Neural Network A Complete Guide 2019 Edition

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Neural Network A Complete Guide - 2019 Edition

Author : Gerardus Blokdyk
Publisher : 5starcooks
ISBN 13 : 9780655548102
Total Pages : 310 pages
Book Rating : 4.5/5 (481 download)

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Book Synopsis Neural Network A Complete Guide - 2019 Edition by : Gerardus Blokdyk

Download or read book Neural Network A Complete Guide - 2019 Edition written by Gerardus Blokdyk and published by 5starcooks. This book was released on 2019-06-18 with total page 310 pages. Available in PDF, EPUB and Kindle. Book excerpt: Can biological insight gained from in vitro cultured neural networks on electronic substrates be used for construction of new types of large-scale artificial neural network? To what extent do you believe that artificial neural networks come close to how the brain actually works? What kind of classification of brainwaves is possible to use in a neural network? Why did you choose to rely on a twenty year old technology, namely multilayer neural networks? For what types of business problems would you use neural networks instead of standard techniques? Defining, designing, creating, and implementing a process to solve a challenge or meet an objective is the most valuable role... In EVERY group, company, organization and department. Unless you are talking a one-time, single-use project, there should be a process. Whether that process is managed and implemented by humans, AI, or a combination of the two, it needs to be designed by someone with a complex enough perspective to ask the right questions. Someone capable of asking the right questions and step back and say, 'What are we really trying to accomplish here? And is there a different way to look at it?' This Self-Assessment empowers people to do just that - whether their title is entrepreneur, manager, consultant, (Vice-)President, CxO etc... - they are the people who rule the future. They are the person who asks the right questions to make Neural network investments work better. This Neural network All-Inclusive Self-Assessment enables You to be that person. All the tools you need to an in-depth Neural network Self-Assessment. Featuring 953 new and updated case-based questions, organized into seven core areas of process design, this Self-Assessment will help you identify areas in which Neural network improvements can be made. In using the questions you will be better able to: - diagnose Neural network projects, initiatives, organizations, businesses and processes using accepted diagnostic standards and practices - implement evidence-based best practice strategies aligned with overall goals - integrate recent advances in Neural network and process design strategies into practice according to best practice guidelines Using a Self-Assessment tool known as the Neural network Scorecard, you will develop a clear picture of which Neural network areas need attention. Your purchase includes access details to the Neural network self-assessment dashboard download which gives you your dynamically prioritized projects-ready tool and shows your organization exactly what to do next. You will receive the following contents with New and Updated specific criteria: - The latest quick edition of the book in PDF - The latest complete edition of the book in PDF, which criteria correspond to the criteria in... - The Self-Assessment Excel Dashboard - Example pre-filled Self-Assessment Excel Dashboard to get familiar with results generation - In-depth and specific Neural network Checklists - Project management checklists and templates to assist with implementation INCLUDES LIFETIME SELF ASSESSMENT UPDATES Every self assessment comes with Lifetime Updates and Lifetime Free Updated Books. Lifetime Updates is an industry-first feature which allows you to receive verified self assessment updates, ensuring you always have the most accurate information at your fingertips.

Neural Network Projects with Python

Author : James Loy
Publisher : Packt Publishing Ltd
ISBN 13 : 1789133319
Total Pages : 301 pages
Book Rating : 4.7/5 (891 download)

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Book Synopsis Neural Network Projects with Python by : James Loy

Download or read book Neural Network Projects with Python written by James Loy and published by Packt Publishing Ltd. This book was released on 2019-02-28 with total page 301 pages. Available in PDF, EPUB and Kindle. Book excerpt: Build your Machine Learning portfolio by creating 6 cutting-edge Artificial Intelligence projects using neural networks in Python Key FeaturesDiscover neural network architectures (like CNN and LSTM) that are driving recent advancements in AIBuild expert neural networks in Python using popular libraries such as KerasIncludes projects such as object detection, face identification, sentiment analysis, and moreBook Description Neural networks are at the core of recent AI advances, providing some of the best resolutions to many real-world problems, including image recognition, medical diagnosis, text analysis, and more. This book goes through some basic neural network and deep learning concepts, as well as some popular libraries in Python for implementing them. It contains practical demonstrations of neural networks in domains such as fare prediction, image classification, sentiment analysis, and more. In each case, the book provides a problem statement, the specific neural network architecture required to tackle that problem, the reasoning behind the algorithm used, and the associated Python code to implement the solution from scratch. In the process, you will gain hands-on experience with using popular Python libraries such as Keras to build and train your own neural networks from scratch. By the end of this book, you will have mastered the different neural network architectures and created cutting-edge AI projects in Python that will immediately strengthen your machine learning portfolio. What you will learnLearn various neural network architectures and its advancements in AIMaster deep learning in Python by building and training neural networkMaster neural networks for regression and classificationDiscover convolutional neural networks for image recognitionLearn sentiment analysis on textual data using Long Short-Term MemoryBuild and train a highly accurate facial recognition security systemWho this book is for This book is a perfect match for data scientists, machine learning engineers, and deep learning enthusiasts who wish to create practical neural network projects in Python. Readers should already have some basic knowledge of machine learning and neural networks.

