Applied Deep Learning With Pytorch

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Applied Deep Learning With Pytorch

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Author : Hyatt Saleh
language : en
Publisher:
Release Date : 2019-04-26

Applied Deep Learning With Pytorch written by Hyatt Saleh and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019-04-26 with Computers categories.

Implement techniques such as image classification and natural language processing (NLP) by understanding the different neural network architectures Key Features Understand deep learning and how it can solve complex real-world problems Apply deep learning for image classification and text processing using neural networks Develop deep learning solutions for tasks such as basic classification and solving style transfer problems Book Description Machine learning is rapidly becoming the most preferred way of solving data problems, thanks to the huge variety of mathematical algorithms that find patterns, which are otherwise invisible to us. Applied Deep Learning with PyTorch takes your understanding of deep learning, its algorithms, and its applications to a higher level. The book begins by helping you browse through the basics of deep learning and PyTorch. Once you are well versed with the PyTorch syntax and capable of building a single-layer neural network, you will gradually learn to tackle more complex data problems by configuring and training a convolutional neural network (CNN) to perform image classification. As you progress through the chapters, you'll discover how you can solve an NLP problem by implementing a recurrent neural network (RNN). By the end of this book, you'll be able to apply the skills and confidence you've gathered along your learning process to use PyTorch for building deep learning solutions that can solve your business data problems. What you will learn Detect a variety of data problems to which you can apply deep learning solutions Learn the PyTorch syntax and build a single-layer neural network with it Build a deep neural network to solve a classification problem Develop a style transfer model Implement data augmentation and retrain your model Build a system for text processing using a recurrent neural network Who this book is for Applied Deep Learning with PyTorch is designed for data scientists, data analysts, and developers who want to work with data using deep learning techniques. Anyone looking to explore and implement advanced algorithms with PyTorch will also find this book useful. Some working knowledge of Python and familiarity with the basics of machine learning are a must. However, knowledge of NumPy and pandas will be beneficial, but not essential.

Machine Learning With Pytorch And Scikit Learn

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Author : Sebastian Raschka
language : en
Publisher: Packt Publishing Ltd
Release Date : 2022-02-25

Machine Learning With Pytorch And Scikit Learn written by Sebastian Raschka and has been published by Packt Publishing Ltd this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022-02-25 with Computers categories.

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.

Deep Learning With Pytorch Lightning

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Author : Kunal Sawarkar
language : en
Publisher: Packt Publishing Ltd
Release Date : 2022-04-29

Deep Learning With Pytorch Lightning written by Kunal Sawarkar and has been published by Packt Publishing Ltd this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022-04-29 with Computers categories.

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.

Mastering Pytorch

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Author : Ashish Ranjan Jha
language : en
Publisher: Packt Publishing Ltd
Release Date : 2024-05-31

Mastering Pytorch written by Ashish Ranjan Jha and has been published by Packt Publishing Ltd this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-05-31 with Computers categories.

