Deep Learning with PyTorch: Build, train, and tune neural networks using Python tools

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Deep Learning with PyTorch: Build, train, and tune neural networks using Python tools: 520 pages

Author : y Eli Stevens , Luca Antiga, Thomas Viehmann

Language : English

1) Book Description

Key Features

Written by PyTorch’s creator and key contributors

Develop deep learning models in a familiar Pythonic way

Use PyTorch to build an image classifier for cancer detection

Diagnose problems with your neural network and improve training with data augmentation

About The Book

Every other day we hear about new ways to put deep learning to good use: improved medical imaging, accurate credit card fraud detection, long range weather forecasting, and more.

PyTorch puts these superpowers in your hands. Instantly familiar to anyone who knows Python data tools like NumPy and Scikit-learn, PyTorch simplifies deep learning without sacrificing advanced features. It’s great for building quick models, and it scales smoothly from laptop to enterprise.

Deep Learning with PyTorch teaches you to create deep learning and neural network systems with PyTorch. This practical book gets you to work right away building a tumor image classifier from scratch. After covering the basics, you’ll learn best practices for the entire deep learning pipeline, tackling advanced projects as your PyTorch skills become more sophisticated. All code samples are easy to explore in downloadable Jupyter notebooks.

What You Will Learn

Understanding deep learning data structures such as tensors and neural networks

Best practices for the PyTorch Tensor API, loading data in Python, and visualizing results

Implementing modules and loss functions

Utilizing pretrained models from PyTorch Hub

Methods for training networks with limited inputs

Sifting through unreliable results to diagnose and fix problems in your neural network

Improve your results with augmented data, better model architecture, and fine tuning

2) About Author

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. Thomas Viehmann is a Machine Learning and PyTorch speciality trainer and consultant based in Munich, Germany and a PyTorch core developer.