Deep Learning Techniques Designing Next Generation Machine Intelligence Algorithms
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Fundamentals Of Deep Learning
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Author : Nikhil Buduma
language : en
Publisher: "O'Reilly Media, Inc."
Release Date : 2017-05-25
Fundamentals Of Deep Learning written by Nikhil Buduma and has been published by "O'Reilly Media, Inc." this book supported file pdf, txt, epub, kindle and other format this book has been release on 2017-05-25 with Computers categories.
With the reinvigoration of neural networks in the 2000s, deep learning has become an extremely active area of research, one that’s paving the way for modern machine learning. In this practical book, author Nikhil Buduma provides examples and clear explanations to guide you through major concepts of this complicated field. Companies such as Google, Microsoft, and Facebook are actively growing in-house deep-learning teams. For the rest of us, however, deep learning is still a pretty complex and difficult subject to grasp. If you’re familiar with Python, and have a background in calculus, along with a basic understanding of machine learning, this book will get you started. Examine the foundations of machine learning and neural networks Learn how to train feed-forward neural networks Use TensorFlow to implement your first neural network Manage problems that arise as you begin to make networks deeper Build neural networks that analyze complex images Perform effective dimensionality reduction using autoencoders Dive deep into sequence analysis to examine language Learn the fundamentals of reinforcement learning
Deep Learning Techniques Designing Next Generation Machine Intelligence Algorithms
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Author : Dr. V.S. Manjula
language : en
Publisher: OrangeBooks Publication
Release Date : 2023-10-13
Deep Learning Techniques Designing Next Generation Machine Intelligence Algorithms written by Dr. V.S. Manjula and has been published by OrangeBooks Publication this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-10-13 with Education categories.
I am Dr. V. S. Manjula has Completed B.Sc. MCA, M.Phil., B.Ed.(CS), Ph.D. and I have a total 23 years experienced in teaching & administration work and received Ph.D. degree in Computer Science from Bharathiar University in 2013. At present, I am working as a Professor, at the Department of Computer Science, School of Mathematics and Computing in Kampala International University, Kampala, Uganda, East Africa. Previously I worked as an Associate Professor at Wollo University in the Department of Computer Science under the College of Informatics, Kombolcha Institute of Technology, Kombolcha, Ethiopia, and East Africa and I worked as an Associate Professor & HOD in the Department of Computer Science and Engineering & Information Technology in St. Joseph University College of Engineering & Technology, Dar-Es-Salaam in Tanzania, East Africa. I Worked as HOD in the Master of Computer Applications (MCA) Department, at Gurushree Shantivijai Jain College, the Best College in Chennai. I am appointed foreign external examiner evaluating PHD Thesis for various Universities in India & Abroad and a member of the Research Journal of the International Association of Computer Science & Information Technology (IACSIT) & Member of IAENG (International Association of Engineers) – USA Member No: 143718. I am JASIC International Journal Managing Journal Editing Board Member at Kampala International University, Uganda, East Africa. I have published in more than 25 International Journals and National & International Conferences.
Python Deep Learning
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Author : Valentino Zocca
language : en
Publisher:
Release Date : 2017-04-28
Python Deep Learning written by Valentino Zocca and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2017-04-28 with Machine learning categories.
Take your machine learning skills to the next level by mastering Deep Learning concepts and algorithms using Python.About This Book* Explore and create intelligent systems using cutting-edge deep learning techniques* Implement deep learning algorithms and work with revolutionary libraries in Python* Get real-world examples and easy-to-follow tutorials on Theano, TensorFlow, H2O and moreWho This Book Is ForThis book is for Data Science practitioners as well as aspirants who have a basic foundational understanding of Machine Learning concepts and some programming experience with Python. A mathematical background with a conceptual understanding of calculus and statistics is also desired.What You Will Learn* Get a practical deep dive into deep learning algorithms* Explore deep learning further with Theano, Caffe, Keras, and TensorFlow* Learn about two of the most powerful techniques at the core of many practical deep learning implementations: Auto-Encoders and Restricted Boltzmann Machines* Dive into Deep Belief Nets and Deep Neural Networks* Discover more deep learning algorithms with Dropout and Convolutional Neural Networks* Get to know device strategies so you can use deep learning algorithms and libraries in the real worldIn DetailWith an increasing interest in AI around the world, deep learning has attracted a great deal of public attention. Every day, deep learning algorithms are used broadly across different industries.The book will give you all the practical information available on the subject, including the best practices, using real-world use cases. You will learn to recognize and extract information to increase predictive accuracy and optimize results.Starting with a quick recap of important machine learning concepts, the book will delve straight into deep learning principles using Sci-kit learn. Moving ahead, you will learn to use the latest open source libraries such as Theano, Keras, Google's TensorFlow, and H20. Use this guide to uncover the difficulties of pattern recognition, scaling data with greater accuracy and discussing deep learning algorithms and techniques.Whether you want to dive deeper into Deep Learning, or want to investigate how to get more out of this powerful technology, you'll find everything inside.Style and approachPython Machine Learning by example follows practical hands on approach. It walks you through the key elements of Python and its powerful machine learning libraries with the help of real world projects.
