Inside Deep Learning
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Inside Deep Learning
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Author : Edward Raff
language : en
Publisher: Simon and Schuster
Release Date : 2022-05-31
Inside Deep Learning written by Edward Raff 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 2022-05-31 with Computers categories.
Journey through the theory and practice of modern deep learning, and apply innovative techniques to solve everyday data problems. In Inside Deep Learning, you will learn how to: Implement deep learning with PyTorch Select the right deep learning components Train and evaluate a deep learning model Fine tune deep learning models to maximize performance Understand deep learning terminology Adapt existing PyTorch code to solve new problems Inside Deep Learning is an accessible guide to implementing deep learning with the PyTorch framework. It demystifies complex deep learning concepts and teaches you to understand the vocabulary of deep learning so you can keep pace in a rapidly evolving field. No detail is skipped--you'll dive into math, theory, and practical applications. Everything is clearly explained in plain English. About the Technology Deep learning doesn't have to be a black box! Knowing how your models and algorithms actually work gives you greater control over your results. And you don't have to be a mathematics expert or a senior data scientist to grasp what's going on inside a deep learning system. This book gives you the practical insight you need to understand and explain your work with confidence. About the Book Inside Deep Learning illuminates the inner workings of deep learning algorithms in a way that even machine learning novices can understand. You'll explore deep learning concepts and tools through plain language explanations, annotated code, and dozens of instantly useful PyTorch examples. Each type of neural network is clearly presented without complex math, and every solution in this book can run using readily available GPU hardware! What's Inside Select the right deep learning components Train and evaluate a deep learning model Fine tune deep learning models to maximize performance Understand deep learning terminology About the Reader For Python programmers with basic machine learning skills. About the Author Edward Raff is a Chief Scientist at Booz Allen Hamilton, and the author of the JSAT machine learning library. Quotes Pick up this book, and you won't be able to put it down. A rich, engaging knowledge base of deep learning math, algorithms, and models--just like the title says! - From the Foreword by Kirk Borne Ph.D., Chief Science Officer, DataPrime.ai The clearest and easiest book for learning deep learning principles and techniques I have ever read. The graphical representations for the algorithms are an eye-opening revelation. - Richard Vaughan, Purple Monkey Collective A great read for anyone interested in understanding the details of deep learning. - Vishwesh Ravi Shrimali, MBRDI.
Deep Learning Models
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Author : Jonah Gamba
language : en
Publisher: Springer Nature
Release Date : 2024-04-09
Deep Learning Models written by Jonah Gamba and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-04-09 with Computers categories.
This book focuses on and prioritizes a practical approach, minimizing theoretical concepts to deliver algorithms effectively. With deep learning emerging as a vibrant field of research and development in numerous industrial applications, there is a pressing need for accessible resources that provide comprehensive examples and quick guidance. Unfortunately, many existing books on the market tend to emphasize theoretical aspects, leaving newcomers scrambling for practical guidance. This book takes a different approach by focusing on practicality while keeping theoretical concepts to a necessary minimum. The book begins by laying a foundation of basic information on deep learning, gradually delving into the subject matter to explain and illustrate the limitations of existing algorithms. A dedicated chapter is allocated to evaluating the performance of multiple algorithms on specific datasets, highlighting techniques and strategies that can address real-world challenges when deep learning is employed. By consolidating all necessary information into a single resource, readers can bypass the hassle of scouring scattered online sources, gaining a one-stop solution to dive into deep learning for object detection and classification. To facilitate understanding, the book employs a rich array of illustrations, figures, tables, and code snippets. Comprehensive code examples are provided, empowering readers to grasp concepts quickly and develop practical solutions. The book covers essential methods and tools, ensuring a complete and comprehensive coverage that enables professionals to implement deep learning algorithms swiftly and effectively. This book is designed to equip professionals with the necessary skills to thrive in the active field of deep learning, where it has the potential to revolutionize traditional problem-solving approaches. This book serves as a practical companion, enabling readers to grasp concepts swiftly and embark on building practical solutions.
Handbook Of Research On Deep Learning Techniques For Cloud Based Industrial Iot
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Author : Swarnalatha, P.
language : en
Publisher: IGI Global
Release Date : 2023-07-03
Handbook Of Research On Deep Learning Techniques For Cloud Based Industrial Iot written by Swarnalatha, P. and has been published by IGI Global this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-07-03 with Computers categories.
