Pytorch Deep Learning Hands On
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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.
Deep Learning With Pytorch Quick Start Guide
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Author : David Julian
language : en
Publisher: Packt Publishing Ltd
Release Date : 2018-12-24
Deep Learning With Pytorch Quick Start Guide written by David Julian 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 2018-12-24 with Computers categories.
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.
Hands On Python And Pytorch
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Author : Sarful Hassan
language : en
Publisher: Independently Published
Release Date : 2025-02-04
Hands On Python And Pytorch written by Sarful Hassan and has been published by Independently Published this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-02-04 with Computers categories.
Hands-On Python and PyTorch: A Practical Guide to Deep Learning Master Deep Learning with Python and PyTorch Are you ready to dive into the world of deep learning and AI? Hands-On Python and PyTorch: A Practical Guide to Deep Learning is your step-by-step companion to mastering neural networks, machine learning models, and real-world AI applications with Python and PyTorch. Why This Book? ✅ Comprehensive & Hands-On - Covers everything from basic PyTorch operations to advanced deep learning techniques. ✅ Real-World Applications - Learn to build image classifiers, NLP models, GANs, and reinforcement learning systems. ✅ AI & Deep Learning Integration - Understand how PyTorch works with TensorFlow, OpenCV, and other AI frameworks. ✅ Optimized for Python - Uses Python 3.x for efficient and scalable implementation. ✅ Beginner to Expert Guide - Suitable for students, developers, data scientists, and AI enthusiasts looking to master PyTorch and deep learning. What You'll Learn ✔️ Setting up PyTorch and Python for deep learning projects ✔️ Core PyTorch concepts: Tensors, Autograd, and Modules ✔️ Building and training neural networks from scratch ✔️ Advanced optimization techniques and model tuning ✔️ Real-time applications in computer vision, NLP, and reinforcement learning ✔️ Deploying AI models efficiently for production Who Should Read This Book?
Hands On Natural Language Processing With Pytorch 1 X
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Author : Thomas Dop
language : en
Publisher: Packt Publishing Ltd
Release Date : 2020-07-09
Hands On Natural Language Processing With Pytorch 1 X written by Thomas Dop 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 2020-07-09 with Computers categories.
Become a proficient NLP data scientist by developing deep learning models for NLP and extract valuable insights from structured and unstructured data Key FeaturesGet to grips with word embeddings, semantics, labeling, and high-level word representations using practical examplesLearn modern approaches to NLP and explore state-of-the-art NLP models using PyTorchImprove your NLP applications with innovative neural networks such as RNNs, LSTMs, and CNNsBook Description In the internet age, where an increasing volume of text data is generated daily from social media and other platforms, being able to make sense of that data is a crucial skill. With this book, you’ll learn how to extract valuable insights from text by building deep learning models for natural language processing (NLP) tasks. Starting by understanding how to install PyTorch and using CUDA to accelerate the processing speed, you’ll explore how the NLP architecture works with the help of practical examples. This PyTorch NLP book will guide you through core concepts such as word embeddings, CBOW, and tokenization in PyTorch. You’ll then learn techniques for processing textual data and see how deep learning can be used for NLP tasks. The book demonstrates how to implement deep learning and neural network architectures to build models that will allow you to classify and translate text and perform sentiment analysis. Finally, you’ll learn how to build advanced NLP models, such as conversational chatbots. By the end of this book, you’ll not only have understood the different NLP problems that can be solved using deep learning with PyTorch, but also be able to build models to solve them. What you will learnUse NLP techniques for understanding, processing, and generating textUnderstand PyTorch, its applications and how it can be used to build deep linguistic modelsExplore the wide variety of deep learning architectures for NLPDevelop the skills you need to process and represent both structured and unstructured NLP dataBecome well-versed with state-of-the-art technologies and exciting new developments in the NLP domainCreate chatbots using attention-based neural networksWho this book is for This PyTorch book is for NLP developers, machine learning and deep learning developers, and anyone interested in building intelligent language applications using both traditional NLP approaches and deep learning architectures. If you’re looking to adopt modern NLP techniques and models for your development projects, this book is for you. Working knowledge of Python programming, along with basic working knowledge of NLP tasks, is required.
Hands On Deep Learning With Pytorch
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Author : CAMILA. JONES
language : en
Publisher: Independently Published
Release Date : 2025-01-26
Hands On Deep Learning With Pytorch written by CAMILA. JONES and has been published by Independently Published this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-01-26 with Computers categories.