Master advanced techniques and algorithms for machine learning with PyTorch using real-world examples Updated for PyTorch 2.x, including integration with Hugging Face, mobile deployment, diffusion models, and graph neural networks Get With Your Book: PDF Copy, AI Assistant, and Next-Gen Reader Free Key Features Understand how to use PyTorch to build advanced neural network models Get the best from PyTorch by working with Hugging Face, fastai, PyTorch Lightning, PyTorch Geometric, Flask, and Docker Unlock faster training with multiple GPUs and optimize model deployment using efficient inference frameworks Book DescriptionPyTorch is making it easier than ever before for anyone to build deep learning applications. This PyTorch deep learning book will help you uncover expert techniques to get the most out of your data and build complex neural network models. You’ll build convolutional neural networks for image classification and recurrent neural networks and transformers for sentiment analysis. As you advance, you'll apply deep learning across different domains, such as music, text, and image generation, using generative models, including diffusion models. You'll not only build and train your own deep reinforcement learning models in PyTorch but also learn to optimize model training using multiple CPUs, GPUs, and mixed-precision training. You’ll deploy PyTorch models to production, including mobile devices. Finally, you’ll discover the PyTorch ecosystem and its rich set of libraries. These libraries will add another set of tools to your deep learning toolbelt, teaching you how to use fastai to prototype models and PyTorch Lightning to train models. You’ll discover libraries for AutoML and explainable AI (XAI), create recommendation systems, and build language and vision transformers with Hugging Face. By the end of this book, you'll be able to perform complex deep learning tasks using PyTorch to build smart artificial intelligence models.What you will learn Implement text, vision, and music generation models using PyTorch Build a deep Q-network (DQN) model in PyTorch Deploy PyTorch models on mobile devices (Android and iOS) Become well versed in rapid prototyping using PyTorch with fastai Perform neural architecture search effectively using AutoML Easily interpret machine learning models using Captum Design ResNets, LSTMs, and graph neural networks (GNNs) Create language and vision transformer models using Hugging Face Who this book is for This deep learning with PyTorch book is for data scientists, machine learning engineers, machine learning researchers, and deep learning practitioners looking to implement advanced deep learning models using PyTorch. This book is ideal for those looking to switch from TensorFlow to PyTorch. Working knowledge of deep learning with Python is required.

Deep Learning With Python

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Author : Nikhil Ketkar
language : en
Publisher: Apress
Release Date : 2021-04-10

Deep Learning With Python written by Nikhil Ketkar and has been published by Apress this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-04-10 with Computers categories.

Master the practical aspects of implementing deep learning solutions with PyTorch, using a hands-on approach to understanding both theory and practice. This updated edition will prepare you for applying deep learning to real world problems with a sound theoretical foundation and practical know-how with PyTorch, a platform developed by Facebook’s Artificial Intelligence Research Group. You'll start with a perspective on how and why deep learning with PyTorch has emerged as an path-breaking framework with a set of tools and techniques to solve real-world problems. Next, the book will ground you with the mathematical fundamentals of linear algebra, vector calculus, probability and optimization. Having established this foundation, you'll move on to key components and functionality of PyTorch including layers, loss functions and optimization algorithms. You'll also gain an understanding of Graphical Processing Unit (GPU) based computation, which is essential for training deep learning models. All the key architectures in deep learning are covered, including feedforward networks, convolution neural networks, recurrent neural networks, long short-term memory networks, autoencoders and generative adversarial networks. Backed by a number of tricks of the trade for training and optimizing deep learning models, this edition of Deep Learning with Python explains the best practices in taking these models to production with PyTorch. What You'll Learn Review machine learning fundamentals such as overfitting, underfitting, and regularization. Understand deep learning fundamentals such as feed-forward networks, convolution neural networks, recurrent neural networks, automatic differentiation, and stochastic gradient descent. Apply in-depth linear algebra with PyTorch Explore PyTorch fundamentals and its building blocks Work with tuning and optimizing models Who This Book Is For Beginners with a working knowledge of Python who want to understand Deep Learning in a practical, hands-on manner.

Programming Pytorch For Deep Learning

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Author : Ian Pointer
language : en
Publisher: O'Reilly Media
Release Date : 2019-09-20

Programming Pytorch For Deep Learning written by Ian Pointer and has been published by O'Reilly Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019-09-20 with Computers categories.

Take the next steps toward mastering deep learning, the machine learning method that’s transforming the world around us by the second. In this practical book, you’ll get up to speed on key ideas using Facebook’s open source PyTorch framework and gain the latest skills you need to create your very own neural networks. Ian Pointer shows you how to set up PyTorch on a cloud-based environment, then walks you through the creation of neural architectures that facilitate operations on images, sound, text,and more through deep dives into each element. He also covers the critical concepts of applying transfer learning to images, debugging models, and PyTorch in production. Learn how to deploy deep learning models to production Explore PyTorch use cases from several leading companies Learn how to apply transfer learning to images Apply cutting-edge NLP techniques using a model trained on Wikipedia Use PyTorch’s torchaudio library to classify audio data with a convolutional-based model Debug PyTorch models using TensorBoard and flame graphs Deploy PyTorch applications in production in Docker containers and Kubernetes clusters running on Google Cloud

Applied Deep Learning On Graphs

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Author : Lakshya Khandelwal
language : en
Publisher: Packt Publishing Ltd
Release Date : 2024-12-27

Applied Deep Learning On Graphs written by Lakshya Khandelwal and has been published by Packt Publishing Ltd this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-12-27 with Computers categories.

