Deep Learning with PyTorch MEAP 1st edition by Luca Pietro Giovanni Antiga, Eli Stevens, Thomas Viehmann ISBN 1638354073 9781638354079

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ISBN 10: 1638354073
ISBN 13: 9781638354079
Author: Luca Pietro Giovanni Antiga, Eli Stevens, Thomas Viehmann

“We finally have the definitive treatise on PyTorch! It covers the basics and abstractions in great detail. I hope this book becomes your extended reference document.” —Soumith Chintala, co-creator of PyTorch 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 Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications. 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 This Book Is Written For For Python programmers with an interest in machine learning. No experience with PyTorch or other deep learning frameworks is required. About The Authors 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. Table of Contents PART 1 – CORE PYTORCH 1 Introducing deep learning and the PyTorch Library 2 Pretrained networks 3 It starts with a tensor 4 Real-world data representation using tensors 5 The mechanics of learning 6 Using a neural network to fit the data 7 Telling birds from airplanes: Learning from images 8 Using convolutions to generalize PART 2 – LEARNING FROM IMAGES IN THE REAL WORLD: EARLY DETECTION OF LUNG CANCER 9 Using PyTorch to fight cancer 10 Combining data sources into a unified dataset 11 Training a classification model to detect suspected tumors 12 Improving training with metrics and augmentation 13 Using segmentation to find suspected nodules 14 End-to-end nodule analysis, and where to go next PART 3 – DEPLOYMENT 15 Deploying to production            

Deep Learning with PyTorch MEAP 1st Table of contents:

Part 1: Core PyTorch

1. Introducing Deep Learning and the PyTorch Library

   • Explains the fundamentals of deep learning and why PyTorch is a preferred tool for building deep learning models.

   • Discusses PyTorch’s key features and the competitive landscape in deep learning tools.

2. Pretrained Networks

   • Details the use of pretrained models like AlexNet, ResNet, and CycleGAN for various tasks (image recognition, image-to-image translation, etc.).

   • Explains how pretrained models can be utilized for tasks like scene description and how to use frameworks like Torch Hub.

3. It Starts with a Tensor

   • Focuses on PyTorch’s core data structure, the tensor, covering how to create, manipulate, and perform operations on tensors.

4. Real-World Data Representation Using Tensors

   • Discusses how to handle different types of data, like images, time series, tabular data, and text, in PyTorch using tensors.

5. The Mechanics of Learning

   • Describes the process of training a deep learning model, focusing on loss functions, backpropagation, and gradient descent.

   • Introduces PyTorch’s autograd feature for automatic differentiation.

6. Using a Neural Network to Fit the Data

   • Covers how to create and train a simple neural network using PyTorch’s nn.Module and explore activation functions, error functions, and network parameters.

7. Telling Birds from Airplanes: Learning from Images

   • Focuses on using deep learning for image classification, including working with datasets like CIFAR-10 and building neural network models to classify images.

8. Using Convolutions to Generalize

   • Introduces convolutional neural networks (CNNs), including how convolutions work, and guides you through creating a CNN using PyTorch to solve image classification problems.

Part 2: Learning from Images in the Real World: Early Detection of Lung Cancer

9. Using PyTorch to Fight Cancer

   • Explores how deep learning can be used in healthcare, specifically for lung cancer detection using CT scans.

   • Covers the preparation and challenges associated with large-scale projects, like working with the LUNA Grand Challenge dataset.

10. Combining Data Sources into a Unified Dataset

    • Walks you through processing CT scan data, extracting relevant features, and preparing the data for training a model.

11. Training a Classification Model to Detect Suspected Tumors

    • Focuses on building and training a neural network for detecting lung cancer in CT scans, with a focus on model design, training loops, and validation.

12. Improving Training with Metrics and Augmentation

    • Discusses improving model performance through metrics like precision, recall, and the F1 score, as well as using data augmentation to reduce overfitting.

13. Using Segmentation to Find Suspected Nodules

    • Explains how to apply image segmentation techniques to detect suspected nodules in CT scans, and uses architectures like U-Net for per-pixel classification.

14. End-to-End Nodule Analysis, and Where to Go Next

    • Details the process of building a full pipeline for lung cancer detection, from segmentation to classification and evaluation, and explores next steps for improving the model.

Part 3: Deployment

15. Deploying to Production

    • Discusses how to deploy PyTorch models in real-world applications, including serving models via Flask, exporting models with ONNX or TorchScript, and deploying on mobile or C++ environments.

    • Provides insights into optimizing models for efficiency and integrating with enterprise systems.

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Tags: Luca Pietro Giovanni Antiga, Eli Stevens, Thomas Viehmann, Deep Learning, PyTorch MEAP