| title | excerpt | publishDate | category |
|---|---|---|---|
The Best PyTorch Books |
Discover exceptional Pytorch books covering deep learning, artificial intelligence, and cutting-edge applications with our comprehensive roundup, providing expert insights and resources for Python developers. |
2024-05-18T09:51:39Z |
Welcome to our Pytorch Books roundup, where we've curated a selection of top-notch reads to help deepen your understanding of this powerful machine learning framework. Dive into these engaging and informative books, and explore the world of Pytorch like never before. Let's get started! 📖💻
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Bayesian Inference in Statistical Analysis, by the esteemed authors George E. P. Box and George C. Tiao, delves into the realm of mathematical analysis and inquisition. This comprehensive exploration revolves around the application and relevance of Bayes' theorem to scientific issues where parameters are uncertain, and information is scarce a priori.
The authors introduce the reader to the essential elements of the Bayesian approach, elucidating critical concepts such as the selection of prior distributions, noninformative prior distributions, the role of nuisance parameters, and the significance of sufficient statistics. They then proceed to tackle a variety of standard problems centered on location and scale parameters, employing a robust analysis to present a range of mathematical insights.
Throughout the book, the authors illustrate the practical applications of Bayesian inference with a wealth of detailed examples, which serve to underscore the importance of this methodology in scientific contexts. By presenting the evidence of the Bayesian approach's efficacy through these examples, the authors enable readers to grasp the true value of this powerful analytical tool.
Bayesian Inference in Statistical Analysis is meticulously crafted to meet the needs of mathematicians and researchers alike, expertly balancing theory and application. Whether one seeks to employ the principles of the Bayesian approach in their own research or wishes to deepen their understanding of its significance in the scientific landscape, this insightful volume offers the ideal starting point.
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Authors: George E. P. Box, George C. Tiao
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Publisher: John Wiley & Sons
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Published Date: January 25, 2011
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Page Count: 608
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Print Type: BOOK
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Categories: Mathematics
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Maturity Rating: NOT_MATURE
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Language: en
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"Concepts and Programming in PyTorch" by Chitra Vasudevan is a comprehensive guide for those seeking to master the intricacies of this powerful deep learning framework. This book's aim is to provide a solid foundation in PyTorch, with an emphasis on detailed explanations, practical examples, and in-depth insights into core concepts and advanced topics. The book is perfect for both beginners and seasoned developers, as it covers everything from the basics of PyTorch to complex neural networks.
With 174 pages filled with clear and concise content, "Concepts and Programming in PyTorch" offers readers an abundance of worked-out coding examples, making the learning process highly self-explanatory and user-friendly. The author also includes real-world case studies and detailed explanations of essential concepts like CNN architecture and RNN architecture, which are crucial for the successful implementation of deep learning projects.
The book caters to a wide range of professionals and researchers, with content geared towards developers looking to integrate PyTorch into their work, as well as those seeking to strengthen their customer relationships by delivering superior value and getting an equitable return.
In summary, "Concepts and Programming in PyTorch" is an indispensable resource for anyone interested in deep learning and the fascinating world of machine learning. Don't miss out on this opportunity to hone your skills and bring your projects to life with the power of PyTorch!
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Authors: Chitra Vasudevan
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Publisher: BPB Publications
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Published Date: June 27, 2018
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Page Count: 174
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Print Type: BOOK
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Categories: Computers
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Maturity Rating: NOT_MATURE
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Language: en
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Learn to train and deploy neural network models in Python
"Deep Learning with PyTorch Quick Start Guide" is a fantastic entry-level resource for those eager to delve into the intriguing field of deep learning and harness the strength of the well-known PyTorch library. This in-depth book guides you through a hands-on experience, where you'll construct a convolutional neural network and a recurrent neural network, tackling issues such as image classification and natural language processing in real-world scenarios.
The text provides lucid and succinct explanations, enabling you to grasp the fundamental concepts of deep learning models while experiencing practical examples. As you advance, you'll learn to enhance your models using hyperparameters, operate with multiprocessor and distributed settings, and even predict text utilizing long short-term memory networks (LSTMs). By the book's conclusion, you'll possess a strong grasp of PyTorch's capabilities and the expertise required to train deep learning neural networks effortlessly.