Neural Networks A Complete Guide - 2019 Edition

Author : Gerardus Blokdyk
Publisher : 5starcooks
ISBN 13 : 9780655840558
Total Pages : 300 pages
Book Rating : 4.8/5 (45 download)

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Book Synopsis Neural Networks A Complete Guide - 2019 Edition by : Gerardus Blokdyk

Download or read book Neural Networks A Complete Guide - 2019 Edition written by Gerardus Blokdyk and published by 5starcooks. This book was released on 2019-08-03 with total page 300 pages. Available in PDF, EPUB and Kindle. Book excerpt: What neural networks events should you attend? Among the neural networks product and service cost to be estimated, which is considered hardest to estimate? How can you measure neural networks in a systematic way? Are neural networks changes recognized early enough to be approved through the regular process? Who are the key stakeholders for the neural networks evaluation? Defining, designing, creating, and implementing a process to solve a challenge or meet an objective is the most valuable role... In EVERY group, company, organization and department. Unless you are talking a one-time, single-use project, there should be a process. Whether that process is managed and implemented by humans, AI, or a combination of the two, it needs to be designed by someone with a complex enough perspective to ask the right questions. Someone capable of asking the right questions and step back and say, 'What are we really trying to accomplish here? And is there a different way to look at it?' This Self-Assessment empowers people to do just that - whether their title is entrepreneur, manager, consultant, (Vice-)President, CxO etc... - they are the people who rule the future. They are the person who asks the right questions to make Neural Networks investments work better. This Neural Networks All-Inclusive Self-Assessment enables You to be that person. All the tools you need to an in-depth Neural Networks Self-Assessment. Featuring 913 new and updated case-based questions, organized into seven core areas of process design, this Self-Assessment will help you identify areas in which Neural Networks improvements can be made. In using the questions you will be better able to: - diagnose Neural Networks projects, initiatives, organizations, businesses and processes using accepted diagnostic standards and practices - implement evidence-based best practice strategies aligned with overall goals - integrate recent advances in Neural Networks and process design strategies into practice according to best practice guidelines Using a Self-Assessment tool known as the Neural Networks Scorecard, you will develop a clear picture of which Neural Networks areas need attention. Your purchase includes access details to the Neural Networks self-assessment dashboard download which gives you your dynamically prioritized projects-ready tool and shows your organization exactly what to do next. You will receive the following contents with New and Updated specific criteria: - The latest quick edition of the book in PDF - The latest complete edition of the book in PDF, which criteria correspond to the criteria in... - The Self-Assessment Excel Dashboard - Example pre-filled Self-Assessment Excel Dashboard to get familiar with results generation - In-depth and specific Neural Networks Checklists - Project management checklists and templates to assist with implementation INCLUDES LIFETIME SELF ASSESSMENT UPDATES Every self assessment comes with Lifetime Updates and Lifetime Free Updated Books. Lifetime Updates is an industry-first feature which allows you to receive verified self assessment updates, ensuring you always have the most accurate information at your fingertips.

