Bộ sách luyện thi ETS 2023 LC&RC
199,000₫
299,000₫
Sách PyTorch Recipes A Problem-Solution Approach to Build, Train and Deploy Neural Network Models, 2nd Edition (sách keo gáy, bìa mềm)
Thể loại:Computers - Artificial Intelligence (AI)
Năm:2023
In lần thứ:2nd
Ngôn ngữ:english
Trang:282
Learn how to use PyTorch to build neural network models using code
snippets updated for this second edition. This book includes new
chapters covering topics such as distributed PyTorch modeling, deploying
PyTorch models in production, and developments around PyTorch with
updated code. You'll start by learning how to use tensors to develop and
fine-tune neural network models and implement deep learning models such
as LSTMs, and RNNs. Next, you'll explore probability distribution
concepts using PyTorch, as well as supervised and unsupervised
algorithms with PyTorch. This is followed by a deep dive on building
models with convolutional neural networks, deep neural networks, and
recurrent neural networks using PyTorch. This new edition covers also
topics such as Scorch, a compatible module equivalent to the Scikit
machine learning library, model quantization to reduce parameter size,
and preparing a model for deployment within a production system.
Distributed parallel processing for balancing PyTorch workloads, using
PyTorch for image processing, audio analysis, and model interpretation
are also covered in detail. Each chapter includes recipe code snippets
to perform specific activities. By the end of this book, you will be
able to confidently build neural network models using PyTorch. What You
Will Learn Utilize new code snippets and models to train machine
learning models using PyTorch Train deep learning models with fewer and
smarter implementations Explore the PyTorch framework for model
explainability and to bring transparency to model interpretation Build,
train, and deploy neural network models designed to scale with PyTorch
Understand best practices for evaluating and fine-tuning models using
PyTorch Use advanced torch features in training deep neural networks
Explore various neural network models using PyTorch Discover functions
compatible with sci-kit learn compatible models Perform distributed
PyTorch training and execution Who This Book Is ForMachine learning
engineers, data scientists and Python programmers and software
developers interested in learning the PyTorch framework.
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