Intended for both developers and data scientists, this captivating guide necessitates a fundamental knowledge of Python programming. However, prior experience with PyTorch is not obligatory. Embark on this exhilarating journey and unlock your potential within the thrilling realm of deep learning exploiting the superlative PyTorch library.
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Authors: David Julian
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Publisher: Packt Publishing Ltd
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Published Date: December 24, 2018
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Page Count: 150
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Print Type: BOOK
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Categories: Computers
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Maturity Rating: NOT_MATURE
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Language: en
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Build Intelligent Language Applications Using Deep Learning
Embark on a journey through the fascinating world of Natural Language Processing (NLP) with "Natural Language Processing with PyTorch" by Delip Rao and Brian McMahon. This insightful book, published by O'Reilly Media, provides developers and data scientists with a solid foundation in NLP and deep learning as they delve into the powerful capabilities of PyTorch, a Python-based deep learning library.
Navigating the ever-evolving landscape of AI, the authors help readers build intelligent language applications and explore how these cutting-edge techniques have transformed products such as Amazon Alexa and Google Translate. Through the book, readers will learn practical strategies for applying NLP and deep learning algorithms, mastering computational graphs, and embracing the supervised learning paradigm.
Discover the ins and outs of PyTorch's optimized tensor manipulation library and gain a comprehensive understanding of NLP basics, as well as fundamental concepts in neural network creation. Explore the intricacies of word, sentence, and document embedding to effectively capture rich text representations.
With hands-on, code-driven approaches and illustrative examples, "Natural Language Processing with PyTorch" guides readers through crucial aspects of sequence prediction, sequence-to-sequence models, and design patterns for constructing production-ready NLP systems. Experience the powerful synergy of NLP and deep learning through this compelling guide, paving the way for innovative solutions in the ever-evolving world of AI.
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Authors: Delip Rao, Brian McMahan
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Publisher: O'Reilly Media
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Published Date: January 22, 2019
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Page Count: 256
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Print Type: BOOK
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Categories: Computers
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Maturity Rating: NOT_MATURE
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Language: en
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Build Intelligent Language Applications Using Deep Learning
Dive into the fascinating world of Natural Language Processing (NLP) and unleash the full potential of artificial intelligence with PyTorch, an innovative Python-based deep learning library. In "Natural Language Processing with PyTorch: Build Intelligent Language Applications Using Deep Learning, " authors Delip Rao and Brian McMahon guide you through the practical application of NLP and deep learning techniques in a user-friendly and accessible manner.
This comprehensive guide, published by O'Reilly Media, Inc. , is perfect for developers and data scientists new to NLP and deep learning. With a solid grounding in NLP and deep learning algorithms, the book demonstrates the powerful combination of PyTorch and its optimized tensor manipulation library to help you build applications that tackle complex NLP problems.
As you progress through each chapter, you'll gain a deep understanding of computational graphs and the supervised learning paradigm, as well as traditional NLP concepts and methods. You'll learn the basics of building neural networks and master the art of representing words, sentences, documents, and other features using embeddings.
Explore sequence prediction and generate state-of-the-art sequence-to-sequence models, all while adhering to best design patterns for creating production-ready NLP systems. With engaging code examples and illustrations at every turn, "Natural Language Processing with PyTorch" is the ultimate resource for anyone looking to excel in NLP development.
Embrace the power of PyTorch and unlock the limitless possibilities of NLP. "Natural Language Processing with PyTorch" will inspire and guide you on your journey to developing cutting-edge AI applications.
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Authors: Delip Rao, Brian McMahan
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Publisher: "O'Reilly Media, Inc."
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Published Date: January 22, 2019
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Page Count: 256
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Print Type: BOOK
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Categories: Computers
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Average Rating: 5.0
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Ratings Count: 1.0
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Maturity Rating: NOT_MATURE
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Language: en
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A Problem-Solution Approach
If you're searching for a comprehensive guide to grasping the intricate deep learning concepts of PyTorch, look no further. PyTorch Recipes: A Problem-Solution Approach, authored by industry expert Pradeepta Mishra, delves into the nuances of PyTorch with unparalleled ease.