Neural Networks

Author : Steven Cooper
Publisher : Roland Bind
ISBN 13 :
Total Pages : 82 pages
Book Rating : 4.:/5 (661 download)

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Book Synopsis Neural Networks by : Steven Cooper

Download or read book Neural Networks written by Steven Cooper and published by Roland Bind. This book was released on 2018-11-06 with total page 82 pages. Available in PDF, EPUB and Kindle. Book excerpt: ☆★The Best Neural Networks Book for Beginners★☆ If you are looking for a complete beginners guide to learn neural networks with examples, in just a few hours, then you need to continue reading. Have you noticed the increasing prevalence of software that tries to learn from you? More and more, we are interacting with machines and platforms that try to predict what we are looking for. From movie and television show recommendations on Netflix based on your taste to the keyboard on your smartphone trying to predict and recommend the next word you may want to type, it's becoming obvious that machine learning will definitely be part of our future. If you are interested in learning more about the computer programs of tomorrow then, Understanding Neural Networks – A Practical Guide for Understanding and Programming Neural Networks and Useful Insights for Inspiring Reinvention is the book you have been waiting for. ★★ Grab your copy today and learn ★★ ♦ The history of neural networks and the way modern neural networks work ♦ How deep learning works ♦ The different types of neural networks ♦ The ability to explain a neural network to others, while simultaneously being able to build on this knowledge without being COMPLETELY LOST ♦ How to build your own neural network! ♦ An effective technique for hacking into a neural network ♦ Some introductory advice for modifying parameters in the code-based environment ♦ And much more... You'll be an Einstein in no time! And even if you are already up to speed on the topic, this book has the power to illustrate what a neural network is in a way that is capable of inspiring new approaches and technical improvements. The world can't wait to see what you can do! Most of all, this book will feed the abstract reasoning region of your mind so that you are able to theorize and invent new types and styles of machine learning. So, what are you waiting for? Scroll up and click the buy now button to learn everything you need to know in no time!

Neural Networks in Finance

Author : Paul D. McNelis
Publisher : Academic Press
ISBN 13 : 0124859674
Total Pages : 262 pages
Book Rating : 4.1/5 (248 download)

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Book Synopsis Neural Networks in Finance by : Paul D. McNelis

Download or read book Neural Networks in Finance written by Paul D. McNelis and published by Academic Press. This book was released on 2005-01-05 with total page 262 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book explores the intuitive appeal of neural networks and the genetic algorithm in finance. It demonstrates how neural networks used in combination with evolutionary computation outperform classical econometric methods for accuracy in forecasting, classification and dimensionality reduction. McNelis utilizes a variety of examples, from forecasting automobile production and corporate bond spread, to inflation and deflation processes in Hong Kong and Japan, to credit card default in Germany to bank failures in Texas, to cap-floor volatilities in New York and Hong Kong. * Offers a balanced, critical review of the neural network methods and genetic algorithms used in finance * Includes numerous examples and applications * Numerical illustrations use MATLAB code and the book is accompanied by a website

Neural Network A Complete Guide - 2021 Edition

Author : Gerardus Blokdyk
Publisher :
ISBN 13 : 9781867464075
Total Pages : 0 pages
Book Rating : 4.4/5 (64 download)

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Book Synopsis Neural Network A Complete Guide - 2021 Edition by : Gerardus Blokdyk

Download or read book Neural Network A Complete Guide - 2021 Edition written by Gerardus Blokdyk and published by . This book was released on with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Deep Learning Illustrated

Author : Jon Krohn
Publisher : Addison-Wesley Professional
ISBN 13 : 0135121728
Total Pages : 725 pages
Book Rating : 4.1/5 (351 download)

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Book Synopsis Deep Learning Illustrated by : Jon Krohn

Download or read book Deep Learning Illustrated written by Jon Krohn and published by Addison-Wesley Professional. This book was released on 2019-08-05 with total page 725 pages. Available in PDF, EPUB and Kindle. Book excerpt: "The authors’ clear visual style provides a comprehensive look at what’s currently possible with artificial neural networks as well as a glimpse of the magic that’s to come." – Tim Urban, author of Wait But Why Fully Practical, Insightful Guide to Modern Deep Learning Deep learning is transforming software, facilitating powerful new artificial intelligence capabilities, and driving unprecedented algorithm performance. Deep Learning Illustrated is uniquely intuitive and offers a complete introduction to the discipline’s techniques. Packed with full-color figures and easy-to-follow code, it sweeps away the complexity of building deep learning models, making the subject approachable and fun to learn. World-class instructor and practitioner Jon Krohn–with visionary content from Grant Beyleveld and beautiful illustrations by Aglaé Bassens–presents straightforward analogies to explain what deep learning is, why it has become so popular, and how it relates to other machine learning approaches. Krohn has created a practical reference and tutorial for developers, data scientists, researchers, analysts, and students who want to start applying it. He illuminates theory with hands-on Python code in accompanying Jupyter notebooks. To help you progress quickly, he focuses on the versatile deep learning library Keras to nimbly construct efficient TensorFlow models; PyTorch, the leading alternative library, is also covered. You’ll gain a pragmatic understanding of all major deep learning approaches and their uses in applications ranging from machine vision and natural language processing to image generation and game-playing algorithms. Discover what makes deep learning systems unique, and the implications for practitioners Explore new tools that make deep learning models easier to build, use, and improve Master essential theory: artificial neurons, training, optimization, convolutional nets, recurrent nets, generative adversarial networks (GANs), deep reinforcement learning, and more Walk through building interactive deep learning applications, and move forward with your own artificial intelligence projects Register your book for convenient access to downloads, updates, and/or corrections as they become available. See inside book for details.

