Essentials Of Deep Learning And Ai

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Deep Learning

Author : John D. Kelleher
Publisher : MIT Press
ISBN 13 : 0262537559
Total Pages : 298 pages
Book Rating : 4.2/5 (625 download)

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Book Synopsis Deep Learning by : John D. Kelleher

Download or read book Deep Learning written by John D. Kelleher and published by MIT Press. This book was released on 2019-09-10 with total page 298 pages. Available in PDF, EPUB and Kindle. Book excerpt: An accessible introduction to the artificial intelligence technology that enables computer vision, speech recognition, machine translation, and driverless cars. Deep learning is an artificial intelligence technology that enables computer vision, speech recognition in mobile phones, machine translation, AI games, driverless cars, and other applications. When we use consumer products from Google, Microsoft, Facebook, Apple, or Baidu, we are often interacting with a deep learning system. In this volume in the MIT Press Essential Knowledge series, computer scientist John Kelleher offers an accessible and concise but comprehensive introduction to the fundamental technology at the heart of the artificial intelligence revolution. Kelleher explains that deep learning enables data-driven decisions by identifying and extracting patterns from large datasets; its ability to learn from complex data makes deep learning ideally suited to take advantage of the rapid growth in big data and computational power. Kelleher also explains some of the basic concepts in deep learning, presents a history of advances in the field, and discusses the current state of the art. He describes the most important deep learning architectures, including autoencoders, recurrent neural networks, and long short-term networks, as well as such recent developments as Generative Adversarial Networks and capsule networks. He also provides a comprehensive (and comprehensible) introduction to the two fundamental algorithms in deep learning: gradient descent and backpropagation. Finally, Kelleher considers the future of deep learning—major trends, possible developments, and significant challenges.

Artificial Intelligence and Machine Learning Fundamentals

Author : Zsolt Nagy
Publisher : Packt Publishing Ltd
ISBN 13 : 1789809207
Total Pages : 330 pages
Book Rating : 4.7/5 (898 download)

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Book Synopsis Artificial Intelligence and Machine Learning Fundamentals by : Zsolt Nagy

Download or read book Artificial Intelligence and Machine Learning Fundamentals written by Zsolt Nagy and published by Packt Publishing Ltd. This book was released on 2018-12-12 with total page 330 pages. Available in PDF, EPUB and Kindle. Book excerpt: Create AI applications in Python and lay the foundations for your career in data science Key FeaturesPractical examples that explain key machine learning algorithmsExplore neural networks in detail with interesting examplesMaster core AI concepts with engaging activitiesBook Description Machine learning and neural networks are pillars on which you can build intelligent applications. Artificial Intelligence and Machine Learning Fundamentals begins by introducing you to Python and discussing AI search algorithms. You will cover in-depth mathematical topics, such as regression and classification, illustrated by Python examples. As you make your way through the book, you will progress to advanced AI techniques and concepts, and work on real-life datasets to form decision trees and clusters. You will be introduced to neural networks, a powerful tool based on Moore's law. By the end of this book, you will be confident when it comes to building your own AI applications with your newly acquired skills! What you will learnUnderstand the importance, principles, and fields of AIImplement basic artificial intelligence concepts with PythonApply regression and classification concepts to real-world problemsPerform predictive analysis using decision trees and random forestsCarry out clustering using the k-means and mean shift algorithmsUnderstand the fundamentals of deep learning via practical examplesWho this book is for Artificial Intelligence and Machine Learning Fundamentals is for software developers and data scientists who want to enrich their projects with machine learning. You do not need any prior experience in AI. However, it’s recommended that you have knowledge of high school-level mathematics and at least one programming language (preferably Python).

Deep Learning Essentials

Author : Anurag Bhardwaj
Publisher : Packt Publishing Ltd
ISBN 13 : 1785887777
Total Pages : 271 pages
Book Rating : 4.7/5 (858 download)

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Book Synopsis Deep Learning Essentials by : Anurag Bhardwaj