Beginning with an in-depth introduction to PyTorch, this book covers the fundamentals of a specific type of data structure known as tensors, which are utilized for calculating arithmetic operations. Following this, you'll venture into the captivating world of probability distributions and their underlying concepts. Subsequently, you'll explore transformations and graph computations using PyTorch.
Along the way, this book addresses common issues that arise with neural network implementation and tensor differentiation. However, never fear! PyTorch Recipes provides the most effective solutions to these challenges. Gain insights into how PyTorch integrates with supervised and unsupervised algorithms, and witness the remarkable power of convolutional neural networks, deep neural networks, and recurrent neural networks through practical examples.
As your journey continues, you'll unlock the potential of dynamic neural network architectures through natural language processing and text processing using PyTorch.
Ideal for readers eager to dive headfirst into coding with PyTorch, this book boasts 198 pages filled with thorough explanations. It stands as the ultimate guide to conquering PyTorch and unleashing your creativity in the realm of deep learning.
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Authors: Pradeepta Mishra
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Publisher: Apress
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Published Date: January 28, 2019
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Page Count: 198
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Print Type: BOOK
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Categories: Computers
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Maturity Rating: NOT_MATURE
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Language: en
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Demystify Neural Networks with Pytorch
Applied Deep Learning with Pytorch is a comprehensive guide to demystifying neural networks and their applications in solving complex real-world problems. Authored by Hyatt Saleh, this book is designed to be accessible to data scientists, data analysts, and developers who seek to enhance their skills in deep learning techniques.
From the basics to the intricacies, Applied Deep Learning with Pytorch covers essential concepts for effective deep learning implementations. It begins by introducing the reader to the fundamentals of deep learning and the powerful Pytorch library. With its engaging and concise style, readers can easily grasp the PyTorch syntax and efficiently build a single-layer neural network.
As the book progresses, it delves into more advanced neural network architectures such as convolutional neural networks (CNN) and recurrent neural networks (RNN). Readers will learn how to tackle diverse data problems, like image classification, text processing, and even style transfer, to name a few. By the end, you will find yourself empowered with the skills and confidence required to apply deep learning solutions to real-business scenarios.
This book is perfect for anyone who wishes to explore the profound potential of deep learning and enhance their computational skills for data management. A basic understanding of Python, familiarity with some machine learning concepts, and knowledge of NumPy and pandas are beneficial but not essential prerequisites.
Applied Deep Learning with Pytorch is a must-read for anyone who wants to take their deep learning journey to new heights and harness its power to solve some of the most challenging data problems of our time.
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Authors: Hyatt Saleh
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Published Date: April 26, 2019
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Page Count: 254
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Print Type: BOOK
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Categories: Computers
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Maturity Rating: NOT_MATURE
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Language: en
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Build CNNs, RNNs, GANs, reinforcement learning, and more, quickly and easily
PyTorch Deep Learning Hands-On is a quick and practical guide for engineers seeking to harness the immense power of PyTorch for implementing deep learning algorithms. This book takes you on a journey through the practical implementation of popular deep learning methods in PyTorch, making it an invaluable resource for those eager to dive into real-world deep learning workflows.
From the authors Sherin Thomas and Sudhanshu Passi, PyTorch Deep Learning Hands-On covers all key aspects of deep learning, including neural networks, computer vision, recurrent neural networks, generative adversarial networks, and reinforcement learning through hands-on projects. The book further explores deep learning workflows, model migration to TorchScript, and production deployment using advanced tools and principles.
While it does not delve into the deep learning theory, PyTorch Deep Learning Hands-On does offer essential insights for those interested in prototyping, testing, and deploying deep learning models efficiently. Perfect for machine learning engineers looking to enhance their deep learning toolbox with PyTorch, this book focuses on turning ideas into reality by integrating PyTorch into projects covering neural networks, computer vision, language processing, and more.
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Authors: Sherin Thomas, Sudhanshu Passi
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Publisher: Packt Publishing Ltd
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Published Date: April 30, 2019
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Page Count: 251
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Print Type: BOOK
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Categories: Computers
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Maturity Rating: NOT_MATURE
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Language: en
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Creating and Deploying Deep Learning Applications
Discover the power of deep learning with Ian Pointer's 'Programming PyTorch for Deep Learning'. This practical guide is designed to equip you with the essential concepts and techniques needed to leverage Facebook's PyTorch framework in today's rapidly changing technological landscape.