Today’s business world is changing with the adoption of the internet of things (IoT). IoT is helping in prominently capturing a tremendous amount of data from multiple sources. Realizing the future and full potential of IoT devices will require an investment in new technologies. The Handbook of Research on Deep Learning Techniques for Cloud-Based Industrial IoT demonstrates how the computer scientists and engineers of today might employ artificial intelligence in practical applications with the emerging cloud and IoT technologies. The book also gathers recent research works in emerging artificial intelligence methods and applications for processing and storing the data generated from the cloud-based internet of things. Covering key topics such as data, cybersecurity, blockchain, and artificial intelligence, this premier reference source is ideal for industry professionals, engineers, computer scientists, researchers, scholars, academicians, practitioners, instructors, and students.
Deep Learning For Dummies
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Author : John Paul Mueller
language : en
Publisher: John Wiley & Sons
Release Date : 2019-05-14
Deep Learning For Dummies written by John Paul Mueller and has been published by John Wiley & Sons this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019-05-14 with Computers categories.
Take a deep dive into deep learning Deep learning provides the means for discerning patterns in the data that drive online business and social media outlets. Deep Learning for Dummies gives you the information you need to take the mystery out of the topic—and all of the underlying technologies associated with it. In no time, you’ll make sense of those increasingly confusing algorithms, and find a simple and safe environment to experiment with deep learning. The book develops a sense of precisely what deep learning can do at a high level and then provides examples of the major deep learning application types. Includes sample code Provides real-world examples within the approachable text Offers hands-on activities to make learning easier Shows you how to use Deep Learning more effectively with the right tools This book is perfect for those who want to better understand the basis of the underlying technologies that we use each and every day.
Deep Learning With Python Deep Learning Tutorial For Beginners
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Author : BYRON DAVES
language : en
Publisher: BYRON DAVES
Release Date :
Deep Learning With Python Deep Learning Tutorial For Beginners written by BYRON DAVES and has been published by BYRON DAVES this book supported file pdf, txt, epub, kindle and other format this book has been release on with Computers categories.
Deep Learning With Python | Deep Learning Tutorial For Beginners "Deep Learning with Python" will provide you with detailed and comprehensive knowledge of Deep Learning, How it came into emergence. The various subparts of Data Science, how they are related, and How Deep Learning is revolutionalizing the world we live in. What is Deep Learning Applications of Deep Learning Structure of Perceptron Demo: Perceptron from scratch What is a Neural Network ? Demo: Creating Deep Neural Nets
Math And Architectures Of Deep Learning
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Author : Krishnendu Chaudhury
language : en
Publisher: Simon and Schuster
Release Date : 2024-05-21
Math And Architectures Of Deep Learning written by Krishnendu Chaudhury 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 2024-05-21 with Computers categories.
