Deep Learning With Pytorch Quick Start Guide
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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.
Fasttext Quick Start Guide
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Author : Joydeep Bhattacharjee
language : en
Publisher: Packt Publishing Ltd
Release Date : 2018-07-26
Fasttext Quick Start Guide written by Joydeep Bhattacharjee 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 2018-07-26 with Computers categories.
Perform efficient fast text representation and classification with Facebook's fastText library Key Features Introduction to Facebook's fastText library for NLP Perform efficient word representations, sentence classification, vector representation Build better, more scalable solutions for text representation and classification Book Description Facebook's fastText library handles text representation and classification, used for Natural Language Processing (NLP). Most organizations have to deal with enormous amounts of text data on a daily basis, and gaining efficient data insights requires powerful NLP tools such as fastText. This book is your ideal introduction to fastText. You will learn how to create fastText models from the command line, without the need for complicated code. You will explore the algorithms that fastText is built on and how to use them for word representation and text classification. Next, you will use fastText in conjunction with other popular libraries and frameworks such as Keras, TensorFlow, and PyTorch. Finally, you will deploy fastText models to mobile devices. By the end of this book, you will have all the required knowledge to use fastText in your own applications at work or in projects. What you will learn Create models using the default command line options in fastText Understand the algorithms used in fastText to create word vectors Combine command line text transformation capabilities and the fastText library to implement a training, validation, and prediction pipeline Explore word representation and sentence classification using fastText Use Gensim and spaCy to load the vectors, transform, lemmatize, and perform other NLP tasks efficiently Develop a fastText NLP classifier using popular frameworks, such as Keras, Tensorflow, and PyTorch Who this book is for This book is for data analysts, data scientists, and machine learning developers who want to perform efficient word representation and sentence classification using Facebook's fastText library. Basic knowledge of Python programming is required.
Natural Language Processing With Python Quick Start Guide
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Author : Nirant Kasliwal
language : en
Publisher: Packt Publishing Ltd
Release Date : 2018-11-30
Natural Language Processing With Python Quick Start Guide written by Nirant Kasliwal 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 2018-11-30 with Computers categories.
Build and deploy intelligent applications for natural language processing with Python by using industry standard tools and recently popular methods in deep learning Key FeaturesA no-math, code-driven programmer’s guide to text processing and NLPGet state of the art results with modern tooling across linguistics, text vectors and machine learningFundamentals of NLP methods from spaCy, gensim, scikit-learn and PyTorchBook Description NLP in Python is among the most sought after skills among data scientists. With code and relevant case studies, this book will show how you can use industry-grade tools to implement NLP programs capable of learning from relevant data. We will explore many modern methods ranging from spaCy to word vectors that have reinvented NLP. The book takes you from the basics of NLP to building text processing applications. We start with an introduction to the basic vocabulary along with a workflow for building NLP applications. We use industry-grade NLP tools for cleaning and pre-processing text, automatic question and answer generation using linguistics, text embedding, text classifier, and building a chatbot. With each project, you will learn a new concept of NLP. You will learn about entity recognition, part of speech tagging and dependency parsing for Q and A. We use text embedding for both clustering documents and making chatbots, and then build classifiers using scikit-learn. We conclude by deploying these models as REST APIs with Flask. By the end, you will be confident building NLP applications, and know exactly what to look for when approaching new challenges. What you will learnUnderstand classical linguistics in using English grammar for automatically generating questions and answers from a free text corpusWork with text embedding models for dense number representations of words, subwords and characters in the English language for exploring document clusteringDeep Learning in NLP using PyTorch with a code-driven introduction to PyTorchUsing an NLP project management Framework for estimating timelines and organizing your project into stagesHack and build a simple chatbot application in 30 minutesDeploy an NLP or machine learning application using Flask as RESTFUL APIsWho this book is for Programmers who wish to build systems that can interpret language. Exposure to Python programming is required. Familiarity with NLP or machine learning vocabulary will be helpful, but not mandatory.
Deep Learning With Pytorch
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Author : Jerry N. P
language : en
Publisher: Independently Published
Release Date : 2019-01-29
Deep Learning With Pytorch written by Jerry N. P and has been published by Independently Published this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019-01-29 with Computers categories.
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.
Deep Learning With Jax
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Author : Grigory Sapunov
language : en
Publisher: Simon and Schuster
Release Date : 2024-12-03
Deep Learning With Jax written by Grigory Sapunov and has been published by Simon and Schuster this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-12-03 with Computers categories.