Gain a deep understanding of applied deep learning on graphs from data, algorithm, and engineering viewpoints to construct enterprise-ready solutions using deep learning on graph data for wide range of domains Key Features Explore graph data in real-world systems and leverage graph learning for impactful business results Dive into popular and specialized deep neural architectures like graph convolutional and attention networks Learn how to build scalable and productionizable graph learning solutions Purchase of the print or Kindle book includes a free PDF eBook Book Description With their combined expertise spanning cutting-edge AI product development at industry giants such as Walmart, Adobe, Samsung, and Arista Networks, Lakshya and Subhajoy provide real-world insights into the transformative world of graph neural networks (GNNs). This book demystifies GNNs, guiding you from foundational concepts to advanced techniques and real-world applications. You’ll see how graph data structures power today’s interconnected world, why specialized deep learning approaches are essential, and how to address challenges with existing methods. You’ll start by dissecting early graph representation techniques such as DeepWalk and node2vec. From there, the book takes you through popular GNN architectures, covering graph convolutional and attention networks, autoencoder models, LLMs, and technologies such as retrieval augmented generation on graph data. With a strong theoretical grounding, you’ll seamlessly navigate practical implementations, mastering the critical topics of scalability, interpretability, and application domains such as NLP, recommendations, and computer vision. By the end of this book, you’ll have mastered the underlying ideas and practical coding skills needed to innovate beyond current methods and gained strategic insights into the future of GNN technologies. What you will learn Discover how to extract business value through a graph-centric approach Develop a basic understanding of learning graph attributes using machine learning Identify the limitations of traditional deep learning with graph data and explore specialized graph-based architectures Understand industry applications of graph deep learning, including recommender systems and NLP Identify and overcome challenges in production such as scalability and interpretability Perform node classification and link prediction using PyTorch Geometric Who this book is for For data scientists, machine learning practitioners, researchers delving into graph-based data, and software engineers crafting graph-related applications, this book offers theoretical and practical guidance with real-world examples. A foundational grasp of ML concepts and Python is presumed.

Pytorch Deep Learning Hands On

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Author : Sherin Thomas
language : en
Publisher: Packt Publishing Ltd
Release Date : 2019-04-30

Pytorch Deep Learning Hands On written by Sherin Thomas and has been published by Packt Publishing Ltd this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019-04-30 with Computers categories.

Hands-on projects cover all the key deep learning methods built step-by-step in PyTorch Key FeaturesInternals and principles of PyTorchImplement key deep learning methods in PyTorch: CNNs, GANs, RNNs, reinforcement learning, and moreBuild deep learning workflows and take deep learning models from prototyping to productionBook Description PyTorch Deep Learning Hands-On is a book for engineers who want a fast-paced guide to doing deep learning work with Pytorch. It is not an academic textbook and does not try to teach deep learning principles. The book will help you most if you want to get your hands dirty and put PyTorch to work quickly. PyTorch Deep Learning Hands-On shows how to implement the major deep learning architectures in PyTorch. It covers neural networks, computer vision, CNNs, natural language processing (RNN), GANs, and reinforcement learning. You will also build deep learning workflows with the PyTorch framework, migrate models built in Python to highly efficient TorchScript, and deploy to production using the most sophisticated available tools. Each chapter focuses on a different area of deep learning. Chapters start with a refresher on how the model works, before sharing the code you need to implement them in PyTorch. This book is ideal if you want to rapidly add PyTorch to your deep learning toolset. What you will learn Use PyTorch to build: Simple Neural Networks – build neural networks the PyTorch way, with high-level functions, optimizers, and moreConvolutional Neural Networks – create advanced computer vision systemsRecurrent Neural Networks – work with sequential data such as natural language and audioGenerative Adversarial Networks – create new content with models including SimpleGAN and CycleGANReinforcement Learning – develop systems that can solve complex problems such as driving or game playingDeep Learning workflows – move effectively from ideation to production with proper deep learning workflow using PyTorch and its utility packagesProduction-ready models – package your models for high-performance production environmentsWho this book is for Machine learning engineers who want to put PyTorch to work.