Dive into the world of artificial intelligence and machine learning as you explore the intricacies of Python's versatile PyTorch library. With detailed insights into image, sound, text, and other data processing, 'Programming PyTorch for Deep Learning' demonstrates the capabilities of this revolutionary tool.
The book takes you through the stages of setting up and using PyTorch on a cloud-based platform, allowing you to create neural architectures and train them on various types of data. Unlock the secrets of deep learning model deployment to production environments, immerse yourself in PyTorch use cases across industries, and apply transfer learning to images.
'Programming PyTorch for Deep Learning' goes further, exploring real-world applications of NLP techniques. Learn how to apply cutting-edge methodologies using a model trained on Wikipedia, and gain the opportunity to shape the future of deep learning in your own domain.
Experience the transformative potential of deep learning with Ian Pointer's comprehensive guide, 'Programming PyTorch for Deep Learning'. Whether you're a seasoned technician or a curious beginner, this invaluable resource awaits. So what are you waiting for? Dive into the world of deep learning today!
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Authors: Ian Pointer
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Publisher: O'Reilly Media
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Published Date: September 20, 2019
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Page Count: 220
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Print Type: BOOK
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Categories: Computers
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Maturity Rating: NOT_MATURE
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Language: en
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With Keras and PyTorch
Venture into the captivating domain of anomaly detection utilizing deep learning with Python, Keras, and PyTorch. Authored by Sridhar Alla and Suman Kalyan Adari, this beginner's guide demystifies the enigmatic world of anomaly detection and reveals its transformative impact on data scientists and machine learning professionals in today's dynamic technological realm.
Embark on a thorough and extensive voyage delving into the intricacies of statistical as well as traditional machine learning methods in Scikit-Learn. This exploration is complimented by a robust and in-depth introduction to deep learning. Hone your skills as you become adept in constructing and training deep learning models employing Keras and PyTorch. You will also encounter fascinating applications of various deep learning models for anomaly detection.
Unravel the intricacies of unsupervised and semi-supervised anomaly detection, in addition to time series-based anomaly detection. Upon conclusion of this enlightening journey, you will possess a comprehensive understanding of the fundamental principles of anomaly detection with a diverse array of methodologies to tackle this critical task.
Whether you are a data scientist, machine learning engineer, or just eager to broaden your horizons, "Starting Your Journey with Anomaly Detection in Deep Learning Using Python" marks the commencement of your mastery over this imperative skill in the realm of computer science. Don't let this remarkable chance to unlock the complete potential of anomaly detection with deep learning slip through your fingers!
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Authors: Sridhar Alla, Suman Kalyan Adari
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Publisher: Apress
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Published Date: October 10, 2019
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Page Count: 427
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Print Type: BOOK
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Categories: Computers
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Average Rating: 5.0
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Ratings Count: 1.0
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Maturity Rating: NOT_MATURE
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Language: en
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Over 60 recipes to design, develop, and deploy self-learning AI models using Python
Dive into the world of reinforcement learning (RL) with Python and PyTorch 1. x in the PyTorch 1. x Reinforcement Learning Cookbook! This book offers over 60 recipes that will help you master the techniques and algorithms necessary to develop self-learning AI models.
You'll master real-world examples and work through real-life projects, including solving the multi-armed bandit problem, cartpole problem, and even playing Atari games using Deep Q-Networks. Learn to select and build RL models, evaluate their performance, and deploy them in various applications.
Whether you are a machine learning engineer, data scientist, or AI researcher, this book provides quick solutions to different RL problems, making it an invaluable resource for anyone interested in the field. With a focus on PyTorch 1. x, you'll be ahead of the curve and ready to tackle complex reinforcement learning challenges in no time!
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Authors: Yuxi (Hayden) Liu
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Publisher: Packt Publishing Ltd
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Published Date: October 31, 2019
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Page Count: 334
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Print Type: BOOK
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Categories: Computers
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Maturity Rating: NOT_MATURE
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Language: en
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Unleash the power of deep learning with minimal math background and plenty of Python coding in "Deep Learning for Coders with fastai and PyTorch. " Authors Jeremy Howard and Sylvain Gugger, creators of the fastai library, provide a comprehensive guide for programmers to harness deep learning for a wide range of tasks. Gain a complete understanding of the algorithms behind the scenes, while exploring the latest techniques in computer vision, natural language processing, tabular data, and collaborative filtering.