Accelerate deep learning and other number-intensive tasks with JAX, Google’s awesome high-performance numerical computing library. The JAX numerical computing library tackles the core performance challenges at the heart of deep learning and other scientific computing tasks. By combining Google’s Accelerated Linear Algebra platform (XLA) with a hyper-optimized version of NumPy and a variety of other high-performance features, JAX delivers a huge performance boost in low-level computations and transformations. In Deep Learning with JAX you will learn how to: • Use JAX for numerical calculations • Build differentiable models with JAX primitives • Run distributed and parallelized computations with JAX • Use high-level neural network libraries such as Flax • Leverage libraries and modules from the JAX ecosystem Deep Learning with JAX is a hands-on guide to using JAX for deep learning and other mathematically-intensive applications. Google Developer Expert Grigory Sapunov steadily builds your understanding of JAX’s concepts. The engaging examples introduce the fundamental concepts on which JAX relies and then show you how to apply them to real-world tasks. You’ll learn how to use JAX’s ecosystem of high-level libraries and modules, and also how to combine TensorFlow and PyTorch with JAX for data loading and deployment. About the technology Google’s JAX offers a fresh vision for deep learning. This powerful library gives you fine control over low level processes like gradient calculations, delivering fast and efficient model training and inference, especially on large datasets. JAX has transformed how research scientists approach deep learning. Now boasting a robust ecosystem of tools and libraries, JAX makes evolutionary computations, federated learning, and other performance-sensitive tasks approachable for all types of applications. About the book Deep Learning with JAX teaches you to build effective neural networks with JAX. In this example-rich book, you’ll discover how JAX’s unique features help you tackle important deep learning performance challenges, like distributing computations across a cluster of TPUs. You’ll put the library into action as you create an image classification tool, an image filter application, and other realistic projects. The nicely-annotated code listings demonstrate how JAX’s functional programming mindset improves composability and parallelization. What's inside • Use JAX for numerical calculations • Build differentiable models with JAX primitives • Run distributed and parallelized computations with JAX • Use high-level neural network libraries such as Flax About the reader For intermediate Python programmers who are familiar with deep learning. About the author Grigory Sapunov holds a Ph.D. in artificial intelligence and is a Google Developer Expert in Machine Learning. The technical editor on this book was Nicholas McGreivy. Table of Contents Part 1 1 When and why to use JAX 2 Your first program in JAX Part 2 3 Working with arrays 4 Calculating gradients 5 Compiling your code 6 Vectorizing your code 7 Parallelizing your computations 8 Using tensor sharding 9 Random numbers in JAX 10 Working with pytrees Part 3 11 Higher-level neural network libraries 12 Other members of the JAX ecosystem A Installing JAX B Using Google Colab C Using Google Cloud TPUs D Experimental parallelization
Deep Learning With Pytorch
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Author : Eli Stevens
language : en
Publisher: Manning
Release Date : 2020-08-04
Deep Learning With Pytorch written by Eli Stevens and has been published by Manning this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-08-04 with Computers categories.
“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 Step By Step
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Author : Daniel Voight Godoy
language : en
Publisher:
Release Date : 2021
Deep Learning With Pytorch Step By Step written by Daniel Voight Godoy and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021 with Conjugate gradient methods categories.
Pytorch For Deep Learning
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Author : BARRY. LUIZ
language : en
Publisher: Independently Published
Release Date : 2025-02-11
Pytorch For Deep Learning written by BARRY. LUIZ and has been published by Independently Published this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-02-11 with Computers categories.
Ready to dive into the revolutionary world of Deep Learning but feeling overwhelmed? This book is your friendly, hands-on guide to mastering PyTorch, the powerful and intuitive Python framework that's transforming artificial intelligence. "PyTorch for Deep Learning: A Practical Introduction for Beginners" provides a clear and accessible path for anyone with basic Python knowledge to build and train their own deep learning models. You'll start with the fundamentals, learning about tensors, automatic differentiation, and neural network building blocks. The book then guides you through practical examples, including image and text classification, using convolutional neural networks (CNNs) and recurrent neural networks (RNNs). Finally, you'll discover advanced techniques like transfer learning to leverage pre-trained models for rapid development and cutting-edge results. Throughout the book, code examples are clear, well-documented, and designed to help you build real-world applications. This book prioritizes hands-on learning and makes no assumptions about previous machine learning knowledge. Learn to build and train linear regression and logistic regression models, how data is handled by datasets and dataloaders, and how to monitor and evaluate model performance using metrics and visualizations. This book provides practical exercises that reinforce learning and show how to apply what you've learned to real-world problems. This book will help you to gain skills in one of the most indemand AI frameworks. Why Choose This Book? Beginner-Friendly: Assumes no prior machine learning knowledge. Practical Focus: Emphasizes hands-on coding and real-world applications. Clear Explanations: Breaks down complex concepts into simple, easy-to-understand terms. Step-by-Step Guidance: Provides a structured path from foundational concepts to building complete deep learning pipelines. Up-to-Date Content: Covers the latest PyTorch features and best practices. Ready to transform your Python skills into Deep Learning expertise? Grab your copy of "PyTorch for Deep Learning: A Practical Introduction for Beginners" today and start building the future of AI!
