Deep Learning With Pytorch Lightning
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Deep Learning with PyTorch Lightning
Author: Kunal Sawarkar
language: en
Publisher: Packt Publishing Ltd
Release Date: 2022-04-29
Build, train, deploy, and scale deep learning models quickly and accurately, improving your productivity using the lightweight PyTorch Wrapper Key FeaturesBecome well-versed with PyTorch Lightning architecture and learn how it can be implemented in various industry domainsSpeed up your research using PyTorch Lightning by creating new loss functions, networks, and architecturesTrain and build new algorithms for massive data using distributed trainingBook Description PyTorch Lightning lets researchers build their own Deep Learning (DL) models without having to worry about the boilerplate. With the help of this book, you'll be able to maximize productivity for DL projects while ensuring full flexibility from model formulation through to implementation. You'll take a hands-on approach to implementing PyTorch Lightning models to get up to speed in no time. You'll start by learning how to configure PyTorch Lightning on a cloud platform, understand the architectural components, and explore how they are configured to build various industry solutions. Next, you'll build a network and application from scratch and see how you can expand it based on your specific needs, beyond what the framework can provide. The book also demonstrates how to implement out-of-box capabilities to build and train Self-Supervised Learning, semi-supervised learning, and time series models using PyTorch Lightning. As you advance, you'll discover how generative adversarial networks (GANs) work. Finally, you'll work with deployment-ready applications, focusing on faster performance and scaling, model scoring on massive volumes of data, and model debugging. By the end of this PyTorch book, you'll have developed the knowledge and skills necessary to build and deploy your own scalable DL applications using PyTorch Lightning. What you will learnCustomize models that are built for different datasets, model architectures, and optimizersUnderstand how a variety of Deep Learning models from image recognition and time series to GANs, semi-supervised and self-supervised models can be builtUse out-of-the-box model architectures and pre-trained models using transfer learningRun and tune DL models in a multi-GPU environment using mixed-mode precisionsExplore techniques for model scoring on massive workloadsDiscover troubleshooting techniques while debugging DL modelsWho this book is for This deep learning book is for citizen data scientists and expert data scientists transitioning from other frameworks to PyTorch Lightning. This book will also be useful for deep learning researchers who are just getting started with coding for deep learning models using PyTorch Lightning. Working knowledge of Python programming and an intermediate-level understanding of statistics and deep learning fundamentals is expected.
Machine Learning with PyTorch and Scikit-Learn
Author: Sebastian Raschka
language: en
Publisher: Packt Publishing Ltd
Release Date: 2022-02-25
This book of the bestselling and widely acclaimed Python Machine Learning series is a comprehensive guide to machine and deep learning using PyTorch s simple to code framework. Purchase of the print or Kindle book includes a free eBook in PDF format. Key Features Learn applied machine learning with a solid foundation in theory Clear, intuitive explanations take you deep into the theory and practice of Python machine learning Fully updated and expanded to cover PyTorch, transformers, XGBoost, graph neural networks, and best practices Book DescriptionMachine Learning with PyTorch and Scikit-Learn is a comprehensive guide to machine learning and deep learning with PyTorch. It acts as both a step-by-step tutorial and a reference you'll keep coming back to as you build your machine learning systems. Packed with clear explanations, visualizations, and examples, the book covers all the essential machine learning techniques in depth. While some books teach you only to follow instructions, with this machine learning book, we teach the principles allowing you to build models and applications for yourself. Why PyTorch? PyTorch is the Pythonic way to learn machine learning, making it easier to learn and simpler to code with. This book explains the essential parts of PyTorch and how to create models using popular libraries, such as PyTorch Lightning and PyTorch Geometric. You will also learn about generative adversarial networks (GANs) for generating new data and training intelligent agents with reinforcement learning. Finally, this new edition is expanded to cover the latest trends in deep learning, including graph neural networks and large-scale transformers used for natural language processing (NLP). This PyTorch book is your companion to machine learning with Python, whether you're a Python developer new to machine learning or want to deepen your knowledge of the latest developments.What you will learn Explore frameworks, models, and techniques for machines to learn from data Use scikit-learn for machine learning and PyTorch for deep learning Train machine learning classifiers on images, text, and more Build and train neural networks, transformers, and boosting algorithms Discover best practices for evaluating and tuning models Predict continuous target outcomes using regression analysis Dig deeper into textual and social media data using sentiment analysis Who this book is for If you have a good grasp of Python basics and want to start learning about machine learning and deep learning, then this is the book for you. This is an essential resource written for developers and data scientists who want to create practical machine learning and deep learning applications using scikit-learn and PyTorch. Before you get started with this book, you’ll need a good understanding of calculus, as well as linear algebra.
Machine Learning and Immersive Technologies for User-centered Digital Healthcare Innovation
Author: Federico Colecchia
language: en
Publisher: Frontiers Media SA
Release Date: 2025-06-09
Emerging technologies such as machine learning and immersive technologies (including virtual reality and augmented reality) hold great potential for driving disruptive healthcare innovation. However, the adoption of digital technology in healthcare, including use of data-driven tools in support of clinical decision-making and patient-facing applications relying on consumer electronic devices, is often hindered by issues of user experience, trust, equitability, and fairness. There is increasing recognition of a need to facilitate further convergence between the development of emerging technologies and user-centered design research for healthcare, with a view to achieving a positive impact on patients, care professionals, and the healthcare system. This article collection addresses current development trends relating to user-centered digital healthcare innovation based on machine learning and immersive technologies, in order to identify opportunities associated with the deployment of new solutions in a range of environments – including clinical, domestic, and educational settings – and barriers to the adoption of technology by end users. A key aim is to identify opportunities for strengthening interdisciplinary collaboration as well as methods of lowering barriers and overcoming obstacles for the benefit of patients, care professionals, and the healthcare system. Examples of potential outcomes are effective design and use of solutions based on machine learning and immersive technologies to improve user experience, strategies to facilitate ethical development of digital technology for healthcare, and methods of encouraging adoption of advanced tools developed in line with principles of equitability and fairness. Articles should address issues of user-centered digital healthcare innovation driven by machine learning and immersive technologies. Submissions should ideally be positioned at the intersection of digital technology development with user-centered design, although contributions more technical in nature as well as user experience studies are also welcome. A non-exhaustive list of suitable topics and manuscript types is given below: • Machine learning and/or immersive technologies (including augmented reality and virtual reality) for user-centered digital healthcare. • Clinical decision support systems. • Patient-facing applications. • Tools for education and training of future medical professionals. • Potential barriers to adoption of technology: issues of user experience, trust, equitability, and fairness in digital healthcare. • Reviews and contributions discussing the development of intuitive, accessible, and inclusive digital interfaces. • All aspects of healthcare that are being or have the potential to be impacted by machine learning and immersive technologies.