Here, I’m going to introduce a very, VERY, VERYYYY GOOOOOD deep learning book with PyTorch, absolutely free which worth $49.99 (printed copy) and $39.99 (digital copy) at Manning Publications!
Before telling you the book title, let’s see what the Cocreator of PyTorch, Soumith Chintala said about the book.
With this publication, we finally have a definitive treatise on PyTorch. It covers the basics and abstractions in great detail.
From the Foreword by Soumith Chintala, Cocreator of PyTorch
Impressive?!?!
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. 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.
Roadmap
Deep Learning with PyTorch is organized into three distinct parts. Part 1 covers the founda- tions, while part 2 walks you through an end-to-end project, building on the basic con- cepts introduced in part 1 and adding more advanced ones. The short part 3 rounds off the book with a tour of what PyTorch offers for deployment.
Part 1:
Chapter 1 introduces PyTorch as a library and its place in the deep learning revolution, and touches on what sets PyTorch apart from other deep learning frameworks.
Chapter 2 shows PyTorch in action by running examples of pretrained networks; it demonstrates how to download and run models in PyTorch Hub.
Chapter 3 introduces the basic building block of PyTorch — the tensor — showing its API and going behind the scenes with some implementation details.
Chapter 4 demonstrates how different kinds of data can be represented as tensors and how deep learning models expects tensors to be shaped.
Chapter 5 walks through the mechanics of learning through gradient descent and how PyTorch enables it with automatic differentiation.
Chapter 6 shows the process of building and training a neural network for regression in PyTorch using the nn and optim modules.
Chapter 7 builds on the previous chapter to create a fully connected model for image classification and expand the knowledge of the PyTorch API.
Chapter 8 introduces convolutional neural networks and touches on more advanced concepts for building neural network models and their PyTorch implementation.
Part 2:
Chapter 9 describes the end-to-end strategy we’ll use for lung tumor classification, starting from computed tomography (CT) imaging.
Chapter 10 loads the human annotation data along with the images from CT scans and converts the relevant information into tensors, using standard PyTorch APIs.
Chapter 11 introduces the first classification model that consumes the training data introduced in chapter 10. We train the model and collect basic performance metrics. We also introduce using TensorBoard to monitor training.
Chapter 12 explores and implements standard performance metrics and uses those metrics to identify weaknesses in the training done previously. We then mitigate those flaws with an improved training set that uses data balancing and augmentation.
Chapter 13 describes segmentation, a pixel-to-pixel model architecture that we use to produce a heatmap of possible nodule locations that covers the entire CT scan. This heatmap can be used to find nodules on CT scans for which we do not have human-annotated data.
Chapter 14 implements the final end-to-end project: diagnosis of cancer patients using our new segmentation model followed by classification.
Part 3:
Chapter 15 provides an overview of how to deploy PyTorch models to a simple web service, embed them in a C++ program, or bring them to a mobile phone.
A sneak peek of the graphic in the book
Finally
You can download the book here!
Enjoy Reading ❤️
If you love AI and research, take a look at my YouTube Channel: AI Pursuit!
Last, but not least, there is an interview with the authors:
Don’t forget to share with your friends!
