Fundamentals of Deep Learning

Fundamentals of Deep Learning

Author: Nikhil Buduma

Publisher: "O'Reilly Media, Inc."

Published: 2017-05-25

Total Pages: 298

ISBN-13: 1491925582

DOWNLOAD EBOOK

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


Book Synopsis Fundamentals of Deep Learning by : Nikhil Buduma

Download or read book Fundamentals of Deep Learning written by Nikhil Buduma and published by "O'Reilly Media, Inc.". This book was released on 2017-05-25 with total page 298 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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

Fundamentals of Deep Learning

Fundamentals of Deep Learning

Author: Nikhil Buduma

Publisher:

Published: 2017

Total Pages:

ISBN-13: 9781491925607

DOWNLOAD EBOOK


Book Synopsis Fundamentals of Deep Learning by : Nikhil Buduma

Download or read book Fundamentals of Deep Learning written by Nikhil Buduma and published by . This book was released on 2017 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt:

Deep Learning Techniques (Designing Next-Generation Machine Intelligence Algorithms)

Deep Learning Techniques (Designing Next-Generation Machine Intelligence Algorithms)

Author: Dr. V.S. Manjula

Publisher: OrangeBooks Publication

Published: 2023-10-13

Total Pages: 436

ISBN-13:

DOWNLOAD EBOOK

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.


Book Synopsis Deep Learning Techniques (Designing Next-Generation Machine Intelligence Algorithms) by : Dr. V.S. Manjula

Download or read book Deep Learning Techniques (Designing Next-Generation Machine Intelligence Algorithms) written by Dr. V.S. Manjula and published by OrangeBooks Publication. This book was released on 2023-10-13 with total page 436 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.

Fundamentals of Deep Learning

Fundamentals of Deep Learning

Author: Nikhil Buduma

Publisher:

Published: 2017

Total Pages: 0

ISBN-13:

DOWNLOAD EBOOK

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


Book Synopsis Fundamentals of Deep Learning by : Nikhil Buduma

Download or read book Fundamentals of Deep Learning written by Nikhil Buduma and published by . This book was released on 2017 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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

Fundamental of Deep Learning

Fundamental of Deep Learning

Author: Nikhil Buduma

Publisher:

Published: 2017

Total Pages: 283

ISBN-13:

DOWNLOAD EBOOK


Book Synopsis Fundamental of Deep Learning by : Nikhil Buduma

Download or read book Fundamental of Deep Learning written by Nikhil Buduma and published by . This book was released on 2017 with total page 283 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Fundamentals of Deep Learning

Fundamentals of Deep Learning

Author: Nikhil Buduma

Publisher: O'Reilly Media

Published: 2021-09-30

Total Pages: 450

ISBN-13: 9781492082187

DOWNLOAD EBOOK

We're in the midst of an AI research explosion. Deep learning has unlocked superhuman perception that has powered our push toward self-driving vehicles, the ability to defeat human experts at a variety of difficult games including Go and Starcraft, and even generate essays with shockingly coherent prose. But deciphering these breakthroughs often takes a Ph.D. education in machine learning and mathematics. This updated second edition describes the intuition behind these innovations without the jargon and complexity. By the end of this book, Python-proficient programmers, software engineering professionals, and computer science majors will be able to re-implement these breakthroughs on their own and reason about them with a level of sophistication that rivals some of the best in the field. New chapters cover recent advancements in the fields of generative modeling and interpretability. Code examples throughout the book are updated to TensorFlow 2 and PyTorch 1.4.


