Books [Eli Stevens, Luca Antiga, and Thomas Viehmann] Deep Learning with PyTorch

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DESCRIPTION:

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.

This book 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.

• 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

About the Authors:

• Eli Stevens has worked at startups in Silicon Valley, with roles ranging from software engineer to CTO.
• Luca Antiga worked as a researcher (biomedical engineering), a cofounder and CTO of an AI company.
• Thomas Viehmann is a machine learning and PyTorch trainer, consultant, and a PyTorch core developer.

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