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

  • 15h 26m 18s
  • Eli Stevens, Luca Antiga, Manning Publications, Mark Thomas, Thomas Viehmann
  • Manning Publications
  • 2021

There are countless ways to put deep learning to good use: improved medical imaging, credit card fraud detection, long range weather forecasting. PyTorch puts these superpowers in your hands, providing a comfortable Python experience that gets you started quickly and then grows with you as you - and your deep-learning skills - become more sophisticated.

This book will make that journey engaging and fun.

About the technology

Although many deep learning tools use Python, the PyTorch library is truly Pythonic. Instantly familiar to anyone who knows PyData tools like NumPy and scikit-learn, PyTorch simplifies deep learning without sacrificing advanced features. It's excellent for building quick models, and it scales smoothly from laptop to enterprise. Because companies like Apple, Facebook, and JPMorgan Chase rely on PyTorch, it's a great skill to have as you expand your career options.

About the book

Deep Learning with PyTorch teaches you to create neural networks and deep learning systems with PyTorch. This practical book quickly gets you to work building a real-world example from scratch: a tumor image classifier. Along the way, it covers best practices for the entire DL pipeline, including the PyTorch Tensor API, loading data in Python, monitoring training, and visualizing results.

After covering the basics, the book will take you on a journey through larger projects.

What's inside

  • Training deep neural networks
  • Implementing modules and loss functions
  • Utilizing pretrained models from PyTorch Hub
  • Exploring code samples in Jupyter Notebooks

In this Audiobook

  • Introduction
  • Chapter 1 - Introducing deep learning and the PyTorch Library
  • Chapter 2 - Pretrained networks
  • Chapter 3 - It Starts With A Tensor
  • Chapter 4 - Real-world data representation using tensors
  • Chapter 5 - The Mechanics Of Learning
  • Chapter 6 - Using a Neural Network To Fit The Data
  • Chapter 7 - Telling Birds From Airplanes: Learning From Images
  • Chapter 8 - Using convolutions to generalize
  • Chapter 9 - Using PyTorch to fight cancer
  • Chapter 10 - Combining data sources into a unified dataset
  • Chapter 11 - Training a classification model to detect suspected tumors
  • Chapter 12 - Improving training with metrics and augmentation
  • Chapter 13 - Using segmenatation to find suspected nodules
  • Chapter 14 - End-to-end nodule analysis, and where to go next
  • Chapter 15 - Deploying to production