Book Synopsis Fundamentals of Deep Learning by : Nikhil Buduma

Download or read book Fundamentals of Deep Learning written by Nikhil Buduma and published by O'Reilly Media. This book was released on 2021-09-30 with total page 450 pages. Available in PDF, EPUB and Kindle. Book excerpt: We're in the midst of an AI research explosion. Deep learning has unlocked superhuman perception that has powered our push toward self-driving vehicles, the ability to defeat human experts at a variety of difficult games including Go and Starcraft, and even generate essays with shockingly coherent prose. But deciphering these breakthroughs often takes a Ph.D. education in machine learning and mathematics. This updated second edition describes the intuition behind these innovations without the jargon and complexity. By the end of this book, Python-proficient programmers, software engineering professionals, and computer science majors will be able to re-implement these breakthroughs on their own and reason about them with a level of sophistication that rivals some of the best in the field. New chapters cover recent advancements in the fields of generative modeling and interpretability. Code examples throughout the book are updated to TensorFlow 2 and PyTorch 1.4.

Python Deep Learning

Python Deep Learning

Author: Valentino Zocca

Publisher: Packt Publishing Ltd

Published: 2017-04-28

Total Pages: 406

ISBN-13: 1786460661

DOWNLOAD EBOOK

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 more Who This Book Is For This 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 world In Detail With 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 approach Python 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.


Book Synopsis Python Deep Learning by : Valentino Zocca

Download or read book Python Deep Learning written by Valentino Zocca and published by Packt Publishing Ltd. This book was released on 2017-04-28 with total page 406 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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 more Who This Book Is For This 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 world In Detail With 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 approach Python 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.

Deep Learning

Deep Learning

Author: Ian Goodfellow

Publisher: MIT Press

Published: 2016-11-10

Total Pages: 801

ISBN-13: 0262337371

DOWNLOAD EBOOK

An introduction to a broad range of topics in deep learning, covering mathematical and conceptual background, deep learning techniques used in industry, and research perspectives. “Written by three experts in the field, Deep Learning is the only comprehensive book on the subject.” —Elon Musk, cochair of OpenAI; cofounder and CEO of Tesla and SpaceX Deep learning is a form of machine learning that enables computers to learn from experience and understand the world in terms of a hierarchy of concepts. Because the computer gathers knowledge from experience, there is no need for a human computer operator to formally specify all the knowledge that the computer needs. The hierarchy of concepts allows the computer to learn complicated concepts by building them out of simpler ones; a graph of these hierarchies would be many layers deep. This book introduces a broad range of topics in deep learning. The text offers mathematical and conceptual background, covering relevant concepts in linear algebra, probability theory and information theory, numerical computation, and machine learning. It describes deep learning techniques used by practitioners in industry, including deep feedforward networks, regularization, optimization algorithms, convolutional networks, sequence modeling, and practical methodology; and it surveys such applications as natural language processing, speech recognition, computer vision, online recommendation systems, bioinformatics, and videogames. Finally, the book offers research perspectives, covering such theoretical topics as linear factor models, autoencoders, representation learning, structured probabilistic models, Monte Carlo methods, the partition function, approximate inference, and deep generative models. Deep Learning can be used by undergraduate or graduate students planning careers in either industry or research, and by software engineers who want to begin using deep learning in their products or platforms. A website offers supplementary material for both readers and instructors.


Book Synopsis Deep Learning by : Ian Goodfellow

Download or read book Deep Learning written by Ian Goodfellow and published by MIT Press. This book was released on 2016-11-10 with total page 801 pages. Available in PDF, EPUB and Kindle. Book excerpt: An introduction to a broad range of topics in deep learning, covering mathematical and conceptual background, deep learning techniques used in industry, and research perspectives. “Written by three experts in the field, Deep Learning is the only comprehensive book on the subject.” —Elon Musk, cochair of OpenAI; cofounder and CEO of Tesla and SpaceX Deep learning is a form of machine learning that enables computers to learn from experience and understand the world in terms of a hierarchy of concepts. Because the computer gathers knowledge from experience, there is no need for a human computer operator to formally specify all the knowledge that the computer needs. The hierarchy of concepts allows the computer to learn complicated concepts by building them out of simpler ones; a graph of these hierarchies would be many layers deep. This book introduces a broad range of topics in deep learning. The text offers mathematical and conceptual background, covering relevant concepts in linear algebra, probability theory and information theory, numerical computation, and machine learning. It describes deep learning techniques used by practitioners in industry, including deep feedforward networks, regularization, optimization algorithms, convolutional networks, sequence modeling, and practical methodology; and it surveys such applications as natural language processing, speech recognition, computer vision, online recommendation systems, bioinformatics, and videogames. Finally, the book offers research perspectives, covering such theoretical topics as linear factor models, autoencoders, representation learning, structured probabilistic models, Monte Carlo methods, the partition function, approximate inference, and deep generative models. Deep Learning can be used by undergraduate or graduate students planning careers in either industry or research, and by software engineers who want to begin using deep learning in their products or platforms. A website offers supplementary material for both readers and instructors.