Download or read book Deep Learning Essentials written by Anurag Bhardwaj and published by Packt Publishing Ltd. This book was released on 2018-01-30 with total page 271 pages. Available in PDF, EPUB and Kindle. Book excerpt: Get to grips with the essentials of deep learning by leveraging the power of Python Key Features Your one-stop solution to get started with the essentials of deep learning and neural network modeling Train different kinds of neural networks to tackle various problems in Natural Language Processing, computer vision, speech recognition, and more Covers popular Python libraries such as Tensorflow, Keras, and more, along with tips on training, deploying and optimizing your deep learning models in the best possible manner Book Description Deep Learning a trending topic in the field of Artificial Intelligence today and can be considered to be an advanced form of machine learning, which is quite tricky to master. This book will help you take your first steps in training efficient deep learning models and applying them in various practical scenarios. You will model, train, and deploy different kinds of neural networks such as Convolutional Neural Network, Recurrent Neural Network, and will see some of their applications in real-world domains including computer vision, natural language processing, speech recognition, and so on. You will build practical projects such as chatbots, implement reinforcement learning to build smart games, and develop expert systems for image captioning and processing. Popular Python library such as TensorFlow is used in this book to build the models. This book also covers solutions for different problems you might come across while training models, such as noisy datasets, small datasets, and more. This book does not assume any prior knowledge of deep learning. By the end of this book, you will have a firm understanding of the basics of deep learning and neural network modeling, along with their practical applications. What you will learn Get to grips with the core concepts of deep learning and neural networks Set up deep learning library such as TensorFlow Fine-tune your deep learning models for NLP and Computer Vision applications Unify different information sources, such as images, text, and speech through deep learning Optimize and fine-tune your deep learning models for better performance Train a deep reinforcement learning model that plays a game better than humans Learn how to make your models get the best out of your GPU or CPU Who this book is for Aspiring data scientists and machine learning experts who have limited or no exposure to deep learning will find this book to be very useful. If you are looking for a resource that gets you up and running with the fundamentals of deep learning and neural networks, this book is for you. As the models in the book are trained using the popular Python-based libraries such as Tensorflow and Keras, it would be useful to have sound programming knowledge of Python.

Fundamentals of Deep Learning

Author : Nikhil Buduma
Publisher : "O'Reilly Media, Inc."
ISBN 13 : 1491925566
Total Pages : 272 pages
Book Rating : 4.4/5 (919 download)

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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 272 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

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.

Machine Learning

Author : Ethem Alpaydin
Publisher : MIT Press
ISBN 13 : 0262529513
Total Pages : 225 pages
Book Rating : 4.2/5 (625 download)

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Book Synopsis Machine Learning by : Ethem Alpaydin

Download or read book Machine Learning written by Ethem Alpaydin and published by MIT Press. This book was released on 2016-10-07 with total page 225 pages. Available in PDF, EPUB and Kindle. Book excerpt: A concise overview of machine learning—computer programs that learn from data—which underlies applications that include recommendation systems, face recognition, and driverless cars. Today, machine learning underlies a range of applications we use every day, from product recommendations to voice recognition—as well as some we don't yet use everyday, including driverless cars. It is the basis of the new approach in computing where we do not write programs but collect data; the idea is to learn the algorithms for the tasks automatically from data. As computing devices grow more ubiquitous, a larger part of our lives and work is recorded digitally, and as “Big Data” has gotten bigger, the theory of machine learning—the foundation of efforts to process that data into knowledge—has also advanced. In this book, machine learning expert Ethem Alpaydin offers a concise overview of the subject for the general reader, describing its evolution, explaining important learning algorithms, and presenting example applications. Alpaydin offers an account of how digital technology advanced from number-crunching mainframes to mobile devices, putting today's machine learning boom in context. He describes the basics of machine learning and some applications; the use of machine learning algorithms for pattern recognition; artificial neural networks inspired by the human brain; algorithms that learn associations between instances, with such applications as customer segmentation and learning recommendations; and reinforcement learning, when an autonomous agent learns act so as to maximize reward and minimize penalty. Alpaydin then considers some future directions for machine learning and the new field of “data science,” and discusses the ethical and legal implications for data privacy and security.

Guide to Deep Learning Basics

Author : Sandro Skansi
Publisher : Springer Nature
ISBN 13 : 3030375919
Total Pages : 140 pages
Book Rating : 4.0/5 (33 download)

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Book Synopsis Guide to Deep Learning Basics by : Sandro Skansi

Download or read book Guide to Deep Learning Basics written by Sandro Skansi and published by Springer Nature. This book was released on 2020-01-23 with total page 140 pages. Available in PDF, EPUB and Kindle. Book excerpt: This stimulating text/reference presents a philosophical exploration of the conceptual foundations of deep learning, presenting enlightening perspectives that encompass such diverse disciplines as computer science, mathematics, logic, psychology, and cognitive science. The text also highlights select topics from the fascinating history of this exciting field, including the pioneering work of Rudolf Carnap, Warren McCulloch, Walter Pitts, Bulcsú László, and Geoffrey Hinton. Topics and features: Provides a brief history of mathematical logic, and discusses the critical role of philosophy, psychology, and neuroscience in the history of AI Presents a philosophical case for the use of fuzzy logic approaches in AI Investigates the similarities and differences between the Word2vec word embedding algorithm, and the ideas of Wittgenstein and Firth on linguistics Examines how developments in machine learning provide insights into the philosophical challenge of justifying inductive inferences Debates, with reference to philosophical anthropology, whether an advanced general artificial intelligence might be considered as a living being Investigates the issue of computational complexity through deep-learning strategies for understanding AI-complete problems and developing strong AI Explores philosophical questions at the intersection of AI and transhumanism This inspirational volume will rekindle a passion for deep learning in those already experienced in coding and studying this discipline, and provide a philosophical big-picture perspective for those new to the field.

