Deep Learning Architectures
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Deep Learning Architectures
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Author : Ovidiu Calin
language : en
Publisher: Springer Nature
Release Date : 2020-02-13
Deep Learning Architectures written by Ovidiu Calin and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-02-13 with Mathematics categories.
This book describes how neural networks operate from the mathematical point of view. As a result, neural networks can be interpreted both as function universal approximators and information processors. The book bridges the gap between ideas and concepts of neural networks, which are used nowadays at an intuitive level, and the precise modern mathematical language, presenting the best practices of the former and enjoying the robustness and elegance of the latter. This book can be used in a graduate course in deep learning, with the first few parts being accessible to senior undergraduates. In addition, the book will be of wide interest to machine learning researchers who are interested in a theoretical understanding of the subject.
Hands On Deep Learning Architectures With Python
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Author : Yuxi (Hayden) Liu
language : en
Publisher: Packt Publishing Ltd
Release Date : 2019-04-30
Hands On Deep Learning Architectures With Python written by Yuxi (Hayden) Liu and has been published by Packt Publishing Ltd this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019-04-30 with Computers categories.
Concepts, tools, and techniques to explore deep learning architectures and methodologies Key FeaturesExplore advanced deep learning architectures using various datasets and frameworksImplement deep architectures for neural network models such as CNN, RNN, GAN, and many moreDiscover design patterns and different challenges for various deep learning architecturesBook Description Deep learning architectures are composed of multilevel nonlinear operations that represent high-level abstractions; this allows you to learn useful feature representations from the data. This book will help you learn and implement deep learning architectures to resolve various deep learning research problems. Hands-On Deep Learning Architectures with Python explains the essential learning algorithms used for deep and shallow architectures. Packed with practical implementations and ideas to help you build efficient artificial intelligence systems (AI), this book will help you learn how neural networks play a major role in building deep architectures. You will understand various deep learning architectures (such as AlexNet, VGG Net, GoogleNet) with easy-to-follow code and diagrams. In addition to this, the book will also guide you in building and training various deep architectures such as the Boltzmann mechanism, autoencoders, convolutional neural networks (CNNs), recurrent neural networks (RNNs), natural language processing (NLP), GAN, and more—all with practical implementations. By the end of this book, you will be able to construct deep models using popular frameworks and datasets with the required design patterns for each architecture. You will be ready to explore the potential of deep architectures in today's world. What you will learnImplement CNNs, RNNs, and other commonly used architectures with PythonExplore architectures such as VGGNet, AlexNet, and GoogLeNetBuild deep learning architectures for AI applications such as face and image recognition, fraud detection, and many moreUnderstand the architectures and applications of Boltzmann machines and autoencoders with concrete examples Master artificial intelligence and neural network concepts and apply them to your architectureUnderstand deep learning architectures for mobile and embedded systemsWho this book is for If you’re a data scientist, machine learning developer/engineer, or deep learning practitioner, or are curious about AI and want to upgrade your knowledge of various deep learning architectures, this book will appeal to you. You are expected to have some knowledge of statistics and machine learning algorithms to get the best out of this book
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
Elements Of Deep Learning For Computer Vision
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Author : Bharat Sikka
language : en
Publisher: BPB Publications
Release Date : 2021-06-24
Elements Of Deep Learning For Computer Vision written by Bharat Sikka and has been published by BPB Publications this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-06-24 with Computers categories.
Conceptualizing deep learning in computer vision applications using PyTorch and Python libraries. KEY FEATURES ● Covers a variety of computer vision projects, including face recognition and object recognition such as Yolo, Faster R-CNN. ● Includes graphical representations and illustrations of neural networks and teaches how to program them. ● Includes deep learning techniques and architectures introduced by Microsoft, Google, and the University of Oxford. DESCRIPTION Elements of Deep Learning for Computer Vision gives a thorough understanding of deep learning and provides highly accurate computer vision solutions while using libraries like PyTorch. This book introduces you to Deep Learning and explains all the concepts required to understand the basic working, development, and tuning of a neural network using Pytorch. The book then addresses the field of computer vision using two libraries, including the Python wrapper/version of OpenCV and PIL. After establishing and understanding both the primary concepts, the book addresses them together by explaining Convolutional Neural Networks(CNNs). CNNs are further elaborated using top industry standards and research to explain how they provide complicated Object Detection in images and videos, while also explaining their evaluation. Towards the end, the book explains how to develop a fully functional object detection model, including its deployment over APIs. By the end of this book, you are well-equipped with the role of deep learning in the field of computer vision along with a guided process to design deep learning solutions. WHAT YOU WILL LEARN ● Get to know the mechanism of deep learning and how neural networks operate. ● Learn to develop a highly accurate neural network model. ● Access to rich Python libraries to address computer vision challenges. ● Build deep learning models using PyTorch and learn how to deploy using the API. ● Learn to develop Object Detection and Face Recognition models along with their deployment. WHO THIS BOOK IS FOR This book is for the readers who aspire to gain a strong fundamental understanding of how to infuse deep learning into computer vision and image processing applications. Readers are expected to have intermediate Python skills. No previous knowledge of PyTorch and Computer Vision is required. TABLE OF CONTENTS 1. An Introduction to Deep Learning 2. Supervised Learning 3. Gradient Descent 4. OpenCV with Python 5. Python Imaging Library and Pillow 6. Introduction to Convolutional Neural Networks 7. GoogLeNet, VGGNet, and ResNet 8. Understanding Object Detection 9. Popular Algorithms for Object Detection 10. Faster RCNN with PyTorch and YoloV4 with Darknet 11. Comparing Algorithms and API Deployment with Flask 12. Applications in Real World
Hardware Architectures For Deep Learning
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Author : Masoud Daneshtalab
language : en
Publisher: Institution of Engineering and Technology
Release Date : 2020-02-28
Hardware Architectures For Deep Learning written by Masoud Daneshtalab and has been published by Institution of Engineering and Technology this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-02-28 with Computers categories.
