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The official publication of "Python Deep Learning and Enterprise Project Practice"

How to efficiently get started with deep learning can be said to be a clich é d question. The first path is to start with traditional statistical learning and then follow the formulas in books to learn mathematics; Another approach is to start with experiments, by learning the deep learning framework PyTorch2.0 and specific project practical tasks. For students who want to achieve quick results, the first method is not recommended unless you have strong mathematical skills and want to do some theoretical work. For most people, it is better to start with deep learning frameworks, after all, deep learning is an experimental science that mainly focuses on project-based hands-on experiments.

The official publication of "Python Deep Learning and Enterprise Project Practice"_深度学习

This book is based on practical experience and provides a detailed introduction to the basic theories and necessary knowledge of deep learning in a simple and easy to understand way. At the same time, it guides readers to get started with artificial intelligence deep learning through hands-on operations.


This book is divided into 15 chapters, mainly including the relationship between artificial intelligence, machine learning and deep learning, the environment construction of deep learning framework PyTorch, the principle of deep learning and the knowledge related to convolutional neural network, the introduction of PyTorch minimalism, and enterprise level practical projects such as migration recognition flower recognition, garbage classification recognition, short-term power load prediction, air quality prediction, handwritten number recognition, face recognition and emotion recognition, image style migration, diabetes prediction, animation character portrait generation based on GAN, natural language processing based on big language model, monkeypox virus recognition, lung infection recognition, etc. Starting from the simplest common sense, enter the field of artificial intelligence deep learning and create a seamless and exciting learning experience. Readers of this book only need to have a basic knowledge of the Python language, without the need for a special mathematical foundation. By following the steps in this book step by step, they can quickly get started with the deep learning framework PyTorch.


This book is most suitable as an introductory book for students and technicians who want to understand deep learning and quickly get started using deep learning frameworks. It provides source code and data for all real cases.

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