Applied Machine Learning And Deep Learning Architectures And Techniques

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Author : Nitin Liladhar Rane
language : en
Publisher: Deep Science Publishing
Release Date : 2024-10-13

Applied Machine Learning And Deep Learning Architectures And Techniques written by Nitin Liladhar Rane and has been published by Deep Science Publishing this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-10-13 with Computers categories.

This book provides an extensive overview of recent advances in machine learning (ML) and deep learning (DL). It starts with a comprehensive introduction to the latest architectural and design practices, with an overview of basic techniques and optimization algorithms and methodologies that are fundamental to modern ML/DL development followed by the tools and frameworks that are driving innovation in ML/DL. The presentation then points to the central position of ML and DL in developing generative AI like ChatGPT. Then look at different industrial applications such as explaining the real-world impacts of each. This includes challenges around corroborate artificial Intelligence (AI), and trustworthy AI, and so on. Finally, the book presents a futuristic vision on the potentials and implications of future ML and DL architectures, making it an ideal guide for researchers, practitioners and industry professionals. This book will be a significant resource for comprehending present advancements, addressing encounter challenges, and traversing the ML and DL landscape in future, making it an indispensable reference for anyone interested in applying these technologies across sectors.

Deep Learning With Pytorch 1 X

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Author : Laura Mitchell
language : en
Publisher:
Release Date : 2019-11-29

Deep Learning With Pytorch 1 X written by Laura Mitchell and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019-11-29 with Computers categories.

Build and train neural network models with high speed and flexibility in text, vision, and advanced analytics using PyTorch 1.x Key Features Gain a thorough understanding of the PyTorch framework and learn to implement neural network architectures Understand GPU computing to perform heavy deep learning computations using Python Apply cutting-edge natural language processing (NLP) techniques to solve problems with textual data Book Description PyTorch is gaining the attention of deep learning researchers and data science professionals due to its accessibility and efficiency, along with the fact that it's more native to the Python way of development. This book will get you up and running with this cutting-edge deep learning library, effectively guiding you through implementing deep learning concepts. In this second edition, you'll learn the fundamental aspects that power modern deep learning, and explore the new features of the PyTorch 1.x library. You'll understand how to solve real-world problems using CNNs, RNNs, and LSTMs, along with discovering state-of-the-art modern deep learning architectures, such as ResNet, DenseNet, and Inception. You'll then focus on applying neural networks to domains such as computer vision and NLP. Later chapters will demonstrate how to build, train, and scale a model with PyTorch and also cover complex neural networks such as GANs and autoencoders for producing text and images. In addition to this, you'll explore GPU computing and how it can be used to perform heavy computations. Finally, you'll learn how to work with deep learning-based architectures for transfer learning and reinforcement learning problems. By the end of this book, you'll be able to confidently and easily implement deep learning applications in PyTorch. What you will learn Build text classification and language modeling systems using neural networks Implement transfer learning using advanced CNN architectures Use deep reinforcement learning techniques to solve optimization problems in PyTorch Mix multiple models for a powerful ensemble model Build image classifiers by implementing CNN architectures using PyTorch Get up to speed with reinforcement learning, GANs, LSTMs, and RNNs with real-world examples Who this book is for This book is for data scientists and machine learning engineers looking to work with deep learning algorithms using PyTorch 1.x. You will also find this book useful if you want to migrate to PyTorch 1.x. Working knowledge of Python programming and some understanding of machine learning will be helpful.