Deep Learning with PyTorch 在线电子书 图书标签: 机器学习 深度学习 PyTorch 计算机科学 deep-learning 2020 人工智能 计算机
发表于2024-12-23
Deep Learning with PyTorch 在线电子书 pdf 下载 txt下载 epub 下载 mobi 下载 2024
入门级,画得很用心,但是感觉读起来有点费劲
评分写得非常简明易懂,上手pytorch的不二之选
评分作为入门OK。但对于原理的深度阐述,还是有所欠缺。
评分一本入门科普书。
评分介绍了pytorch的各种基本概念(主要是tensor和autograde),介绍了深度学习的一些概念和框架。作为快速入门pytorch的简介来说很合适,但不适于进一步上手(竟然没有dataset的构建_(:з」∠)_)。 最后,人生苦短,拒绝tensorflow,请用pytorch嗯
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.
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
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Deep Learning with PyTorch 在线电子书 pdf 下载 txt下载 epub 下载 mobi 下载 2024