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Deep Learning With Pytorch

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Deep Learning with PyTorch

Deep Learning with PyTorch Book
Author : Luca Pietro Giovanni Antiga,Eli Stevens,Thomas Viehmann
Publisher : Simon and Schuster
Release : 2020-07-01
ISBN : 1638354073
Language : En, Es, Fr & De

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Book Description :

“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

Deep Learning with PyTorch Book
Author : Vishnu Subramanian
Publisher : Packt Publishing Ltd
Release : 2018-02-23
ISBN : 1788626079
Language : En, Es, Fr & De

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Book Description :

Build neural network models in text, vision and advanced analytics using PyTorch Key Features Learn PyTorch for implementing cutting-edge deep learning algorithms. Train your neural networks for higher speed and flexibility and learn how to implement them in various scenarios; Cover various advanced neural network architecture such as ResNet, Inception, DenseNet and more with practical examples; Book Description Deep learning powers the most intelligent systems in the world, such as Google Voice, Siri, and Alexa. Advancements in powerful hardware, such as GPUs, software frameworks such as PyTorch, Keras, Tensorflow, and CNTK along with the availability of big data have made it easier to implement solutions to problems in the areas of text, vision, and advanced analytics. This book will get you up and running with one of the most cutting-edge deep learning libraries—PyTorch. PyTorch is grabbing the attention of deep learning researchers and data science professionals due to its accessibility, efficiency and being more native to Python way of development. You'll start off by installing PyTorch, then quickly move on to learn various fundamental blocks that power modern deep learning. You will also learn how to use CNN, RNN, LSTM and other networks to solve real-world problems. This book explains the concepts of various state-of-the-art deep learning architectures, such as ResNet, DenseNet, Inception, and Seq2Seq, without diving deep into the math behind them. You will also learn about GPU computing during the course of the book. You will see how to train a model with PyTorch and dive into complex neural networks such as generative networks for producing text and images. By the end of the book, you'll be able to implement deep learning applications in PyTorch with ease. What you will learn Use PyTorch for GPU-accelerated tensor computations Build custom datasets and data loaders for images and test the models using torchvision and torchtext Build an image classifier by implementing CNN architectures using PyTorch Build systems that do text classification and language modeling using RNN, LSTM, and GRU Learn advanced CNN architectures such as ResNet, Inception, Densenet, and learn how to use them for transfer learning Learn how to mix multiple models for a powerful ensemble model Generate new images using GAN’s and generate artistic images using style transfer Who this book is for This book is for machine learning engineers, data analysts, data scientists interested in deep learning and are looking to explore implementing advanced algorithms in PyTorch. Some knowledge of machine learning is helpful but not a mandatory need. Working knowledge of Python programming is expected.

Programming PyTorch for Deep Learning

Programming PyTorch for Deep Learning Book
Author : Ian Pointer
Publisher : O'Reilly Media
Release : 2019-09-20
ISBN : 1492045322
Language : En, Es, Fr & De

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Book Description :

Deep learning is changing everything. This machine-learning method has already surpassed traditional computer vision techniques, and the same is happening with NLP. If you're looking to bring deep learning into your domain, this practical book will bring you up to speed on key concepts using Facebook's PyTorch framework. Once author Ian Pointer helps you set up PyTorch on a cloud-based environment, you'll learn how use the framework to create neural architectures for performing operations on images, sound, text, and other types of data. By the end of the book, you'll be able to create neural networks and train them on multiple types of data. Learn how to deploy deep learning models to production Explore PyTorch use cases in companies other than Facebook Learn how to apply transfer learning to images Apply cutting-edge NLP techniques using a model trained on Wikipedia

Deep Learning for Coders with fastai and PyTorch

Deep Learning for Coders with fastai and PyTorch Book
Author : Jeremy Howard,Sylvain Gugger
Publisher : "O'Reilly Media, Inc."
Release : 2020-06-29
ISBN : 1492045470
Language : En, Es, Fr & De

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Book Description :

