Deep Learning with PyTorch, Second Edition, MEAP, Version 5, Antiqa L., Stevens E., Huang H., Viehmann T., 2024

Deep Learning with PyTorch, Second Edition, MEAP, Version 5, Antiqa L., Stevens E., Huang H., Viehmann T., 2024.
     
   The book is written for developers, students, or even hobbyists who have some prior experience with the Python programming language and want to gain a better understanding of deep learning. There is no better time to learn about deep learning than now as artificial intelligence gains an evergrowing significance in shaping our products and the world around us.




PyTorch for deep learning.
PyTorch is a Python library that facilitates building deep learning projects. It emphasizes flexibility and allows deep learning models to be expressed in idiomatic Python. This approachability and ease of use found early adopters in the research community, and in the years since its first release, it has grown into one of the most prominent deep learning tools across a broad range of applications.

As Python does for programming, PyTorch provides an excellent introduction to deep learning. At the same time, PyTorch has been proven to be fully qualified for use in professional contexts for real-world, high-profile work. We believe that PyTorch's clear syntax, streamlined API, and easy debugging make it an excellent choice for introducing deep learning. We highly recommend studying PyTorch for your first deep learning library. Whether it ought to be the last deep learning library you learn is a decision we leave up to you.

At its core, the deep learning machine in figure 1.1 is a rather complex mathematical function mapping inputs to an output. To facilitate expressing this function, PyTorch provides a core data structure, the tensor, which is a multidimensional array that shares many similarities with NumPy arrays. Around that foundation, PyTorch comes with features to perform accelerated mathematical operations on dedicated hardware, which makes it convenient to design neural network architectures and train them on individual machines or parallel computing resources.

CONTENTS.
PART 1: CORE PYTORCH.
1 Introducing deep learning and the PyTorch Library.
2 Pretrained networks.
3 It starts with a tensor.
4 Real-world data representation using tensors.
5 The mechanics of learning.
6 Using a neural network to fit the data.
7 Telling birds from airplanes: Learning from images.
8 Using convolutions to generalize.
PART 2: CASE STUDIES AND PRACTICAL APPLICATIONS.
9 Introducing generative models.
10 Vision transformers and diffusion models.
11 Using PyTorch to fight cancer.
12 Combining data sources into a unified dataset.
13 Training a classification model to detect suspected tumors.
14 Improving training with metric and augmentation.
15 Using segmentation to find suspected nodules.
16 End-to-end nodule analysis, and where to go next.
PART 3: SHIP IT! PYTORCH IN PRODUCTION.
17 Optimizations to supercharge your PyTorch models.
18 Deploying with PyTorch.



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Теги: учебник по программированию :: программирование :: Antiqa :: Stevens :: Huang :: Viehmann