Unlock the full potential of deep learning with this comprehensive, hands-on guide to PyTorch! Whether you are a beginner eager to learn the foundations of deep learning or an experienced practitioner looking to tackle advanced projects, this book has everything you need to get started and master PyTorch. Key Features: Practical, hands-on approach: Dive into real-world projects, step-by-step tutorials, and interactive code examples designed to teach you how to apply deep learning techniques effectively. Comprehensive coverage: From understanding the basics of neural networks and backpropagation to mastering advanced topics like CNNs, RNNs, transformers, GANs, and reinforcement learning. Build, train, and deploy models: Learn how to design and train cutting-edge models for a variety of applications, including image classification, natural language processing, and generative modeling. Real-world case studies: Explore practical use cases from industries like healthcare, finance, and autonomous vehicles, demonstrating how deep learning is transforming modern technology. Master PyTorch: Understand the core PyTorch concepts, including tensors, autograd, and GPU acceleration, and apply them in building efficient, scalable deep learning models. This book will take you through the complete deep learning workflow: Start with the basics: Learn the foundations of neural networks and PyTorch, setting up your development environment and writing your first deep learning model. Explore advanced techniques: Dive deep into convolutional neural networks (CNNs) for computer vision, recurrent neural networks (RNNs) and LSTMs for time-series and NLP tasks, and the powerful transformer models that power state-of-the-art natural language processing. Hands-on projects: Build practical applications, from image classification and sentiment analysis to creating your own generative models like GANs for art and data generation. Deploy your models: Learn how to take your trained models and deploy them to production with tools like PyTorch Serve and ONNX, or convert them for use with other frameworks such as TensorFlow. Why This Book? With clear, simple explanations and practical code examples, this book simplifies complex deep learning concepts and turns theory into action. Each chapter includes exercises and challenges that reinforce your learning, making this a perfect resource for students, professionals, and anyone passionate about AI and machine learning. By the end of this book, you'll have the skills to: Build deep learning models using PyTorch for a variety of tasks. Fine-tune pre-trained models for your own applications. Work with the latest deep learning techniques and frameworks. Apply deep learning solutions in real-world scenarios. Perfect for: Beginners wanting to learn deep learning and PyTorch from scratch. Intermediate learners looking to advance their knowledge and tackle more complex models. Developers and AI enthusiasts who want to integrate deep learning into their projects. Get ready to build powerful, production-ready deep learning models. Start your journey with PyTorch today, and take the first step toward mastering AI!
Modern Computer Vision With Pytorch
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Author : V Kishore Ayyadevara
language : en
Publisher: Packt Publishing Ltd
Release Date : 2020-11-27
Modern Computer Vision With Pytorch written by V Kishore Ayyadevara 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 2020-11-27 with Computers categories.
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.
Hands On One Shot Learning With Python
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Author : Shruti Jadon
language : en
Publisher: Packt Publishing Ltd
Release Date : 2020-04-10
Hands On One Shot Learning With Python written by Shruti Jadon 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 2020-04-10 with Computers categories.
Get to grips with building powerful deep learning models using PyTorch and scikit-learn Key FeaturesLearn how you can speed up the deep learning process with one-shot learningUse Python and PyTorch to build state-of-the-art one-shot learning modelsExplore architectures such as Siamese networks, memory-augmented neural networks, model-agnostic meta-learning, and discriminative k-shot learningBook Description One-shot learning has been an active field of research for scientists trying to develop a cognitive machine that mimics human learning. With this book, you'll explore key approaches to one-shot learning, such as metrics-based, model-based, and optimization-based techniques, all with the help of practical examples. Hands-On One-shot Learning with Python will guide you through the exploration and design of deep learning models that can obtain information about an object from one or just a few training samples. The book begins with an overview of deep learning and one-shot learning and then introduces you to the different methods you can use to achieve it, such as deep learning architectures and probabilistic models. Once you've got to grips with the core principles, you'll explore real-world examples and implementations of one-shot learning using PyTorch 1.x on datasets such as Omniglot and MiniImageNet. Finally, you'll explore generative modeling-based methods and discover the key considerations for building systems that exhibit human-level intelligence. By the end of this book, you'll be well-versed with the different one- and few-shot learning methods and be able to use them to build your own deep learning models. What you will learnGet to grips with the fundamental concepts of one- and few-shot learningWork with different deep learning architectures for one-shot learningUnderstand when to use one-shot and transfer learning, respectivelyStudy the Bayesian network approach for one-shot learningImplement one-shot learning approaches based on metrics, models, and optimization in PyTorchDiscover different optimization algorithms that help to improve accuracy even with smaller volumes of dataExplore various one-shot learning architectures based on classification and regressionWho this book is for If you're an AI researcher or a machine learning or deep learning expert looking to explore one-shot learning, this book is for you. It will help you get started with implementing various one-shot techniques to train models faster. Some Python programming experience is necessary to understand the concepts covered in this book.
Pytorch 1 X Reinforcement Learning Cookbook
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Author : Yuxi (Hayden) Liu
language : en
Publisher: Packt Publishing Ltd
Release Date : 2019-10-31
Pytorch 1 X Reinforcement Learning Cookbook written by Yuxi (Hayden) Liu 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 2019-10-31 with Computers categories.