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.

Deep Learning: Fundamentals, Theory and Applications

Author : Kaizhu Huang
Publisher : Springer
ISBN 13 : 303006073X
Total Pages : 163 pages
Book Rating : 4.0/5 (3 download)

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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.

Machine Learning and Artificial Intelligence in Marketing and Sales

Author : Niladri Syam
Publisher : Emerald Group Publishing
ISBN 13 : 1800438826
Total Pages : 177 pages
Book Rating : 4.8/5 (4 download)

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Book Synopsis Machine Learning and Artificial Intelligence in Marketing and Sales by : Niladri Syam

Download or read book Machine Learning and Artificial Intelligence in Marketing and Sales written by Niladri Syam and published by Emerald Group Publishing. This book was released on 2021-03-10 with total page 177 pages. Available in PDF, EPUB and Kindle. Book excerpt: Machine Learning and Artificial Intelligence in Marketing and Sales explores the ideas, and the statistical and mathematical concepts, behind Artificial Intelligence (AI) and machine learning models, as applied to marketing and sales, without getting lost in the details of mathematical derivations and computer programming.

Artificial Intelligence and Deep Learning Essentials

Author : James Russell
Publisher : Independently Published
ISBN 13 : 9781981078967
Total Pages : 142 pages
Book Rating : 4.0/5 (789 download)

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Book Synopsis Artificial Intelligence and Deep Learning Essentials by : James Russell

Download or read book Artificial Intelligence and Deep Learning Essentials written by James Russell and published by Independently Published. This book was released on 2018-05-12 with total page 142 pages. Available in PDF, EPUB and Kindle. Book excerpt: Get to grips with the essentials of deep learning by leveraging the power of PythonKey Features Your one-stop solution to get started with the essentials of deep learning and neural network modeling Train different kinds of neural networks to tackle various problems in Natural Language Processing, computer vision, speech recognition, and more Covers popular Python libraries such as Tensorflow, Keras, and more, along with tips on training, deploying and optimizing your deep learning models in the best possible manner Deep Learning a trending topic in the field of Artificial Intelligence today and can be considered to be an advanced form of machine learning, which is quite tricky to master. This book will help you take your first steps in training efficient deep learning models and applying them in various practical scenarios. You will model, train, and deploy different kinds of neural networks such as Convolutional Neural Network, Recurrent Neural Network, and will see some of their applications in real-world domains including computer vision, natural language processing, speech recognition, and so on. You will build practical projects such as chatbots, implement reinforcement learning to build smart games, and develop expert systems for image captioning and processing. Popular Python library such as TensorFlow is used in this book to build the models. This book also covers solutions for different problems you might come across while training models, such as noisy datasets, small datasets, and more. This book does not assume any prior knowledge of deep learning. By the end of this book, you will have a firm understanding of the basics of deep learning and neural network modeling, along with their practical applications.What you will learn Get to grips with the core concepts of deep learning and neural networks Set up deep learning library such as TensorFlow Fine-tune your deep learning models for NLP and Computer Vision applications Unify different information sources, such as images, text, and speech through deep learning Optimize and fine-tune your deep learning models for better performance Train a deep reinforcement learning model that plays a game better than humans Learn how to make your models get the best out of your GPU or CPU Who This Book Is For Aspiring data scientists and machine learning experts who have limited or no exposure to deep learning will find this book to be very useful. If you are looking for a resource that gets you up and running with the fundamentals of deep learning and neural networks, this book is for you. As the models in the book are trained using the popular Python-based libraries such as Tensorflow and Keras, it would be useful to have sound programming knowledge of Python. Table of Contents 1. What is artificial intelligence 2. Why is the artificial intelligence important ? 3. Applications of Machine Learning 4. Semantics, Probability and IA 5. Numerical Computation 6. Sequence Modeling, Recurrent and Recursive Nets 7. Autoencoders 8. Markov Chains, Monte Carlo Methods, and Machine Learning