Fundamentals Of Deep Learning
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Author : Nithin Buduma
language : en
Publisher: "O'Reilly Media, Inc."
Release Date : 2022-05-16
Fundamentals Of Deep Learning written by Nithin Buduma and has been published by "O'Reilly Media, Inc." this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022-05-16 with Computers categories.
We're in the midst of an AI research explosion. Deep learning has unlocked superhuman perception to power our push toward creating self-driving vehicles, defeating human experts at a variety of difficult games including Go, and even generating essays with shockingly coherent prose. But deciphering these breakthroughs often takes a PhD in machine learning and mathematics. The updated second edition of this book describes the intuition behind these innovations without jargon or complexity. Python-proficient programmers, software engineering professionals, and computer science majors will be able to reimplement these breakthroughs on their own and reason about them with a level of sophistication that rivals some of the best developers in the field. Learn the mathematics behind machine learning jargon Examine the foundations of machine learning and neural networks Manage problems that arise as you begin to make networks deeper Build neural networks that analyze complex images Perform effective dimensionality reduction using autoencoders Dive deep into sequence analysis to examine language Explore methods in interpreting complex machine learning models Gain theoretical and practical knowledge on generative modeling Understand the fundamentals of reinforcement learning
Deep Learning From Scratch
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Author : Seth Weidman
language : en
Publisher: O'Reilly Media
Release Date : 2019-09-09
Deep Learning From Scratch written by Seth Weidman and has been published by O'Reilly Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019-09-09 with Computers categories.
With the resurgence of neural networks in the 2010s, deep learning has become essential for machine learning practitioners and even many software engineers. This book provides a comprehensive introduction for data scientists and software engineers with machine learning experience. You’ll start with deep learning basics and move quickly to the details of important advanced architectures, implementing everything from scratch along the way. Author Seth Weidman shows you how neural networks work using a first principles approach. You’ll learn how to apply multilayer neural networks, convolutional neural networks, and recurrent neural networks from the ground up. With a thorough understanding of how neural networks work mathematically, computationally, and conceptually, you’ll be set up for success on all future deep learning projects. This book provides: Extremely clear and thorough mental models—accompanied by working code examples and mathematical explanations—for understanding neural networks Methods for implementing multilayer neural networks from scratch, using an easy-to-understand object-oriented framework Working implementations and clear-cut explanations of convolutional and recurrent neural networks Implementation of these neural network concepts using the popular PyTorch framework
Advanced Deep Learning With Python
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Author : Ivan Vasilev
language : en
Publisher: Packt Publishing Ltd
Release Date : 2019-12-12
Advanced Deep Learning With Python written by Ivan Vasilev 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 2019-12-12 with Computers categories.
Gain expertise in advanced deep learning domains such as neural networks, meta-learning, graph neural networks, and memory augmented neural networks using the Python ecosystem Key FeaturesGet to grips with building faster and more robust deep learning architecturesInvestigate and train convolutional neural network (CNN) models with GPU-accelerated libraries such as TensorFlow and PyTorchApply deep neural networks (DNNs) to computer vision problems, NLP, and GANsBook Description In order to build robust deep learning systems, you’ll need to understand everything from how neural networks work to training CNN models. In this book, you’ll discover newly developed deep learning models, methodologies used in the domain, and their implementation based on areas of application. You’ll start by understanding the building blocks and the math behind neural networks, and then move on to CNNs and their advanced applications in computer vision. You'll also learn to apply the most popular CNN architectures in object detection and image segmentation. Further on, you’ll focus on variational autoencoders and GANs. You’ll then use neural networks to extract sophisticated vector representations of words, before going on to cover various types of recurrent networks, such as LSTM and GRU. You’ll even explore the attention mechanism to process sequential data without the help of recurrent neural networks (RNNs). Later, you’ll use graph neural networks for processing structured data, along with covering meta-learning, which allows you to train neural networks with fewer training samples. Finally, you’ll understand how to apply deep learning to autonomous vehicles. By the end of this book, you’ll have mastered key deep learning concepts and the different applications of deep learning models in the real world. What you will learnCover advanced and state-of-the-art neural network architecturesUnderstand the theory and math behind neural networksTrain DNNs and apply them to modern deep learning problemsUse CNNs for object detection and image segmentationImplement generative adversarial networks (GANs) and variational autoencoders to generate new imagesSolve natural language processing (NLP) tasks, such as machine translation, using sequence-to-sequence modelsUnderstand DL techniques, such as meta-learning and graph neural networksWho this book is for This book is for data scientists, deep learning engineers and researchers, and AI developers who want to further their knowledge of deep learning and build innovative and unique deep learning projects. Anyone looking to get to grips with advanced use cases and methodologies adopted in the deep learning domain using real-world examples will also find this book useful. Basic understanding of deep learning concepts and working knowledge of the Python programming language is assumed.