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.
Deep Reinforcement Learning Hands On
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Author : Maxim Lapan
language : en
Publisher: Packt Publishing Ltd
Release Date : 2020-01-31
Deep Reinforcement Learning Hands On written by Maxim Lapan 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 2020-01-31 with Computers categories.
Revised and expanded to include multi-agent methods, discrete optimization, RL in robotics, advanced exploration techniques, and more Key Features Second edition of the bestselling introduction to deep reinforcement learning, expanded with six new chapters Learn advanced exploration techniques including noisy networks, pseudo-count, and network distillation methods Apply RL methods to cheap hardware robotics platforms Book DescriptionDeep Reinforcement Learning Hands-On, Second Edition is an updated and expanded version of the bestselling guide to the very latest reinforcement learning (RL) tools and techniques. It provides you with an introduction to the fundamentals of RL, along with the hands-on ability to code intelligent learning agents to perform a range of practical tasks. With six new chapters devoted to a variety of up-to-the-minute developments in RL, including discrete optimization (solving the Rubik's Cube), multi-agent methods, Microsoft's TextWorld environment, advanced exploration techniques, and more, you will come away from this book with a deep understanding of the latest innovations in this emerging field. In addition, you will gain actionable insights into such topic areas as deep Q-networks, policy gradient methods, continuous control problems, and highly scalable, non-gradient methods. You will also discover how to build a real hardware robot trained with RL for less than $100 and solve the Pong environment in just 30 minutes of training using step-by-step code optimization. In short, Deep Reinforcement Learning Hands-On, Second Edition, is your companion to navigating the exciting complexities of RL as it helps you attain experience and knowledge through real-world examples.What you will learn Understand the deep learning context of RL and implement complex deep learning models Evaluate RL methods including cross-entropy, DQN, actor-critic, TRPO, PPO, DDPG, D4PG, and others Build a practical hardware robot trained with RL methods for less than $100 Discover Microsoft s TextWorld environment, which is an interactive fiction games platform Use discrete optimization in RL to solve a Rubik s Cube Teach your agent to play Connect 4 using AlphaGo Zero Explore the very latest deep RL research on topics including AI chatbots Discover advanced exploration techniques, including noisy networks and network distillation techniques Who this book is for Some fluency in Python is assumed. Sound understanding of the fundamentals of deep learning will be helpful. This book is an introduction to deep RL and requires no background in RL
Hands On Computer Vision With Detectron2
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Author : Van Vung Pham
language : en
Publisher: Packt Publishing Ltd
Release Date : 2023-04-14
Hands On Computer Vision With Detectron2 written by Van Vung Pham 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 2023-04-14 with Computers categories.
Explore Detectron2 using cutting-edge models and learn all about implementing future computer vision applications in custom domains Purchase of the print or Kindle book includes a free PDF eBook Key Features Learn how to tackle common computer vision tasks in modern businesses with Detectron2 Leverage Detectron2 performance tuning techniques to control the model's finest details Deploy Detectron2 models into production and develop Detectron2 models for mobile devices Book Description Computer vision is a crucial component of many modern businesses, including automobiles, robotics, and manufacturing, and its market is growing rapidly. This book helps you explore Detectron2, Facebook's next-gen library providing cutting-edge detection and segmentation algorithms. It's used in research and practical projects at Facebook to support computer vision tasks, and its models can be exported to TorchScript or ONNX for deployment. The book provides you with step-by-step guidance on using existing models in Detectron2 for computer vision tasks (object detection, instance segmentation, key-point detection, semantic detection, and panoptic segmentation). You'll get to grips with the theories and visualizations of Detectron2's architecture and learn how each module in Detectron2 works. As you advance, you'll build your practical skills by working on two real-life projects (preparing data, training models, fine-tuning models, and deployments) for object detection and instance segmentation tasks using Detectron2. Finally, you'll deploy Detectron2 models into production and develop Detectron2 applications for mobile devices. By the end of this deep learning book, you'll have gained sound theoretical knowledge and useful hands-on skills to help you solve advanced computer vision tasks using Detectron2. What you will learn Build computer vision applications using existing models in Detectron2 Grasp the concepts underlying Detectron2's architecture and components Develop real-life projects for object detection and object segmentation using Detectron2 Improve model accuracy using Detectron2's performance-tuning techniques Deploy Detectron2 models into server environments with ease Develop and deploy Detectron2 models into browser and mobile environments Who this book is for If you are a deep learning application developer, researcher, or software developer with some prior knowledge about deep learning, this book is for you to get started and develop deep learning models for computer vision applications. Even if you are an expert in computer vision and curious about the features of Detectron2, or you would like to learn some cutting-edge deep learning design patterns, you will find this book helpful. Some HTML, Android, and C++ programming skills are advantageous if you want to deploy computer vision applications using these platforms.