Take your models to the next level by improving accuracy, speed, and reliability, and transform them into user-friendly web applications. Dive deep into the world of ethical considerations surrounding your work, and learn how to implement deep learning algorithms from scratch. Featuring a foreword by PyTorch cofounder Soumith Chintala, this book sets you on the path to mastering deep learning.
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Authors: Jeremy Howard, Sylvain Gugger
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Publisher: O'Reilly Media
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Published Date: June 29, 2020
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Page Count: 624
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Print Type: BOOK
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Categories: Computers
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Maturity Rating: NOT_MATURE
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Language: en
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Deep Learning with PyTorch: The Ultimate Guide to Unleashing the Power of Deep Learning with Python's Favorite Framework
Deep Learning with PyTorch is the ultimate guide for Python programmers interested in machine learning, covering the essentials of deep learning using PyTorch without requiring any prior experience with the framework. Authored by key contributors and the creator of PyTorch, this comprehensive book takes you on a journey through the core principles and best practices of deep learning and neural networks, from the basics to advanced techniques.
With Deep Learning with PyTorch, you will:
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Understand the foundations of deep learning data structures, such as tensors and neural networks.
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Master PyTorch's Tensor API, loading data in Python, and visualizing results.
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Learn how to implement modules and loss functions, as well as utilize pre-trained models from the PyTorch Hub.
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Discover methods for training networks with limited inputs and sifting through unreliable results for error diagnosis and improvement.
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Improve your neural network results with augmented data, better model architecture, and fine-tuning techniques.
Explore real-world applications of deep learning through the authors' stories and projects, such as early detection of lung cancer using PyTorch. Each chapter provides hands-on exercises in downloadable Jupyter notebooks for you to practice and gain confidence in your newfound skills.
Don't miss this opportunity to join the ever-growing community of deep learning enthusiasts and professionals who are embracing the future of artificial intelligence with PyTorch. Order your copy today and dive into the world of deep learning with PyTorch!
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Authors: Luca Pietro Giovanni Antiga, Eli Stevens, Thomas Viehmann
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Publisher: Simon and Schuster
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Published Date: July 01, 2020
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Page Count: 518
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Print Type: BOOK
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Categories: Computers
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Average Rating: 5.0
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Ratings Count: 1.0
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Maturity Rating: NOT_MATURE
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Language: en
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Build smart, AI-driven linguistic applications using deep learning and NLP techniques
Embark on a journey to unlock the potential of natural language processing with this comprehensive PyTorch 1. x guide. "Hands-On Natural Language Processing with PyTorch 1. x" is your go-to resource for mastering deep learning models and extracting valuable insights from structured and unstructured data.
From understanding the basics of PyTorch installation and utilizing CUDA for accelerated processing, to exploring the intricacies of word embeddings, CBOW, and tokenization, this book empowers you to engage with cutting-edge NLP techniques.
Unleash the power of transformative deep learning architectures, such as RNNs, LSTMs, and CNNs, as you build models to classify, translate, and conduct sentiment analysis on textual data. The captivating journey culminates in the creation of advanced NLP applications, including conversational chatbots.
Whether you are an NLP developer, a machine learning or deep learning pro, or simply someone interested in the limitless possibilities of building intelligent language applications, this book has something for everyone. Prerequisites include a working knowledge of Python programming and a basic understanding of NLP tasks.
Join the ranks of those who understand the ever-evolving nature of NLP and stay ahead of the curve with "Hands-On Natural Language Processing with PyTorch 1. x".
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Authors: Thomas Dop
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Publisher: Packt Publishing Ltd
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Published Date: July 09, 2020
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Page Count: 277
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Print Type: BOOK
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Categories: Computers
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Maturity Rating: NOT_MATURE
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Language: en
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Build deep neural networks and artificial intelligence applications with PyTorch
Discover the power of deep learning and artificial intelligence with "The Deep Learning with PyTorch Workshop. " This engaging guidebook by Hyatt Saleh, published by Packt Publishing Ltd, is designed for readers of all levels, whether you're diving into the world of deep learning for the first time or already have a solid understanding of Python.
