Deep Learning with PyTorch

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 networksBest practices for the PyTorch Tensor API, loading data in Python, and visualizing resultsImplementing modules and loss functionsUtilizing pretrained models from PyTorch HubMethods for training networks with limited inputsSifting through unreliable results to diagnose and fix problems in your neural networkImprove your results with augmented data, better model architecture, and fine tuning