Deep learning is often viewed as the exclusive domain of math PhDs and big tech companies. But as this hands-on guide demonstrates, programmers comfortable with Python can achieve impressive results in deep learning with little math background, small amounts of data, and minimal code. How? With fastai, the first library to provide a consistent interface to the most frequently used deep learning applications. Authors Jeremy Howard and Sylvain Gugger, the creators of fastai, show you how to train a model on a wide range of tasks using fastai and PyTorch. You’ll also dive progressively further into deep learning theory to gain a complete understanding of the algorithms behind the scenes. Train models in computer vision, natural language processing, tabular data, and collaborative filtering Learn the latest deep learning techniques that matter most in practice Improve accuracy, speed, and reliability by understanding how deep learning models work Discover how to turn your models into web applications Implement deep learning algorithms from scratch Consider the ethical implications of your work Gain insight from the foreword by PyTorch cofounder, Soumith Chintala

Natural Language Processing with PyTorch

Natural Language Processing with PyTorch Book
Author : Delip Rao,Brian McMahan
Publisher : O'Reilly Media
Release : 2019-01-22
ISBN : 1491978201
Language : En, Es, Fr & De

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Book Description :

Natural Language Processing (NLP) provides boundless opportunities for solving problems in artificial intelligence, making products such as Amazon Alexa and Google Translate possible. If you’re a developer or data scientist new to NLP and deep learning, this practical guide shows you how to apply these methods using PyTorch, a Python-based deep learning library. Authors Delip Rao and Brian McMahon provide you with a solid grounding in NLP and deep learning algorithms and demonstrate how to use PyTorch to build applications involving rich representations of text specific to the problems you face. Each chapter includes several code examples and illustrations. Explore computational graphs and the supervised learning paradigm Master the basics of the PyTorch optimized tensor manipulation library Get an overview of traditional NLP concepts and methods Learn the basic ideas involved in building neural networks Use embeddings to represent words, sentences, documents, and other features Explore sequence prediction and generate sequence-to-sequence models Learn design patterns for building production NLP systems

Mastering PyTorch

Mastering PyTorch Book
Author : Ashish Ranjan Jha
Publisher : Packt Publishing Ltd
Release : 2021-02-12
ISBN : 1789616409
Language : En, Es, Fr & De

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Book Description :

Master advanced techniques and algorithms for deep learning with PyTorch using real-world examples Key FeaturesUnderstand how to use PyTorch 1.x to build advanced neural network modelsLearn to perform a wide range of tasks by implementing deep learning algorithms and techniquesGain expertise in domains such as computer vision, NLP, Deep RL, Explainable AI, and much moreBook Description Deep learning is driving the AI revolution, and PyTorch is making it easier than ever before for anyone to build deep learning applications. This PyTorch book will help you uncover expert techniques to get the most out of your data and build complex neural network models. The book starts with a quick overview of PyTorch and explores using convolutional neural network (CNN) architectures for image classification. You'll then work with recurrent neural network (RNN) architectures 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 and explore the world of generative adversarial networks (GANs). You'll not only build and train your own deep reinforcement learning models in PyTorch but also deploy PyTorch models to production using expert tips and techniques. Finally, you'll get to grips with training large models efficiently in a distributed manner, searching neural architectures effectively with AutoML, and rapidly prototyping models using PyTorch and fast.ai. By the end of this PyTorch book, you'll be able to perform complex deep learning tasks using PyTorch to build smart artificial intelligence models. What you will learnImplement text and music generating models using PyTorchBuild a deep Q-network (DQN) model in PyTorchExport universal PyTorch models using Open Neural Network Exchange (ONNX)Become well-versed with rapid prototyping using PyTorch with fast.aiPerform neural architecture search effectively using AutoMLEasily interpret machine learning (ML) models written in PyTorch using CaptumDesign ResNets, LSTMs, Transformers, and more using PyTorchFind out how to use PyTorch for distributed training using the torch.distributed APIWho this book is for This book is for data scientists, machine learning researchers, and deep learning practitioners looking to implement advanced deep learning paradigms using PyTorch 1.x. Working knowledge of deep learning with Python programming is required.