Key features of the workshop include learning the fundamentals of deep learning and its applications, understanding PyTorch syntax, and mastering the intricacies of different neural network architectures such as convolutional, artificial, and recurrent networks. It even guides readers into complex challenges of data analysis and presents you with real-world data problems to solve!
This insightful book not only offers an in-depth into the world of deep learning, but also provides hands-on practical experiences in creating deep neural networks. By the end of it, you will be well-equipped to build your own intelligent applications powered by deep learning processes.
Whether you're interested in developing self-driving cars or voice-activated assistants, "The Deep Learning with PyTorch Workshop" is your ultimate resource, providing you with the essential concepts, tools, and libraries required to develop your deep learning skills with PyTorch. So grab your copy and let's dive into the deep end of deep learning!
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Authors: Hyatt Saleh
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Publisher: Packt Publishing Ltd
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Published Date: July 22, 2020
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Page Count: 329
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Print Type: BOOK
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Categories: Computers
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Maturity Rating: NOT_MATURE
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Language: en
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Explore deep learning concepts and implement over 50 real-world image applications
"Modern Computer Vision with PyTorch" is a comprehensive guide for beginners and intermediate machine learning practitioners seeking to master computer vision techniques using PyTorch deep learning. Co-authored by V Kishore Ayyadevara and Yeshwanth Reddy, this book delves into the practical application of over 50 real-world computer vision ideas, from image classification to facial expression swapping, via hands-on examples and code snippets.
The book begins by taking readers through neural network architecture implementation, and the use of convolutional neural networks and transfer learning. Subsequent chapters delve into the mechanics of 2D and 3D multi-object detection and segmentation, the nuances of human-pose estimation, and the intricacies of autoencoders and generative adversarial networks.
In addition to the exploration of numerous real-world use-cases, readers are also introduced to the combination of computer vision with NLP techniques, including LSTM, transformers, and deep learning. An intriguing part of this book is the manipulation of images, a feat achievable through CycleGAN, Pix2PixGAN, StyleGAN2, and SRGAN, which are covered in depth.
The authors also provide an in-depth understanding of combining computer vision with reinforcement learning. This part of the book presents the creation of agents that can play pong and drive a car autonomously.
The book's final section offers a step-by-step guide on deploying neural network models on AWS Cloud using FastAPI, Docker.
In total, "Modern Computer Vision with PyTorch" encapsulates the practicality, versatility, and power of deep learning, making it a must-read for anyone looking to upscale their computational skills within computer vision and AI.
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Authors: V Kishore Ayyadevara, Yeshwanth Reddy
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Publisher: Packt Publishing Ltd
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Published Date: November 27, 2020
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Page Count: 805
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Print Type: BOOK
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Categories: Computers
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Maturity Rating: NOT_MATURE
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Language: en
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Embark on an intriguing journey through the realm of deep learning with the help of "PyTorch Pocket Reference" by Joe Papa. This succinct guide offers a practical and easily accessible solution for professionals involved in research, development, and deployment tasks. With a comprehensive focus on essentials, Joe Papa ensures that you possess the vital skills to craft, refine, and enhance neural networks.
The "PyTorch Pocket Reference" is the ultimate resource for those eager to delve into deep learning research, machine learning engineering, or software development. Its meticulously arranged code examples simplify the understanding of PyTorch, enabling you to commence your work swiftly.
This invaluable reference guide introduces readers to the fundamental principles, making the basic syntax and design patterns effortlessly comprehensible. You'll also discover priceless tips on constructing custom models and data transformations, as well as training and deploying models using GPUs and TPUs.
Moreover, this book offers insights into expediting training via optimization techniques and distributed models. It provides access to the extensive library of PyTorch libraries and the diverse ecosystem that supports it. With "PyTorch Pocket Reference" at your disposal, you can deploy your code to production on platforms such as AWS, Google Cloud, and Azure, and create ML models suitable for mobile and edge devices.
In the pages of "PyTorch Pocket Reference," you'll find a treasure trove of 310 pages, brimming with indispensable information. This guide serves as the ultimate companion for anyone aiming to master deep learning with PyTorch, whether you're a novice, experienced professional, or anywhere in between. This book guarantees to enhance your development, all while saving you invaluable time.