Neural Network Methods for Natural Language Processing

Author : Yoav Goldberg
Publisher : Springer Nature
ISBN 13 : 3031021657
Total Pages : 20 pages
Book Rating : 4.0/5 (31 download)

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Book Synopsis Neural Network Methods for Natural Language Processing by : Yoav Goldberg

Download or read book Neural Network Methods for Natural Language Processing written by Yoav Goldberg and published by Springer Nature. This book was released on 2022-06-01 with total page 20 pages. Available in PDF, EPUB and Kindle. Book excerpt: Neural networks are a family of powerful machine learning models. This book focuses on the application of neural network models to natural language data. The first half of the book (Parts I and II) covers the basics of supervised machine learning and feed-forward neural networks, the basics of working with machine learning over language data, and the use of vector-based rather than symbolic representations for words. It also covers the computation-graph abstraction, which allows to easily define and train arbitrary neural networks, and is the basis behind the design of contemporary neural network software libraries. The second part of the book (Parts III and IV) introduces more specialized neural network architectures, including 1D convolutional neural networks, recurrent neural networks, conditioned-generation models, and attention-based models. These architectures and techniques are the driving force behind state-of-the-art algorithms for machine translation, syntactic parsing, and many other applications. Finally, we also discuss tree-shaped networks, structured prediction, and the prospects of multi-task learning.

Neural Networks and Deep Learning

Author : Charu C. Aggarwal
Publisher : Springer
ISBN 13 : 3319944630
Total Pages : 512 pages
Book Rating : 4.3/5 (199 download)

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Book Synopsis Neural Networks and Deep Learning by : Charu C. Aggarwal

Download or read book Neural Networks and Deep Learning written by Charu C. Aggarwal and published by Springer. This book was released on 2018-08-25 with total page 512 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book covers both classical and modern models in deep learning. The primary focus is on the theory and algorithms of deep learning. The theory and algorithms of neural networks are particularly important for understanding important concepts, so that one can understand the important design concepts of neural architectures in different applications. Why do neural networks work? When do they work better than off-the-shelf machine-learning models? When is depth useful? Why is training neural networks so hard? What are the pitfalls? The book is also rich in discussing different applications in order to give the practitioner a flavor of how neural architectures are designed for different types of problems. Applications associated with many different areas like recommender systems, machine translation, image captioning, image classification, reinforcement-learning based gaming, and text analytics are covered. The chapters of this book span three categories: The basics of neural networks: Many traditional machine learning models can be understood as special cases of neural networks. An emphasis is placed in the first two chapters on understanding the relationship between traditional machine learning and neural networks. Support vector machines, linear/logistic regression, singular value decomposition, matrix factorization, and recommender systems are shown to be special cases of neural networks. These methods are studied together with recent feature engineering methods like word2vec. Fundamentals of neural networks: A detailed discussion of training and regularization is provided in Chapters 3 and 4. Chapters 5 and 6 present radial-basis function (RBF) networks and restricted Boltzmann machines. Advanced topics in neural networks: Chapters 7 and 8 discuss recurrent neural networks and convolutional neural networks. Several advanced topics like deep reinforcement learning, neural Turing machines, Kohonen self-organizing maps, and generative adversarial networks are introduced in Chapters 9 and 10. The book is written for graduate students, researchers, and practitioners. Numerous exercises are available along with a solution manual to aid in classroom teaching. Where possible, an application-centric view is highlighted in order to provide an understanding of the practical uses of each class of techniques.