Deep Learning: Algorithms and Applications

Deep Learning: Algorithms and Applications

Author: Witold Pedrycz

Publisher: Springer Nature

Published: 2019-10-23

Total Pages: 360

ISBN-13: 3030317609

DOWNLOAD EBOOK

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.


Book Synopsis Deep Learning: Algorithms and Applications by : Witold Pedrycz

Download or read book Deep Learning: Algorithms and Applications written by Witold Pedrycz and published by Springer Nature. This book was released on 2019-10-23 with total page 360 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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: Fundamentals, Theory and Applications

Deep Learning: Fundamentals, Theory and Applications

Author: Kaizhu Huang

Publisher: Springer

Published: 2019-02-15

Total Pages: 163

ISBN-13: 303006073X

DOWNLOAD EBOOK

The purpose of this edited volume is to provide a comprehensive overview on the fundamentals of deep learning, introduce the widely-used learning architectures and algorithms, present its latest theoretical progress, discuss the most popular deep learning platforms and data sets, and describe how many deep learning methodologies have brought great breakthroughs in various applications of text, image, video, speech and audio processing. Deep learning (DL) has been widely considered as the next generation of machine learning methodology. DL attracts much attention and also achieves great success in pattern recognition, computer vision, data mining, and knowledge discovery due to its great capability in learning high-level abstract features from vast amount of data. This new book will not only attempt to provide a general roadmap or guidance to the current deep learning methodologies, but also present the challenges and envision new perspectives which may lead to further breakthroughs in this field. This book will serve as a useful reference for senior (undergraduate or graduate) students in computer science, statistics, electrical engineering, as well as others interested in studying or exploring the potential of exploiting deep learning algorithms. It will also be of special interest to researchers in the area of AI, pattern recognition, machine learning and related areas, alongside engineers interested in applying deep learning models in existing or new practical applications.


Book Synopsis Deep Learning: Fundamentals, Theory and Applications by : Kaizhu Huang

Download or read book Deep Learning: Fundamentals, Theory and Applications written by Kaizhu Huang and published by Springer. This book was released on 2019-02-15 with total page 163 pages. Available in PDF, EPUB and Kindle. Book excerpt: The purpose of this edited volume is to provide a comprehensive overview on the fundamentals of deep learning, introduce the widely-used learning architectures and algorithms, present its latest theoretical progress, discuss the most popular deep learning platforms and data sets, and describe how many deep learning methodologies have brought great breakthroughs in various applications of text, image, video, speech and audio processing. Deep learning (DL) has been widely considered as the next generation of machine learning methodology. DL attracts much attention and also achieves great success in pattern recognition, computer vision, data mining, and knowledge discovery due to its great capability in learning high-level abstract features from vast amount of data. This new book will not only attempt to provide a general roadmap or guidance to the current deep learning methodologies, but also present the challenges and envision new perspectives which may lead to further breakthroughs in this field. This book will serve as a useful reference for senior (undergraduate or graduate) students in computer science, statistics, electrical engineering, as well as others interested in studying or exploring the potential of exploiting deep learning algorithms. It will also be of special interest to researchers in the area of AI, pattern recognition, machine learning and related areas, alongside engineers interested in applying deep learning models in existing or new practical applications.