Pytorch For Beginners
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Author : Jason Brener
language : en
Publisher: Independently Published
Release Date : 2025-07-31
Pytorch For Beginners written by Jason Brener and has been published by Independently Published this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-07-31 with Computers categories.
PyTorch for Beginners: A Hands-On Guide to Deep Learning with Python PyTorch for Beginners is a practical, beginner-friendly introduction to building deep learning models using Python and PyTorch. This book demystifies the world of neural networks by guiding readers through real-world projects and step-by-step implementations, all without requiring a background in machine learning or advanced mathematics. Whether you're just starting your journey in artificial intelligence or switching from another framework, this guide helps you gain a solid foundation and hands-on experience with one of today's most popular deep learning libraries. With a clear focus on practical applications, the book covers everything from tensors and automatic differentiation to building and training your first neural network. By the end, you'll be comfortable creating models for tasks like image classification, natural language processing, and more empowered to take on real-world deep learning challenges with confidence. This book simplifies deep learning by combining theoretical insights with code-driven learning. Using PyTorch, one of the most flexible and beginner-friendly frameworks, you'll learn to work with tensors, train models, and understand how neural networks operate under the hood. Each chapter builds on the last, offering progressively deeper insights into model design, optimization, and deployment. Key Features of This Book Step-by-step tutorials with fully documented PyTorch code Real-world projects covering vision and NLP applications Clear explanations of core deep learning concepts Best practices for training, debugging, and optimizing models Hands-on exercises to reinforce learning at each stage A complete companion code repository for experimentation This book is ideal for Python developers, students, data enthusiasts, and aspiring machine learning engineers who want to break into deep learning using a practical, project-based approach. No prior experience with PyTorch or deep learning is required just a willingness to learn and experiment. Whether you're building your first neural network or preparing for a deep learning role, PyTorch for Beginners is your gateway into modern AI development. Grab your copy now and start building real-world deep learning models with confidence one line of PyTorch code at a time!
Tensorflow Reinforcement Learning Quick Start Guide
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Author : Kaushik Balakrishnan
language : en
Publisher: Packt Publishing Ltd
Release Date : 2019-03-30
Tensorflow Reinforcement Learning Quick Start Guide written by Kaushik Balakrishnan 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 2019-03-30 with Computers categories.
Leverage the power of Tensorflow to Create powerful software agents that can self-learn to perform real-world tasks Key FeaturesExplore efficient Reinforcement Learning algorithms and code them using TensorFlow and PythonTrain Reinforcement Learning agents for problems, ranging from computer games to autonomous driving.Formulate and devise selective algorithms and techniques in your applications in no time.Book Description Advances in reinforcement learning algorithms have made it possible to use them for optimal control in several different industrial applications. With this book, you will apply Reinforcement Learning to a range of problems, from computer games to autonomous driving. The book starts by introducing you to essential Reinforcement Learning concepts such as agents, environments, rewards, and advantage functions. You will also master the distinctions between on-policy and off-policy algorithms, as well as model-free and model-based algorithms. You will also learn about several Reinforcement Learning algorithms, such as SARSA, Deep Q-Networks (DQN), Deep Deterministic Policy Gradients (DDPG), Asynchronous Advantage Actor-Critic (A3C), Trust Region Policy Optimization (TRPO), and Proximal Policy Optimization (PPO). The book will also show you how to code these algorithms in TensorFlow and Python and apply them to solve computer games from OpenAI Gym. Finally, you will also learn how to train a car to drive autonomously in the Torcs racing car simulator. By the end of the book, you will be able to design, build, train, and evaluate feed-forward neural networks and convolutional neural networks. You will also have mastered coding state-of-the-art algorithms and also training agents for various control problems. What you will learnUnderstand the theory and concepts behind modern Reinforcement Learning algorithmsCode state-of-the-art Reinforcement Learning algorithms with discrete or continuous actionsDevelop Reinforcement Learning algorithms and apply them to training agents to play computer gamesExplore DQN, DDQN, and Dueling architectures to play Atari's Breakout using TensorFlowUse A3C to play CartPole and LunarLanderTrain an agent to drive a car autonomously in a simulatorWho this book is for Data scientists and AI developers who wish to quickly get started with training effective reinforcement learning models in TensorFlow will find this book very useful. Prior knowledge of machine learning and deep learning concepts (as well as exposure to Python programming) will be useful.