Deep Learning with PyTorch Quick Start Guide

Deep Learning with PyTorch Quick Start Guide Book
Author : David Julian
Publisher : Packt Publishing Ltd
Release : 2018-12-24
ISBN : 1789539730
Language : En, Es, Fr & De

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Book Description :

Introduction to deep learning and PyTorch by building a convolutional neural network and recurrent neural network for real-world use cases such as image classification, transfer learning, and natural language processing. Key FeaturesClear and concise explanationsGives important insights into deep learning modelsPractical demonstration of key conceptsBook Description PyTorch is extremely powerful and yet easy to learn. It provides advanced features, such as supporting multiprocessor, distributed, and parallel computation. This book is an excellent entry point for those wanting to explore deep learning with PyTorch to harness its power. This book will introduce you to the PyTorch deep learning library and teach you how to train deep learning models without any hassle. We will set up the deep learning environment using PyTorch, and then train and deploy different types of deep learning models, such as CNN, RNN, and autoencoders. You will learn how to optimize models by tuning hyperparameters and how to use PyTorch in multiprocessor and distributed environments. We will discuss long short-term memory network (LSTMs) and build a language model to predict text. By the end of this book, you will be familiar with PyTorch's capabilities and be able to utilize the library to train your neural networks with relative ease. What you will learnSet up the deep learning environment using the PyTorch libraryLearn to build a deep learning model for image classificationUse a convolutional neural network for transfer learningUnderstand to use PyTorch for natural language processingUse a recurrent neural network to classify textUnderstand how to optimize PyTorch in multiprocessor and distributed environmentsTrain, optimize, and deploy your neural networks for maximum accuracy and performanceLearn to deploy production-ready modelsWho this book is for Developers and Data Scientist familiar with Machine Learning but new to deep learning, or existing practitioners of deep learning who would like to use PyTorch to train their deep learning models will find this book to be useful. Having knowledge of Python programming will be an added advantage, while previous exposure to PyTorch is not needed.

Modern Computer Vision with PyTorch

Modern Computer Vision with PyTorch Book
Author : V Kishore Ayyadevara,Yeshwanth Reddy
Publisher : Packt Publishing Ltd
Release : 2020-11-27
ISBN : 1839216530
Language : En, Es, Fr & De

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Book Description :

Get to grips with deep learning techniques for building image processing applications using PyTorch with the help of code notebooks and test questions Key FeaturesImplement solutions to 50 real-world computer vision applications using PyTorchUnderstand the theory and working mechanisms of neural network architectures and their implementationDiscover best practices using a custom library created especially for this bookBook Description Deep learning is the driving force behind many recent advances in various computer vision (CV) applications. This book takes a hands-on approach to help you to solve over 50 CV problems using PyTorch1.x on real-world datasets. You’ll start by building a neural network (NN) from scratch using NumPy and PyTorch and discover best practices for tweaking its hyperparameters. You’ll then perform image classification using convolutional neural networks and transfer learning and understand how they work. As you progress, you’ll implement multiple use cases of 2D and 3D multi-object detection, segmentation, human-pose-estimation by learning about the R-CNN family, SSD, YOLO, U-Net architectures, and the Detectron2 platform. The book will also guide you in performing facial expression swapping, generating new faces, and manipulating facial expressions as you explore autoencoders and modern generative adversarial networks. You’ll learn how to combine CV with NLP techniques, such as LSTM and transformer, and RL techniques, such as Deep Q-learning, to implement OCR, image captioning, object detection, and a self-driving car agent. Finally, you'll move your NN model to production on the AWS Cloud. By the end of this book, you’ll be able to leverage modern NN architectures to solve over 50 real-world CV problems confidently. What you will learnTrain a NN from scratch with NumPy and PyTorchImplement 2D and 3D multi-object detection and segmentationGenerate digits and DeepFakes with autoencoders and advanced GANsManipulate images using CycleGAN, Pix2PixGAN, StyleGAN2, and SRGANCombine CV with NLP to perform OCR, image captioning, and object detectionCombine CV with reinforcement learning to build agents that play pong and self-drive a carDeploy a deep learning model on the AWS server using FastAPI and DockerImplement over 35 NN architectures and common OpenCV utilitiesWho this book is for This book is for beginners to PyTorch and intermediate-level machine learning practitioners who are looking to get well-versed with computer vision techniques using deep learning and PyTorch. If you are just getting started with neural networks, you’ll find the use cases accompanied by notebooks in GitHub present in this book useful. Basic knowledge of the Python programming language and machine learning is all you need to get started with this book.