Automated Design Of Machine Learning And Search Algorithms
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Author : Nelishia Pillay
language : en
Publisher: Springer Nature
Release Date : 2021-07-28
Automated Design Of Machine Learning And Search Algorithms written by Nelishia Pillay and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-07-28 with Computers categories.
This book presents recent advances in automated machine learning (AutoML) and automated algorithm design and indicates the future directions in this fast-developing area. Methods have been developed to automate the design of neural networks, heuristics and metaheuristics using techniques such as metaheuristics, statistical techniques, machine learning and hyper-heuristics. The book first defines the field of automated design, distinguishing it from the similar but different topics of automated algorithm configuration and automated algorithm selection. The chapters report on the current state of the art by experts in the field and include reviews of AutoML and automated design of search, theoretical analyses of automated algorithm design, automated design of control software for robot swarms, and overfitting as a benchmark and design tool. Also covered are automated generation of constructive and perturbative low-level heuristics, selection hyper-heuristics for automated design, automated design of deep-learning approaches using hyper-heuristics, genetic programming hyper-heuristics with transfer knowledge and automated design of classification algorithms. The book concludes by examining future research directions of this rapidly evolving field. The information presented here will especially interest researchers and practitioners in the fields of artificial intelligence, computational intelligence, evolutionary computation and optimisation.
Deep Learning Algorithms And Applications
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Author : Witold Pedrycz
language : en
Publisher: Springer Nature
Release Date : 2019-10-23
Deep Learning Algorithms And Applications written by Witold Pedrycz and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019-10-23 with Technology & Engineering categories.
This book presents a wealth of deep-learning algorithms and demonstrates their design process. It also highlights the need for a prudent alignment with the essential characteristics of the nature of learning encountered in the practical problems being tackled. Intended for readers interested in acquiring practical knowledge of analysis, design, and deployment of deep learning solutions to real-world problems, it covers a wide range of the paradigm’s algorithms and their applications in diverse areas including imaging, seismic tomography, smart grids, surveillance and security, and health care, among others. Featuring systematic and comprehensive discussions on the development processes, their evaluation, and relevance, the book offers insights into fundamental design strategies for algorithms of deep learning.
Deep Learning
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Author : Josh Patterson
language : en
Publisher: "O'Reilly Media, Inc."
Release Date : 2017-07-28
Deep Learning written by Josh Patterson and has been published by "O'Reilly Media, Inc." this book supported file pdf, txt, epub, kindle and other format this book has been release on 2017-07-28 with Computers categories.
Although interest in machine learning has reached a high point, lofty expectations often scuttle projects before they get very far. How can machine learning—especially deep neural networks—make a real difference in your organization? This hands-on guide not only provides the most practical information available on the subject, but also helps you get started building efficient deep learning networks. Authors Adam Gibson and Josh Patterson provide theory on deep learning before introducing their open-source Deeplearning4j (DL4J) library for developing production-class workflows. Through real-world examples, you’ll learn methods and strategies for training deep network architectures and running deep learning workflows on Spark and Hadoop with DL4J. Dive into machine learning concepts in general, as well as deep learning in particular Understand how deep networks evolved from neural network fundamentals Explore the major deep network architectures, including Convolutional and Recurrent Learn how to map specific deep networks to the right problem Walk through the fundamentals of tuning general neural networks and specific deep network architectures Use vectorization techniques for different data types with DataVec, DL4J’s workflow tool Learn how to use DL4J natively on Spark and Hadoop
Deep Learning And Parallel Computing Environment For Bioengineering Systems
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Author : Arun Kumar Sangaiah
language : en
Publisher: Academic Press
Release Date : 2019-07-26
Deep Learning And Parallel Computing Environment For Bioengineering Systems written by Arun Kumar Sangaiah and has been published by Academic Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019-07-26 with Technology & Engineering categories.
Deep Learning and Parallel Computing Environment for Bioengineering Systems delivers a significant forum for the technical advancement of deep learning in parallel computing environment across bio-engineering diversified domains and its applications. Pursuing an interdisciplinary approach, it focuses on methods used to identify and acquire valid, potentially useful knowledge sources. Managing the gathered knowledge and applying it to multiple domains including health care, social networks, mining, recommendation systems, image processing, pattern recognition and predictions using deep learning paradigms is the major strength of this book. This book integrates the core ideas of deep learning and its applications in bio engineering application domains, to be accessible to all scholars and academicians. The proposed techniques and concepts in this book can be extended in future to accommodate changing business organizations' needs as well as practitioners' innovative ideas. - Presents novel, in-depth research contributions from a methodological/application perspective in understanding the fusion of deep machine learning paradigms and their capabilities in solving a diverse range of problems - Illustrates the state-of-the-art and recent developments in the new theories and applications of deep learning approaches applied to parallel computing environment in bioengineering systems - Provides concepts and technologies that are successfully used in the implementation of today's intelligent data-centric critical systems and multi-media Cloud-Big data