Make Your Own Neural Network

Author : Tariq Rashid
Publisher : Createspace Independent Publishing Platform
ISBN 13 : 9781530826605
Total Pages : 0 pages
Book Rating : 4.8/5 (266 download)

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Book Synopsis Make Your Own Neural Network by : Tariq Rashid

Download or read book Make Your Own Neural Network written by Tariq Rashid and published by Createspace Independent Publishing Platform. This book was released on 2016 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book is for anyone who wants to understand what neural network[s] are. It's for anyone who wants to make and use their own. And it's for anyone who wants to appreciate the fairly easy but exciting mathematical ideas that are at the core of how they work. This guide is not aimed at experts in mathematics or computer science. You won't need any special knowledge or mathematical ability beyond school maths [sic] ... Teachers can use this guide as a particularly gentle explanation of neural networks and their implementation to enthuse and excite students making their very own learning artificial intelligence with only a few lines of programming language code. The code has been tested to work with a Raspberry Pi, a small inexpensive computer very popular in schools and with young students"--(page 6, Introduction)

Introduction to Deep Learning and Neural Networks with PythonTM

Author : Ahmed Fawzy Gad
Publisher : Academic Press
ISBN 13 : 0323909345
Total Pages : 302 pages
Book Rating : 4.3/5 (239 download)

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Book Synopsis Introduction to Deep Learning and Neural Networks with PythonTM by : Ahmed Fawzy Gad

Download or read book Introduction to Deep Learning and Neural Networks with PythonTM written by Ahmed Fawzy Gad and published by Academic Press. This book was released on 2020-11-25 with total page 302 pages. Available in PDF, EPUB and Kindle. Book excerpt: Introduction to Deep Learning and Neural Networks with PythonTM: A Practical Guide is an intensive step-by-step guide for neuroscientists to fully understand, practice, and build neural networks. Providing math and PythonTM code examples to clarify neural network calculations, by book’s end readers will fully understand how neural networks work starting from the simplest model Y=X and building from scratch. Details and explanations are provided on how a generic gradient descent algorithm works based on mathematical and PythonTM examples, teaching you how to use the gradient descent algorithm to manually perform all calculations in both the forward and backward passes of training a neural network. Examines the practical side of deep learning and neural networks Provides a problem-based approach to building artificial neural networks using real data Describes PythonTM functions and features for neuroscientists Uses a careful tutorial approach to describe implementation of neural networks in PythonTM Features math and code examples (via companion website) with helpful instructions for easy implementation

Deep Learning

Author : Ian Goodfellow
Publisher : MIT Press
ISBN 13 : 0262337371
Total Pages : 801 pages
Book Rating : 4.2/5 (623 download)

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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.

Hands-On Neural Networks with TensorFlow 2.0

Author : Paolo Galeone
Publisher : Packt Publishing Ltd
ISBN 13 : 1789613795
Total Pages : 346 pages
Book Rating : 4.7/5 (896 download)

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Book Synopsis Hands-On Neural Networks with TensorFlow 2.0 by : Paolo Galeone

Download or read book Hands-On Neural Networks with TensorFlow 2.0 written by Paolo Galeone and published by Packt Publishing Ltd. This book was released on 2019-09-18 with total page 346 pages. Available in PDF, EPUB and Kindle. Book excerpt: A comprehensive guide to developing neural network-based solutions using TensorFlow 2.0 Key FeaturesUnderstand the basics of machine learning and discover the power of neural networks and deep learningExplore the structure of the TensorFlow framework and understand how to transition to TF 2.0Solve any deep learning problem by developing neural network-based solutions using TF 2.0Book Description TensorFlow, the most popular and widely used machine learning framework, has made it possible for almost anyone to develop machine learning solutions with ease. With TensorFlow (TF) 2.0, you'll explore a revamped framework structure, offering a wide variety of new features aimed at improving productivity and ease of use for developers. This book covers machine learning with a focus on developing neural network-based solutions. You'll start by getting familiar with the concepts and techniques required to build solutions to deep learning problems. As you advance, you’ll learn how to create classifiers, build object detection and semantic segmentation networks, train generative models, and speed up the development process using TF 2.0 tools such as TensorFlow Datasets and TensorFlow Hub. By the end of this TensorFlow book, you'll be ready to solve any machine learning problem by developing solutions using TF 2.0 and putting them into production. What you will learnGrasp machine learning and neural network techniques to solve challenging tasksApply the new features of TF 2.0 to speed up developmentUse TensorFlow Datasets (tfds) and the tf.data API to build high-efficiency data input pipelinesPerform transfer learning and fine-tuning with TensorFlow HubDefine and train networks to solve object detection and semantic segmentation problemsTrain Generative Adversarial Networks (GANs) to generate images and data distributionsUse the SavedModel file format to put a model, or a generic computational graph, into productionWho this book is for If you're a developer who wants to get started with machine learning and TensorFlow, or a data scientist interested in developing neural network solutions in TF 2.0, this book is for you. Experienced machine learning engineers who want to master the new features of the TensorFlow framework will also find this book useful. Basic knowledge of calculus and a strong understanding of Python programming will help you grasp the topics covered in this book.