Shine a spotlight into the deep learning “black box”. This comprehensive and detailed guide reveals the mathematical and architectural concepts behind deep learning models, so you can customize, maintain, and explain them more effectively. Inside Math and Architectures of Deep Learning you will find: Math, theory, and programming principles side by side Linear algebra, vector calculus and multivariate statistics for deep learning The structure of neural networks Implementing deep learning architectures with Python and PyTorch Troubleshooting underperforming models Working code samples in downloadable Jupyter notebooks The mathematical paradigms behind deep learning models typically begin as hard-to-read academic papers that leave engineers in the dark about how those models actually function. Math and Architectures of Deep Learning bridges the gap between theory and practice, laying out the math of deep learning side by side with practical implementations in Python and PyTorch. Written by deep learning expert Krishnendu Chaudhury, you’ll peer inside the “black box” to understand how your code is working, and learn to comprehend cutting-edge research you can turn into practical applications. Foreword by Prith Banerjee. About the technology Discover what’s going on inside the black box! To work with deep learning you’ll have to choose the right model, train it, preprocess your data, evaluate performance and accuracy, and deal with uncertainty and variability in the outputs of a deployed solution. This book takes you systematically through the core mathematical concepts you’ll need as a working data scientist: vector calculus, linear algebra, and Bayesian inference, all from a deep learning perspective. About the book Math and Architectures of Deep Learning teaches the math, theory, and programming principles of deep learning models laid out side by side, and then puts them into practice with well-annotated Python code. You’ll progress from algebra, calculus, and statistics all the way to state-of-the-art DL architectures taken from the latest research. What's inside The core design principles of neural networks Implementing deep learning with Python and PyTorch Regularizing and optimizing underperforming models About the reader Readers need to know Python and the basics of algebra and calculus. About the author Krishnendu Chaudhury is co-founder and CTO of the AI startup Drishti Technologies. He previously spent a decade each at Google and Adobe. Table of Contents 1 An overview of machine learning and deep learning 2 Vectors, matrices, and tensors in machine learning 3 Classifiers and vector calculus 4 Linear algebraic tools in machine learning 5 Probability distributions in machine learning 6 Bayesian tools for machine learning 7 Function approximation: How neural networks model the world 8 Training neural networks: Forward propagation and backpropagation 9 Loss, optimization, and regularization 10 Convolutions in neural networks 11 Neural networks for image classification and object detection 12 Manifolds, homeomorphism, and neural networks 13 Fully Bayes model parameter estimation 14 Latent space and generative modeling, autoencoders, and variational autoencoders A Appendix
Multi Disciplinary Trends In Artificial Intelligence
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Author : Olarik Surinta
language : en
Publisher: Springer Nature
Release Date : 2022-11-10
Multi Disciplinary Trends In Artificial Intelligence written by Olarik Surinta and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022-11-10 with Computers categories.
This book constitutes the refereed proceedings of the 15th International Conference on Multi-disciplinary Trends in Artificial Intelligence, MIWAI 2022, held online on November 17–19, 2022. The 14 full papers and 5 short papers presented were carefully reviewed and selected from 42 submissions.
Graph Neural Networks In Action
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Author : Keita Broadwater
language : en
Publisher: Simon and Schuster
Release Date : 2025-04-15
Graph Neural Networks In Action written by Keita Broadwater 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 2025-04-15 with Computers categories.
Graph Neural Networks in Action is a great guide about how to build cutting-edge graph neural networks and powerful deep learning models for recommendation engines, molecular modeling, and more. Ideal for Python programmers, you will dive into graph neural networks perfect for node prediction, link prediction, and graph classification.
Architecture In The Age Of Artificial Intelligence
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Author : Neil Leach
language : en
Publisher: Bloomsbury Publishing
Release Date : 2025-04-17
Architecture In The Age Of Artificial Intelligence written by Neil Leach and has been published by Bloomsbury Publishing this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-04-17 with Architecture categories.
AI has been unleashed. Nothing is going to be the same again. Updated to cover all the latest developments, Architecture in the Age of Artificial Intelligence introduces AI for designers and explores its seismic impact on the future of architecture and design. From ChatGPT and smart assistants to groundbreaking diffusion models for video and 3D modelling, this updated new edition investigates the profound effects of AI technologies on architectural practice. It explores how AI transforms every part of the process-from the inspiration and brief, to regulations and copyright, to performance-driven design- and looks beyond discussions of software and functionality to ask more fundamental questions too: How did AI evolve? How does it work? What does it tell us about creativity? And what does it mean for the very future of the profession itself? Written by one of the world's leading experts in the field, this book is a must-read for all architects wishing to stay at the forefront of the AI revolution.
Deep Learning With Pytorch
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Author : Jason Brownlee
language : en
Publisher: Machine Learning Mastery
Release Date : 2023-03-21
Deep Learning With Pytorch written by Jason Brownlee and has been published by Machine Learning Mastery this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-03-21 with Computers categories.
Deep learning is currently the most interesting and powerful machine learning technique. PyTorch is one of the dominant libraries for deep learning in the Python ecosystem and is widely used in research. With PyTorch, you can easily tap into the power of deep learning with just a few lines of code. Many deep learning models are created in PyTorch. Therefore, knowing PyTorch opens the door for you to leverage the power of deep learning. This Ebook is written in the friendly Machine Learning Mastery style that you’re used to, learn exactly how to get started and apply deep learning to your own machine learning projects.