This book presents and discusses innovative ideas in the design, modelling, implementation, and optimization of hardware platforms for neural networks.
Applied Machine Learning And Deep Learning Architectures And Techniques
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Author : Nitin Liladhar Rane
language : en
Publisher: Deep Science Publishing
Release Date : 2024-10-13
Applied Machine Learning And Deep Learning Architectures And Techniques written by Nitin Liladhar Rane and has been published by Deep Science Publishing this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-10-13 with Computers categories.
This book provides an extensive overview of recent advances in machine learning (ML) and deep learning (DL). It starts with a comprehensive introduction to the latest architectural and design practices, with an overview of basic techniques and optimization algorithms and methodologies that are fundamental to modern ML/DL development followed by the tools and frameworks that are driving innovation in ML/DL. The presentation then points to the central position of ML and DL in developing generative AI like ChatGPT. Then look at different industrial applications such as explaining the real-world impacts of each. This includes challenges around corroborate artificial Intelligence (AI), and trustworthy AI, and so on. Finally, the book presents a futuristic vision on the potentials and implications of future ML and DL architectures, making it an ideal guide for researchers, practitioners and industry professionals. This book will be a significant resource for comprehending present advancements, addressing encounter challenges, and traversing the ML and DL landscape in future, making it an indispensable reference for anyone interested in applying these technologies across sectors.
Deep In Memory Architectures For Machine Learning
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Author : Mingu Kang
language : en
Publisher: Springer Nature
Release Date : 2020-01-30
Deep In Memory Architectures For Machine Learning written by Mingu Kang and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-01-30 with Technology & Engineering categories.
This book describes the recent innovation of deep in-memory architectures for realizing AI systems that operate at the edge of energy-latency-accuracy trade-offs. From first principles to lab prototypes, this book provides a comprehensive view of this emerging topic for both the practicing engineer in industry and the researcher in academia. The book is a journey into the exciting world of AI systems in hardware.
Advances In Deep Learning
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Author : M. Arif Wani
language : en
Publisher: Springer
Release Date : 2019-03-14
Advances In Deep Learning written by M. Arif Wani and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019-03-14 with Computers categories.
This book introduces readers to both basic and advanced concepts in deep network models. It covers state-of-the-art deep architectures that many researchers are currently using to overcome the limitations of the traditional artificial neural networks. Various deep architecture models and their components are discussed in detail, and subsequently illustrated by algorithms and selected applications. In addition, the book explains in detail the transfer learning approach for faster training of deep models; the approach is also demonstrated on large volumes of fingerprint and face image datasets. In closing, it discusses the unique set of problems and challenges associated with these models.
The Principles Of Deep Learning Theory
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Author : Daniel A. Roberts
language : en
Publisher: Cambridge University Press
Release Date : 2022-05-26
The Principles Of Deep Learning Theory written by Daniel A. Roberts and has been published by Cambridge University Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022-05-26 with Computers categories.
This volume develops an effective theory approach to understanding deep neural networks of practical relevance.
Deep Learning Applications And Intelligent Decision Making In Engineering
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Author : Karthikrajan Senthilnathan
language : en
Publisher:
Release Date : 2020
Deep Learning Applications And Intelligent Decision Making In Engineering written by Karthikrajan Senthilnathan and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020 with Computational intelligence categories.
"This book explores the application of deep learning in building a smart world, ranging from smart cities, smart agriculture to smart homes"--