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Authors: Joe Papa
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Publisher: "O'Reilly Media, Inc."
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Published Date: May 11, 2021
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Page Count: 310
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Print Type: BOOK
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Categories: Computers
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Maturity Rating: NOT_MATURE
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Language: en
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With PyTorch, TensorFlow and OpenAI Gym
Deep Reinforcement Learning with Python: With PyTorch, TensorFlow, and OpenAI Gym is a comprehensive guide for machine learning developers and architects looking to stay ahead in the rapidly evolving field of AI and deep learning. This book dives deep into deep reinforcement learning (DRL) using deep-q learning and policy gradient models, covering key concepts like Markov decision processes, Bellman equations, and dynamic programming.
From model-free learning to function approximation using neural networks and deep learning, you'll explore various DRL algorithms like deep q-networks, actor-critic methods, and policy-based methods. Through real-world implementations, you'll gain insights into the exploration vs exploitation dilemma and Monte Carlo tree search (MCTS), which played a pivotal role in AlphaGo's success.
Additionally, this book provides practical guidance on implementing DRL algorithms using popular deep learning frameworks like TensorFlow and PyTorch. It also presents numerous coding exercises to help solidify your understanding of deep reinforcement learning concepts. Don't miss your chance to stay ahead of the curve by jumping into the immersive world of deep reinforcement learning!
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Authors: Nimish Sanghi
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Publisher: Apress
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Published Date: June 12, 2021
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Page Count: 490
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Print Type: BOOK
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Categories: Computers
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Maturity Rating: NOT_MATURE
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Language: en
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Building and Deploying Deep Learning Models
PyTorch Pocket Reference, authored by Joe Papa, is an essential guide designed to streamline your deep learning model development and deployment processes. This compact, highly accessible reference book offers concise syntax explanations, design patterns, and relevant code snippets to help research scientists, machine learning engineers, and software developers easily dive into the world of PyTorch.
From data loading to customizing training loops and optimizing models, this comprehensive resource covers every aspect of neural network development. The author presents clear, well-structured PyTorch code to teach you how to deploy models on platforms such as Amazon Web Services (AWS), Google Cloud Platform (GCP), or Microsoft Azure. Furthermore, the book demonstrates how to implement models on mobile and edge devices.
Gain a solid understanding of PyTorch syntax and design patterns, and learn how to create custom models and data transforms. Develop and deploy models using a GPU or Tensor Processing Unit (TPU), and discover examples on training and testing a deep learning classifier. Additionally, discover methods to accelerate training by utilizing optimization techniques and distributed training.
Access the extensive PyTorch ecosystem and libraries to enhance your model's performance. With a strong emphasis on practical applications, this book serves as a valuable resource for researchers, engineers, and developers looking to develop deep learning applications and accelerate their research using PyTorch.
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Authors: Joe Papa
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Publisher: O'Reilly Media
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Published Date: September 14, 2021
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Page Count: 265
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Print Type: BOOK
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Maturity Rating: NOT_MATURE
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Language: en
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"This Day in June" is a heartfelt and colorful commemoration of the LGBTQ+ community. Authored by Gayle E. Pitman and published by the American Psychological Association, this empowering and instructive illustrated book invites young readers to appreciate and comprehend the happiness and fellowship embodied in a pride parade.
Despite its compact 38-page length, "This Day in June" resonates profoundly. The book comes with a Reading Guide brimming with insightful facts regarding LGBTQ+ history and culture, as well as a beneficial Note to Parents and Caregivers, which offers advice on conveying the concept of sexual orientation to kids.
In a world that values tolerance and comprehension, this book is a must-read for children and families seeking to instill a sense of belonging and togetherness. "This Day in June" serves as an excellent resource for illustrating the LGBTQ+ community to kids, while encouraging open dialogue and love for every individual.