Deep Learning with PyTorch Lightning

Deep Learning with PyTorch Lightning Book
Author : Kunal Sawarkar
Publisher : Packt Publishing
Release : 2022-02-04
ISBN : 9781800561618
Language : En, Es, Fr & De

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Book Description :

Build, train, deploy, and scale deep learning models quickly and accurately, improving your productivity using the lightweight PyTorch Wrapper Key Features: Become well-versed with PyTorch Lightning architecture and learn how it can be implemented in various industry domains Speed up your research using PyTorch Lightning by creating new loss functions, networks, and architectures Train and build new algorithms for massive data using distributed training Book 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 Learn: Customize models that are built for different datasets, model architectures, and optimizers Understand how a variety of Deep Learning models from image recognition and time series to GANs, semi-supervised and self-supervised models can be built Use out-of-the-box model architectures and pre-trained models using transfer learning Run and tune DL models in a multi-GPU environment using mixed-mode precisions Explore techniques for model scoring on massive workloads Discover troubleshooting techniques while debugging DL models Who 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.

Deep Learning with Pytorch 1 X

Deep Learning with Pytorch 1  X Book
Author : Laura Mitchell,Sri. Yogesh K.,Vishnu Subramanian
Publisher : Unknown
Release : 2019-11-29
ISBN : 9781838553005
Language : En, Es, Fr & De

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Book Description :

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.

Deep Learning for Vision Systems

Deep Learning for Vision Systems Book
Author : Mohamed Elgendy
Publisher : Manning Publications
Release : 2020-11-10
ISBN : 1617296198
Language : En, Es, Fr & De

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Book Description :

How does the computer learn to understand what it sees? Deep Learning for Vision Systems answers that by applying deep learning to computer vision. Using only high school algebra, this book illuminates the concepts behind visual intuition. You'll understand how to use deep learning architectures to build vision system applications for image generation and facial recognition. Summary Computer vision is central to many leading-edge innovations, including self-driving cars, drones, augmented reality, facial recognition, and much, much more. Amazing new computer vision applications are developed every day, thanks to rapid advances in AI and deep learning (DL). Deep Learning for Vision Systems teaches you the concepts and tools for building intelligent, scalable computer vision systems that can identify and react to objects in images, videos, and real life. With author Mohamed Elgendy's expert instruction and illustration of real-world projects, you’ll finally grok state-of-the-art deep learning techniques, so you can build, contribute to, and lead in the exciting realm of computer vision! Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications. About the technology How much has computer vision advanced? One ride in a Tesla is the only answer you’ll need. Deep learning techniques have led to exciting breakthroughs in facial recognition, interactive simulations, and medical imaging, but nothing beats seeing a car respond to real-world stimuli while speeding down the highway. About the book How does the computer learn to understand what it sees? Deep Learning for Vision Systems answers that by applying deep learning to computer vision. Using only high school algebra, this book illuminates the concepts behind visual intuition. You'll understand how to use deep learning architectures to build vision system applications for image generation and facial recognition. What's inside Image classification and object detection Advanced deep learning architectures Transfer learning and generative adversarial networks DeepDream and neural style transfer Visual embeddings and image search About the reader For intermediate Python programmers. About the author Mohamed Elgendy is the VP of Engineering at Rakuten. A seasoned AI expert, he has previously built and managed AI products at Amazon and Twilio. Table of Contents PART 1 - DEEP LEARNING FOUNDATION 1 Welcome to computer vision 2 Deep learning and neural networks 3 Convolutional neural networks 4 Structuring DL projects and hyperparameter tuning PART 2 - IMAGE CLASSIFICATION AND DETECTION 5 Advanced CNN architectures 6 Transfer learning 7 Object detection with R-CNN, SSD, and YOLO PART 3 - GENERATIVE MODELS AND VISUAL EMBEDDINGS 8 Generative adversarial networks (GANs) 9 DeepDream and neural style transfer 10 Visual embeddings

PyTorch Deep Learning Hands On

PyTorch Deep Learning Hands On Book
Author : Sherin Thomas,Sudhanshu Passi
Publisher : Packt Publishing Ltd
Release : 2019-04-30
ISBN : 1788833430
Language : En, Es, Fr & De

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Book Description :

