, regression, clustering, etc.
Data processing capabilities: Familiar with basic processes and methods of data processing, such as data cleaning, data preprocessing, data visualization, etc.
The following is a learning route for learning large models:
Learn basic knowledge of deep learning: Understand the basic principles, algorithms and models of deep learning, including neural networks, convolutional neural networks, recurrent neural networks, etc.
Learn programming languages and deep learning frameworks: Master at least one programming language and deep learning framework, such as Python and TensorFlow or PyTorch.
Learn data processing and analysis: Be familiar with the basic processes and methods of data processing, such as data cleaning, data preprocessing, data visualization, etc.
Learn machine learning and statistical knowledge: Understand the basic concepts, algorithms and models in machine learning, such as classification, regression, clustering, etc. At the same time, master commonly used statistical methods, such as hypothesis testing, variance analysis, etc.
Learn the principles and applications of large models: Understand the principles and application scenarios of large models, such as natural language processing, computer vision, etc. At the same time, master the training and deployment methods of large models.
Practical projects and case analysis: Deepen the understanding and application of large models through practical projects and case analysis. You can try to use large models to solve practical problems, such as text classification, image recognition, etc.
Continuous learning and follow-up: With the continuous development of technology, the application scenarios and algorithms of large models are also constantly updated and improved. Therefore, there is a need to continuously learn and keep up with the latest technologies and applications.
2. Learning large models requires mastering the following Python knowledge
Basic syntax: Understand the basic syntax of Python, including variables, data types, control flow, functions, etc.
Data processing: Be familiar with data types in Python, such as lists, tuples, dictionaries, sets, etc., and master their basic operations. At the same time, learn how to use Python for data processing, such as data cleaning, data preprocessing, etc.
Scientific computing: Be familiar with scientific computing libraries in Python, such as NumPy, Pandas, etc., for numerical calculations and data analysis.
Machine learning libraries: Understand and master commonly used machine learning libraries, such as Scikit-learn, TensorFlow, PyTorch, etc., in order to use large models for machine learning tasks.
Deep learning framework: Be familiar with and master deep learning frameworks, such as TensorFlow, PyTorch, etc., in order to train and deploy large models.
Version control and code debugging: Understand and master version control tools (such as Git) and code debugging skills to better manage and debug code.
3. TensorFlow framework
TensorFlow is an open source machine learning library developed by the Google Brain team. It is widely used in the programming implementation of various machine learning algorithms, including deep neural networks and other neural networks. TensorFlow takes the form of data flow graphs. Nodes represent mathematical operations in the graph, and lines (edges) in the graph represent multi-dimensional data arrays, namely tensors, that are interconnected between nodes.
TensorFlow has a multi-level structure, can be deployed on various servers, PC terminals and web pages, and supports high-performance numerical computing on GPU and TPU. It is widely used in Google's internal product development and scientific research in various fields. At the same time, TensorFlow also has multiple projects including TensorFlow Hub, TensorFlow Lite, TensorFlow Research Cloud, and various application programming interfaces (Application Programming Interface, API).
PyTorch is an open source Python machine learning library based on Torch for applications such as natural language processing. It can be regarded as numpy with GPU support, and it can also be regarded as a powerful deep neural network with automatic derivation function.
4. PyTorch framework
Key features of PyTorch include:
Tensor computing capabilities: PyTorch provides a data structure of multi-dimensional arrays (also called tensors) that can be used to perform various mathematical operations. It also provides a rich library for tensor calculations.
Automatic differentiation: PyTorch provides automatic differentiation functionality through its Autograd module, which is very useful for gradient descent and optimization.
Dynamic computation graph: Unlike other deep learning frameworks (such as early versions of TensorFlow) that use static computation graphs, PyTorch uses dynamic computation graphs. This means that graphs are constructed at runtime, which enables more flexible model building.
Simple API: PyTorch's API is designed to be intuitive and easy to use, which makes developing and debugging models easier.
Python integration: Because PyTorch is tightly integrated with Python, it can easily work with the Python ecosystem including NumPy, SciPy, and Matplotlib.
Community and ecosystem: Due to its flexibility and ease of use, PyTorch has won the favor of a large number of developers and researchers. This has resulted in an active community and a large number of third-party libraries and tools.
Multi-platform and multi-backend support: PyTorch not only supports CPU, but also supports NVIDIA and AMD GPUs. It also has a production-ready deployment solution - TorchServe.
Rich pre-training models and toolboxes: PyTorch provides a rich set of pre-training models and data loading tools through libraries such as torchvision, torchaudio and torchtext.