Efficient Processing of Deep Neural Networks

Author : Vivienne Sze
Publisher : Springer Nature
ISBN 13 : 3031017668
Total Pages : 254 pages
Book Rating : 4.0/5 (31 download)

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Book Synopsis Efficient Processing of Deep Neural Networks by : Vivienne Sze

Download or read book Efficient Processing of Deep Neural Networks written by Vivienne Sze and published by Springer Nature. This book was released on 2022-05-31 with total page 254 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book provides a structured treatment of the key principles and techniques for enabling efficient processing of deep neural networks (DNNs). DNNs are currently widely used for many artificial intelligence (AI) applications, including computer vision, speech recognition, and robotics. While DNNs deliver state-of-the-art accuracy on many AI tasks, it comes at the cost of high computational complexity. Therefore, techniques that enable efficient processing of deep neural networks to improve key metrics—such as energy-efficiency, throughput, and latency—without sacrificing accuracy or increasing hardware costs are critical to enabling the wide deployment of DNNs in AI systems. The book includes background on DNN processing; a description and taxonomy of hardware architectural approaches for designing DNN accelerators; key metrics for evaluating and comparing different designs; features of DNN processing that are amenable to hardware/algorithm co-design to improve energy efficiency and throughput; and opportunities for applying new technologies. Readers will find a structured introduction to the field as well as formalization and organization of key concepts from contemporary work that provide insights that may spark new ideas.

Deep Learning

Author : Shriram K Vasudevan
Publisher : CRC Press
ISBN 13 : 1000481875
Total Pages : 307 pages
Book Rating : 4.0/5 (4 download)

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Book Synopsis Deep Learning by : Shriram K Vasudevan

Download or read book Deep Learning written by Shriram K Vasudevan and published by CRC Press. This book was released on 2021-12-24 with total page 307 pages. Available in PDF, EPUB and Kindle. Book excerpt: Deep Learning: A Comprehensive Guide provides comprehensive coverage of Deep Learning (DL) and Machine Learning (ML) concepts. DL and ML are the most sought-after domains, requiring a deep understanding – and this book gives no less than that. This book enables the reader to build innovative and useful applications based on ML and DL. Starting with the basics of neural networks, and continuing through the architecture of various types of CNNs, RNNs, LSTM, and more till the end of the book, each and every topic is given the utmost care and shaped professionally and comprehensively. Key Features Includes the smooth transition from ML concepts to DL concepts Line-by-line explanations have been provided for all the coding-based examples Includes a lot of real-time examples and interview questions that will prepare the reader to take up a job in ML/DL right away Even a person with a non-computer-science background can benefit from this book by following the theory, examples, case studies, and code snippets Every chapter starts with the objective and ends with a set of quiz questions to test the reader’s understanding Includes references to the related YouTube videos that provide additional guidance AI is a domain for everyone. This book is targeted toward everyone irrespective of their field of specialization. Graduates and researchers in deep learning will find this book useful.