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Authors: Gayle E. Pitman
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Publisher: American Psychological Association
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Published Date: December 22, 2021
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Page Count: 38
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Print Type: BOOK
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Categories: Juvenile Fiction
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Maturity Rating: NOT_MATURE
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Language: en
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Develop machine learning and deep learning models with Python
Embark on an enlightening journey in the world of machine learning and deep learning with "Machine Learning with PyTorch and Scikit-Learn" by Sebastian Raschka, Yuxi (Hayden) Liu, Vahid Mirjalili, and Dmytro Dzhulgakov. This essential guide, published by Packt Publishing Ltd, brings the best of scikit-learn and PyTorch frameworks to help you build advanced machine learning models with practical applications.
Whether you're a Python developer dipping your toes in machine learning for the first time or a data scientist wanting to master the latest developments, this comprehensive book covers all the essential concepts and techniques. With crystal-clear explanations, visualizations, and examples, it leaves no stone unturned in exploring the fundamentals of machine learning and deep learning, offering insights into the best practices and trends.
Key topics include applying machine learning principles with a strong theoretical foundation, understanding PyTorch framework, understanding transformers, XGBoost, graph neural networks, GANs for data generation, and reinforcement learning for intelligent agents. The book also delves into modern advancements such as graph neural networks and large-scale transformers employed in natural language processing (NLP).
"Machine Learning with PyTorch and Scikit-Learn" is your trusted companion as you traverse the landscape of machine learning and deep learning using Python. Whether you're coding your first machine learning classifier or seeking to expand your knowledge on cutting-edge approaches, this resource is the cornerstone of your learning journey.
Note: This book assumes a strong foundation in Python basics, calculus, and linear algebra, making it a suitable reference for anyone who is a Python practitioner or data scientist hoping to expand their machine learning knowledge.
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Authors: Sebastian Raschka, Yuxi (Hayden) Liu, Vahid Mirjalili, Dmytro Dzhulgakov
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Publisher: Packt Publishing Ltd
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Published Date: February 25, 2022
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Page Count: 775
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Print Type: BOOK
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Categories: Computers
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Maturity Rating: NOT_MATURE
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Language: en
Swiftly build high-performance Artificial Intelligence (AI) models using Python
Deep Learning with PyTorch Lightning is the perfect guide for building high-performance Artificial Intelligence (AI) models using the lightweight PyTorch Wrapper. Written by Kunal Sawarkar, this book will take you on a hands-on journey through PyTorch Lightning, maximizing productivity for your deep learning projects while ensuring full flexibility from model formulation through to implementation.
With this book, you'll gain a deep understanding of PyTorch Lightning architecture and learn how to implement it in various industry domains. You'll start by learning how to configure PyTorch Lightning on a cloud platform, understand its architectural components, and explore how they are configured to build various industry solutions.
As you progress, you'll build a network and application from scratch, seeing how you can expand it based on your specific needs. You'll also discover the knowledge and skills necessary to build and deploy your own scalable deep learning applications using PyTorch Lightning.
This book is ideal for citizen data scientists, expert data scientists transitioning from other frameworks to PyTorch Lightning, and deep learning researchers who are just getting started with coding for deep learning models using PyTorch Lightning. A working knowledge of Python programming and an intermediate-level understanding of statistics and deep learning fundamentals are expected.
Deepen your understanding of PyTorch Lightning and boost your productivity with Deep Learning with PyTorch Lightning.
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Authors: Kunal Sawarkar
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Publisher: Packt Publishing Ltd
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Published Date: April 29, 2022
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Page Count: 366
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Print Type: BOOK
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Categories: Computers
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Maturity Rating: NOT_MATURE
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Language: en
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"Que no te pese la tierra" is a suspenseful thriller that takes you on a rollercoaster ride of emotions. Inspector Gema Moral faces a series of challenges that threaten to derail her life and career. She's dealing with a strained family, a demanding boss, an unwieldy mortgage, and a case that could be her undoing.
As the investigation unfolds, Gema must confront her past and grapple with the consequences of her actions. This novel is a masterful blend of police procedural, psychological thriller, and character study, delving into themes of loss, redemption, and the lengths we'll go to protect what matters most. "Que no te pese la tierra" is a must-read for fans of intense, character-driven mysteries.
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Authors: Francisco Alcoba González
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Publisher: EDAF
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Published Date: October 18, 2022
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Page Count: 357
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Print Type: BOOK
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Categories: Fiction
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Maturity Rating: NOT_MATURE
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Language: es