Deep Learning with PyTorch pdf epub mobi txt 電子書下載2025

☆☆☆☆☆

Eli Stevens has worked in Silicon Valley for the past 15 years as a software engineer, and the past 7 years as Chief Technical Officer of a startup making medical device software.

Luca Antiga is co-founder and CEO of an AI engineering company located in Bergamo, Italy, and a regular contributor to PyTorch.

出版者:Manning Publications

作者:Eli Stevens

出品人:

頁數:450

译者:

出版時間:2020-6-9

價格:USD 49.99

裝幀:Paperback

isbn號碼:9781617295263

叢書系列:

圖書標籤:

機器學習
深度學習
PyTorch
計算機科學
deep-learning
2020
人工智能
計算機

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Deep Learning with PyTorch teaches you how to implement deep learning algorithms with Python and PyTorch. This book takes you into a fascinating case study: building an algorithm capable of detecting malignant lung tumors using CT scans. As the authors guide you through this real example, you'll discover just how effective and fun PyTorch can be. After a quick introduction to the deep learning landscape, you'll explore the use of pre-trained networks and start sharpening your skills on working with tensors. You'll find out how to represent the most common types of data with tensors and how to build and train neural networks from scratch on practical examples, focusing on images and sequences.

After covering the basics, the book will take you on a journey through larger projects. The centerpiece of the book is a neural network designed for cancer detection. You'll discover ways for training networks with limited inputs and start processing data to get some results. You'll sift through the unreliable initial results and focus on how to diagnose and fix the problems in your neural network. Finally, you'll look at ways to improve your results by training with augmented data, make improvements to the model architecture, and perform other fine tuning.

what's inside

Using the PyTorch tensor API

Understanding automatic differentiation in PyTorch

Training deep neural networks

Monitoring training and visualizing results

Implementing modules and loss functions

Loading data in Python for PyTorch

Interoperability with NumPy

Deploying a PyTorch model for inference