PyTorch is an open source machine learning framework that provides support for dynamic calculation graphs, allowing users to customize and train their own neural networks. It is currently one of the most popular frameworks in the field of machine learning. Based on PyTorch 2.0, This book introduces the basic theory, algorithms and application cases of deep learning in detail, and is accompanied by sample source code and PPT courseware.
"Learning PyTorch 2.0 Deep Learning from Scratch" is divided into 15 chapters, including PyTorch overview, development environment construction, practical MNIST classification based on PyTorch, theoretical basis of deep learning, practical MNIST classification, data processing and model visualization, practical classification based on PyTorch convolutional layer, PyTorch data processing and model visualization, practical ResNet convolution network model, and interesting Word Embedding, Chinese emotion classification practice based on recurrent neural network, natural language processing encoder, pre-trained model BERT standing on the shoulders of giants, natural language processing decoder, PyTorch-based reinforcement learning practice, MFCC-based voice wake-up practice, and PyTorch-based face recognition practice.
"Learning PyTorch 2.0 Deep Learning from Scratch" is suitable for deep learning beginners, PyTorch beginners, and PyTorch deep learning project developers. It can also be used as a teaching material for computer technology, artificial intelligence, intelligent science and technology, data science and big data technology and other related majors in colleges and universities or vocational colleges.
Reader comments
I particularly like the cases in the book, such as practical classification based on PyTorch convolutional layers and encoders and decoders for natural language processing. These cases gave me a more intuitive understanding of the application of deep learning. Moreover, the content in the book is detailed and easy to understand, and the author's explanations are also very clear, making it easy for a novice like me to get started. All in all, highly recommended!
I particularly like the cases in the book, such as practical classification based on PyTorch convolutional layers and encoders and decoders for natural language processing. These cases gave me a more intuitive understanding of the application of deep learning. Moreover, the content in the book is detailed and easy to understand, and the author's explanations are also very clear, making it easy for a novice like me to get started. All in all, highly recommended!