Introduction to Deep Learning

Author : Sandro Skansi
Publisher : Springer
ISBN 13 : 3319730045
Total Pages : 196 pages
Book Rating : 4.3/5 (197 download)

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Book Synopsis Introduction to Deep Learning by : Sandro Skansi

Download or read book Introduction to Deep Learning written by Sandro Skansi and published by Springer. This book was released on 2018-02-04 with total page 196 pages. Available in PDF, EPUB and Kindle. Book excerpt: This textbook presents a concise, accessible and engaging first introduction to deep learning, offering a wide range of connectionist models which represent the current state-of-the-art. The text explores the most popular algorithms and architectures in a simple and intuitive style, explaining the mathematical derivations in a step-by-step manner. The content coverage includes convolutional networks, LSTMs, Word2vec, RBMs, DBNs, neural Turing machines, memory networks and autoencoders. Numerous examples in working Python code are provided throughout the book, and the code is also supplied separately at an accompanying website. Topics and features: introduces the fundamentals of machine learning, and the mathematical and computational prerequisites for deep learning; discusses feed-forward neural networks, and explores the modifications to these which can be applied to any neural network; examines convolutional neural networks, and the recurrent connections to a feed-forward neural network; describes the notion of distributed representations, the concept of the autoencoder, and the ideas behind language processing with deep learning; presents a brief history of artificial intelligence and neural networks, and reviews interesting open research problems in deep learning and connectionism. This clearly written and lively primer on deep learning is essential reading for graduate and advanced undergraduate students of computer science, cognitive science and mathematics, as well as fields such as linguistics, logic, philosophy, and psychology.

Practical Artificial Intelligence

Author : Christian Finlay
Publisher : Independently Published
ISBN 13 : 9781723894374
Total Pages : 436 pages
Book Rating : 4.8/5 (943 download)

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Book Synopsis Practical Artificial Intelligence by : Christian Finlay

Download or read book Practical Artificial Intelligence written by Christian Finlay and published by Independently Published. This book was released on 2018-09-21 with total page 436 pages. Available in PDF, EPUB and Kindle. Book excerpt: This accessible and engaging textbook presents a concise introduction to the exciting field of artificial intelligence (AI). The broad-ranging discussion covers the key subdisciplines within the field, describing practical algorithms and concrete applications in the areas of agents, logic, search, reasoning under uncertainty, machine learning, neural networks, and reinforcement learning. Fully revised and updated, this much-anticipated edition also includes new material on deep learning. Topics and features: presents an application-focused and hands-on approach to learning, with supplementary teaching resources provided at an associated website; contains numerous study exercises and solutions, highlighted examples, definitions, theorems; reports on developments in deep learning, including applications of neural networks to generate creative content such as text, etc. Ideal for foundation courses or modules on AI, this easy-to-read textbook offers an excellent overview of the field for students of computer science and other technical disciplines, requiring no more than a high-school level of knowledge of mathematics to understand the material.

Artificial Intelligence Basics

Author : Tom Taulli
Publisher : Apress
ISBN 13 : 1484250281
Total Pages : 195 pages
Book Rating : 4.4/5 (842 download)

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Book Synopsis Artificial Intelligence Basics by : Tom Taulli

Download or read book Artificial Intelligence Basics written by Tom Taulli and published by Apress. This book was released on 2019-08-01 with total page 195 pages. Available in PDF, EPUB and Kindle. Book excerpt: Artificial intelligence touches nearly every part of your day. While you may initially assume that technology such as smart speakers and digital assistants are the extent of it, AI has in fact rapidly become a general-purpose technology, reverberating across industries including transportation, healthcare, financial services, and many more. In our modern era, an understanding of AI and its possibilities for your organization is essential for growth and success. Artificial Intelligence Basics has arrived to equip you with a fundamental, timely grasp of AI and its impact. Author Tom Taulli provides an engaging, non-technical introduction to important concepts such as machine learning, deep learning, natural language processing (NLP), robotics, and more. In addition to guiding you through real-world case studies and practical implementation steps, Taulli uses his expertise to expand on the bigger questions that surround AI. These include societal trends, ethics, and future impact AI will have on world governments, company structures, and daily life. Google, Amazon, Facebook, and similar tech giants are far from the only organizations on which artificial intelligence has had—and will continue to have—an incredibly significant result. AI is the present and the future of your business as well as your home life. Strengthening your prowess on the subject will prove invaluable to your preparation for the future of tech, and Artificial Intelligence Basics is the indispensable guide that you’ve been seeking. What You Will Learn Study the core principles for AI approaches such as machine learning, deep learning, and NLP (Natural Language Processing)Discover the best practices to successfully implement AI by examining case studies including Uber, Facebook, Waymo, UiPath, and Stitch FixUnderstand how AI capabilities for robots can improve businessDeploy chatbots and Robotic Processing Automation (RPA) to save costs and improve customer serviceAvoid costly gotchasRecognize ethical concerns and other risk factors of using artificial intelligenceExamine the secular trends and how they may impact your business Who This Book Is For Readers without a technical background, such as managers, looking to understand AI to evaluate solutions.