Hands-on projects cover all the key deep learning methods built step-by-step in PyTorch Key Features Internals and principles of PyTorch Implement key deep learning methods in PyTorch: CNNs, GANs, RNNs, reinforcement learning, and more Build deep learning workflows and take deep learning models from prototyping to production Book 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 more Convolutional Neural Networks – create advanced computer vision systems Recurrent Neural Networks – work with sequential data such as natural language and audio Generative Adversarial Networks – create new content with models including SimpleGAN and CycleGAN Reinforcement Learning – develop systems that can solve complex problems such as driving or game playing Deep Learning workflows – move effectively from ideation to production with proper deep learning workflow using PyTorch and its utility packages Production-ready models – package your models for high-performance production environments Who this book is for Machine learning engineers who want to put PyTorch to work.

Machine Learning and Deep Learning in Real Time Applications

Machine Learning and Deep Learning in Real Time Applications Book
Author : Mahrishi, Mehul,Hiran, Kamal Kant,Meena, Gaurav,Sharma, Paawan
Publisher : IGI Global
Release : 2020-04-24
ISBN : 1799830977
Language : En, Es, Fr & De

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Book Description :

Artificial intelligence and its various components are rapidly engulfing almost every professional industry. Specific features of AI that have proven to be vital solutions to numerous real-world issues are machine learning and deep learning. These intelligent agents unlock higher levels of performance and efficiency, creating a wide span of industrial applications. However, there is a lack of research on the specific uses of machine/deep learning in the professional realm. Machine Learning and Deep Learning in Real-Time Applications provides emerging research exploring the theoretical and practical aspects of machine learning and deep learning and their implementations as well as their ability to solve real-world problems within several professional disciplines including healthcare, business, and computer science. Featuring coverage on a broad range of topics such as image processing, medical improvements, and smart grids, this book is ideally designed for researchers, academicians, scientists, industry experts, scholars, IT professionals, engineers, and students seeking current research on the multifaceted uses and implementations of machine learning and deep learning across the globe.

Advanced Deep Learning with Python

Advanced Deep Learning with Python Book
Author : Ivan Vasilev
Publisher : Packt Publishing Ltd
Release : 2019-12-12
ISBN : 1789952719
Language : En, Es, Fr & De

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Book Description :

Gain expertise in advanced deep learning domains such as neural networks, meta-learning, graph neural networks, and memory augmented neural networks using the Python ecosystem Key Features Get to grips with building faster and more robust deep learning architectures Investigate and train convolutional neural network (CNN) models with GPU-accelerated libraries such as TensorFlow and PyTorch Apply deep neural networks (DNNs) to computer vision problems, NLP, and GANs Book Description In order to build robust deep learning systems, you’ll need to understand everything from how neural networks work to training CNN models. In this book, you’ll discover newly developed deep learning models, methodologies used in the domain, and their implementation based on areas of application. You’ll start by understanding the building blocks and the math behind neural networks, and then move on to CNNs and their advanced applications in computer vision. You'll also learn to apply the most popular CNN architectures in object detection and image segmentation. Further on, you’ll focus on variational autoencoders and GANs. You’ll then use neural networks to extract sophisticated vector representations of words, before going on to cover various types of recurrent networks, such as LSTM and GRU. You’ll even explore the attention mechanism to process sequential data without the help of recurrent neural networks (RNNs). Later, you’ll use graph neural networks for processing structured data, along with covering meta-learning, which allows you to train neural networks with fewer training samples. Finally, you’ll understand how to apply deep learning to autonomous vehicles. By the end of this book, you’ll have mastered key deep learning concepts and the different applications of deep learning models in the real world. What you will learn Cover advanced and state-of-the-art neural network architectures Understand the theory and math behind neural networks Train DNNs and apply them to modern deep learning problems Use CNNs for object detection and image segmentation Implement generative adversarial networks (GANs) and variational autoencoders to generate new images Solve natural language processing (NLP) tasks, such as machine translation, using sequence-to-sequence models Understand DL techniques, such as meta-learning and graph neural networks Who this book is for This book is for data scientists, deep learning engineers and researchers, and AI developers who want to further their knowledge of deep learning and build innovative and unique deep learning projects. Anyone looking to get to grips with advanced use cases and methodologies adopted in the deep learning domain using real-world examples will also find this book useful. Basic understanding of deep learning concepts and working knowledge of the Python programming language is assumed.

