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
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.
評分
評分
評分
評分
書不錯,由淺入深的介紹瞭PyTorch,書裏麵有很多的例子可以學習。
评分入門級,畫得很用心,但是感覺讀起來有點費勁
评分最為一個萌新,並沒有感受到有大傢所稱贊的那樣好……
评分寫得非常簡明易懂,上手pytorch的不二之選
评分寫得非常簡明易懂,上手pytorch的不二之選
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