Artificial Neural Networks A Complete Guide - 2020 Edition

Author : Gerardus Blokdyk
Publisher : 5starcooks
ISBN 13 : 9781867326960
Total Pages : 306 pages
Book Rating : 4.3/5 (269 download)

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Book Synopsis Artificial Neural Networks A Complete Guide - 2020 Edition by : Gerardus Blokdyk

Download or read book Artificial Neural Networks A Complete Guide - 2020 Edition written by Gerardus Blokdyk and published by 5starcooks. This book was released on 2020-02-07 with total page 306 pages. Available in PDF, EPUB and Kindle. Book excerpt: Is there any additional Artificial neural networks definition of success? What does Artificial neural networks success mean to the stakeholders? Are you assessing Artificial neural networks and risk? How is Artificial neural networks data gathered? How can you improve Artificial neural networks? Defining, designing, creating, and implementing a process to solve a challenge or meet an objective is the most valuable role... In EVERY group, company, organization and department. Unless you are talking a one-time, single-use project, there should be a process. Whether that process is managed and implemented by humans, AI, or a combination of the two, it needs to be designed by someone with a complex enough perspective to ask the right questions. Someone capable of asking the right questions and step back and say, 'What are we really trying to accomplish here? And is there a different way to look at it?' This Self-Assessment empowers people to do just that - whether their title is entrepreneur, manager, consultant, (Vice-)President, CxO etc... - they are the people who rule the future. They are the person who asks the right questions to make Artificial Neural Networks investments work better. This Artificial Neural Networks All-Inclusive Self-Assessment enables You to be that person. All the tools you need to an in-depth Artificial Neural Networks Self-Assessment. Featuring 943 new and updated case-based questions, organized into seven core areas of process design, this Self-Assessment will help you identify areas in which Artificial Neural Networks improvements can be made. In using the questions you will be better able to: - diagnose Artificial Neural Networks projects, initiatives, organizations, businesses and processes using accepted diagnostic standards and practices - implement evidence-based best practice strategies aligned with overall goals - integrate recent advances in Artificial Neural Networks and process design strategies into practice according to best practice guidelines Using a Self-Assessment tool known as the Artificial Neural Networks Scorecard, you will develop a clear picture of which Artificial Neural Networks areas need attention. Your purchase includes access details to the Artificial Neural Networks self-assessment dashboard download which gives you your dynamically prioritized projects-ready tool and shows your organization exactly what to do next. You will receive the following contents with New and Updated specific criteria: - The latest quick edition of the book in PDF - The latest complete edition of the book in PDF, which criteria correspond to the criteria in... - The Self-Assessment Excel Dashboard - Example pre-filled Self-Assessment Excel Dashboard to get familiar with results generation - In-depth and specific Artificial Neural Networks Checklists - Project management checklists and templates to assist with implementation INCLUDES LIFETIME SELF ASSESSMENT UPDATES Every self assessment comes with Lifetime Updates and Lifetime Free Updated Books. Lifetime Updates is an industry-first feature which allows you to receive verified self assessment updates, ensuring you always have the most accurate information at your fingertips.

Machine Learning

Author : Steven Alex
Publisher :
ISBN 13 : 9781706195856
Total Pages : 135 pages
Book Rating : 4.1/5 (958 download)

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Book Synopsis Machine Learning by : Steven Alex

Download or read book Machine Learning written by Steven Alex and published by . This book was released on 2019-11-06 with total page 135 pages. Available in PDF, EPUB and Kindle. Book excerpt: ★ ★ Buy the Paperback Version of this Book and Get the Kindle Book version for FREE ★ ★ Machine Learning (Update Edition 2019-2020) this Guide is a branch of artificial intelligence, This Machine Learning Series idea is relatively new. A science that researches machines to acquire new knowledge and new skills and to identify existing knowledge. The best way to understand the potential of machine learning is to explore how people and companies are currently taking advantage of it.If you are one of the almost 400 million people with machine learning worldwide, This book offers a method to Techniques! Not every machine learning model uses the same techniques, so training will depend on your approach. Let's consider a few examples: Psychology of learning Machine learning in practice Reinforcement learning Types of machine learning Learning by reinforcement Types of reinforcement The different types of learning This guidebook is going to take some time to explore machine learning, and what it is all about. There are so many different aspects of machine learning and how to make it work for your needs, and all of it is found in this guidebook. Some of the different topics that you will be able to learn about inside include: Neural networks Historical background Why use neural networks? Tasks of neural networks Deep learning Algorithms Starting with python Basic types of data Get access to free software and data sets so you can try out your very own machine learning software. See how advanced machine learning will impact our world in the future! Scroll Up and Click the Buy Now Button!