Deep Learning with Python

Deep Learning with Python Book
Author : Nikhil Ketkar
Publisher : Apress
Release : 2017-04-18
ISBN : 1484227662
Language : En, Es, Fr & De

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Book Description :

Discover the practical aspects of implementing deep-learning solutions using the rich Python ecosystem. This book bridges the gap between the academic state-of-the-art and the industry state-of-the-practice by introducing you to deep learning frameworks such as Keras, Theano, and Caffe. The practicalities of these frameworks is often acquired by practitioners by reading source code, manuals, and posting questions on community forums, which tends to be a slow and a painful process. Deep Learning with Python allows you to ramp up to such practical know-how in a short period of time and focus more on the domain, models, and algorithms. This book briefly covers the mathematical prerequisites and fundamentals of deep learning, making this book a good starting point for software developers who want to get started in deep learning. A brief survey of deep learning architectures is also included. Deep Learning with Python also introduces you to key concepts of automatic differentiation and GPU computation which, while not central to deep learning, are critical when it comes to conducting large scale experiments. What You Will Learn Leverage deep learning frameworks in Python namely, Keras, Theano, and Caffe Gain the fundamentals of deep learning with mathematical prerequisites Discover the practical considerations of large scale experiments Take deep learning models to production Who This Book Is For Software developers who want to try out deep learning as a practical solution to a particular problem. Software developers in a data science team who want to take deep learning models developed by data scientists to production.

Deep Learning with Pytorch

Deep Learning with Pytorch Book
Author : Jerry N. P
Publisher : Independently Published
Release : 2019-01-29
ISBN : 9781795409209
Language : En, Es, Fr & De

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Book Description :

This book is an exploration of deep learning in Python using PyTorch. The author guides you on how to create neural network models using PyTorch in Python. You will know the initial steps of getting started with PyTorch in Python. This involves installing PyTorch and writing your first code. PyTorch works using the concept of graphs. The author helps you know how build neural network graphs in PyTorch. Deep learning in Python with PyTorch simply involves the creation of neural network models. The author helps you understand how to create neural network models with TensorFlow. You are guided on how to train such models with data of various types. Examples of such data include images and text. The process of loading your own data into PyTorch for training neural network models has also been discussed. You will also know how to use the inbuilt data for training your neural network models. This book will help you to understand: - Why PyTorch for Deep Learning? - Getting Started with PyTorch - Building a Neural Network - Loading and Processing Data - Convolutional Neural Networks - Transfer Learning - Developing Distributed Applications - Word Embeddings - Moving a Model from PyTorch to Caffe2 - Custom C Extensions - Neural Transfer with PyTorch Tags: pytorch deep learning, python programming, python, python data science handbook, neural network python, tensorflow python, tensorflow for deep learning, python code programming.

PyTorch Deep Learning Hands On

PyTorch Deep Learning Hands On Book
Author : Sherin Thomas,Sudhanshu Passi
Publisher : Unknown
Release : 2019-04-26
ISBN : 9781788834131
Language : En, Es, Fr & De

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Book Description :

All the key deep learning methods built step-by-step in PyTorch Key Features Understand the internals and principles of PyTorch Implement key deep learning methods in PyTorch: CNNs, GANs, RNNs, reinforcement learning, and more Build deep learning workflows and take deep learning models from prototyping to production Book Description PyTorch is a new, lightweight, and Python-first tool for deep learning. Built by Facebook to offer flexibility and speed, it has quickly become the preferred tool for deep learning experts. PyTorch helps you release deep learning models faster than ever before. PyTorch Deep Learning Hands-On shows how to implement every major deep learning architecture in PyTorch. Starting with simple neural networks, it covers PyTorch for computer vision (CNN), 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 the core principles, before sharing the code you need to implement them in PyTorch. If you want to become a deep learning expert this book is for you. What you will learn Use PyTorch to build: Simple Neural Networks - build neural networks the PyTorch way, with high-level functions, optimizers, and more Convolutional Neural Networks - create advanced computer vision systems Recurrent Neural Networks - work with sequential data such as natural language and audio Generative Adversarial Networks - create new content with models including SimpleGAN and CycleGAN Reinforcement Learning - develop systems that can solve complex problems such as driving or game playing Deep Learning workflows - move effectively from ideation to production with proper deep learning workflow using PyTorch and its utility packages Production-ready models - package your models for high-performance production environments Who this book is for Machine learning professionals and enthusiasts who know Python and want to build efficient and powerful deep learning systems in PyTorch.

PyTorch Recipes

PyTorch Recipes Book
Author : Pradeepta Mishra
Publisher : Apress
Release : 2019-01-28
ISBN : 1484242580
Language : En, Es, Fr & De

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Book Description :

Get up to speed with the deep learning concepts of Pytorch using a problem-solution approach. Starting with an introduction to PyTorch, you'll get familiarized with tensors, a type of data structure used to calculate arithmetic operations and also learn how they operate. You will then take a look at probability distributions using PyTorch and get acquainted with its concepts. Further you will dive into transformations and graph computations with PyTorch. Along the way you will take a look at common issues faced with neural network implementation and tensor differentiation, and get the best solutions for them. Moving on to algorithms; you will learn how PyTorch works with supervised and unsupervised algorithms. You will see how convolutional neural networks, deep neural networks, and recurrent neural networks work using PyTorch. In conclusion you will get acquainted with natural language processing and text processing using PyTorch. What You Will LearnMaster tensor operations for dynamic graph-based calculations using PyTorchCreate PyTorch transformations and graph computations for neural networksCarry out supervised and unsupervised learning using PyTorch Work with deep learning algorithms such as CNN and RNNBuild LSTM models in PyTorch Use PyTorch for text processing Who This Book Is For Readers wanting to dive straight into programming PyTorch.

Deep Learning with Python

Deep Learning with Python Book
Author : Nikhil Ketkar,Jojo Moolayil
Publisher : Apress
Release : 2021-04-10
ISBN : 9781484253632
Language : En, Es, Fr & De

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Book Description :

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.

PyTorch 1 x Reinforcement Learning Cookbook

PyTorch 1 x Reinforcement Learning Cookbook Book
Author : Yuxi (Hayden) Liu
Publisher : Packt Publishing Ltd
Release : 2019-10-31
ISBN : 1838553231
Language : En, Es, Fr & De

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Book Description :

Implement reinforcement learning techniques and algorithms with the help of real-world examples and recipes Key FeaturesUse PyTorch 1.x to design and build self-learning artificial intelligence (AI) modelsImplement RL algorithms to solve control and optimization challenges faced by data scientists todayApply modern RL libraries to simulate a controlled environment for your projectsBook Description Reinforcement learning (RL) is a branch of machine learning that has gained popularity in recent times. It allows you to train AI models that learn from their own actions and optimize their behavior. PyTorch has also emerged as the preferred tool for training RL models because of its efficiency and ease of use. With this book, you'll explore the important RL concepts and the implementation of algorithms in PyTorch 1.x. The recipes in the book, along with real-world examples, will help you master various RL techniques, such as dynamic programming, Monte Carlo simulations, temporal difference, and Q-learning. You'll also gain insights into industry-specific applications of these techniques. Later chapters will guide you through solving problems such as the multi-armed bandit problem and the cartpole problem using the multi-armed bandit algorithm and function approximation. You'll also learn how to use Deep Q-Networks to complete Atari games, along with how to effectively implement policy gradients. Finally, you'll discover how RL techniques are applied to Blackjack, Gridworld environments, internet advertising, and the Flappy Bird game. By the end of this book, you'll have developed the skills you need to implement popular RL algorithms and use RL techniques to solve real-world problems. What you will learnUse Q-learning and the state–action–reward–state–action (SARSA) algorithm to solve various Gridworld problemsDevelop a multi-armed bandit algorithm to optimize display advertisingScale up learning and control processes using Deep Q-NetworksSimulate Markov Decision Processes, OpenAI Gym environments, and other common control problemsSelect and build RL models, evaluate their performance, and optimize and deploy themUse policy gradient methods to solve continuous RL problemsWho this book is for Machine learning engineers, data scientists and AI researchers looking for quick solutions to different reinforcement learning problems will find this book useful. Although prior knowledge of machine learning concepts is required, experience with PyTorch will be useful but not necessary.