Artificial Intelligence

Author : Neil Wilkins
Publisher :
ISBN 13 : 9781950922512
Total Pages : 114 pages
Book Rating : 4.9/5 (225 download)

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Book Synopsis Artificial Intelligence by : Neil Wilkins

Download or read book Artificial Intelligence written by Neil Wilkins and published by . This book was released on 2019-07-20 with total page 114 pages. Available in PDF, EPUB and Kindle. Book excerpt: So, what is the deal with intelligent machines? Will they soon decide on things such as copyright infringement? How about self-driving trucks and cars? What kind of impact will smart machines have on society and the future of human jobs?

Java Deep Learning Essentials

Author : Yusuke Sugomori
Publisher : Packt Publishing Ltd
ISBN 13 : 1785283146
Total Pages : 254 pages
Book Rating : 4.7/5 (852 download)

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Book Synopsis Java Deep Learning Essentials by : Yusuke Sugomori

Download or read book Java Deep Learning Essentials written by Yusuke Sugomori and published by Packt Publishing Ltd. This book was released on 2016-05-30 with total page 254 pages. Available in PDF, EPUB and Kindle. Book excerpt: Dive into the future of data science and learn how to build the sophisticated algorithms that are fundamental to deep learning and AI with Java About This Book Go beyond the theory and put Deep Learning into practice with Java Find out how to build a range of Deep Learning algorithms using a range of leading frameworks including DL4J, Theano and Caffe Whether you're a data scientist or Java developer, dive in and find out how to tackle Deep Learning Who This Book Is For This book is intended for data scientists and Java developers who want to dive into the exciting world of deep learning. It would also be good for machine learning users who intend to leverage deep learning in their projects, working within a big data environment. What You Will Learn Get a practical deep dive into machine learning and deep learning algorithms Implement machine learning algorithms related to deep learning Explore neural networks using some of the most popular Deep Learning frameworks Dive into Deep Belief Nets and Stacked Denoising Autoencoders algorithms Discover more deep learning algorithms with Dropout and Convolutional Neural Networks Gain an insight into the deep learning library DL4J and its practical uses Get to know device strategies to use deep learning algorithms and libraries in the real world Explore deep learning further with Theano and Caffe In Detail AI and Deep Learning are transforming the way we understand software, making computers more intelligent than we could even imagine just a decade ago. Deep Learning algorithms are being used across a broad range of industries – as the fundamental driver of AI, being able to tackle Deep Learning is going to a vital and valuable skill not only within the tech world but also for the wider global economy that depends upon knowledge and insight for growth and success. It's something that's moving beyond the realm of data science – if you're a Java developer, this book gives you a great opportunity to expand your skillset. Starting with an introduction to basic machine learning algorithms, to give you a solid foundation, Deep Learning with Java takes you further into this vital world of stunning predictive insights and remarkable machine intelligence. Once you've got to grips with the fundamental mathematical principles, you'll start exploring neural networks and identify how to tackle challenges in large networks using advanced algorithms. You will learn how to use the DL4J library and apply Deep Learning to a range of real-world use cases. Featuring further guidance and insights to help you solve challenging problems in image processing, speech recognition, language modeling, this book will make you rethink what you can do with Java, showing you how to use it for truly cutting-edge predictive insights. As a bonus, you'll also be able to get to grips with Theano and Caffe, two of the most important tools in Deep Learning today. By the end of the book, you'll be ready to tackle Deep Learning with Java. Wherever you've come from – whether you're a data scientist or Java developer – you will become a part of the Deep Learning revolution! Style and approach This is a step-by-step, practical tutorial that discusses key concepts. This book offers a hands-on approach to key algorithms to help you develop a greater understanding of deep learning. It is packed with implementations from scratch, with detailed explanation that make the concepts easy to understand and follow.

Deep Learning with Python: A Fundamentals Guide to Understanding Machine Learning and Artificial Intelligence with Scikit-Learn, Tensorflow, and

Download Deep Learning with Python: A Fundamentals Guide to Understanding Machine Learning and Artificial Intelligence with Scikit-Learn, Tensorflow, and PDF Online Free

Author : Sebastian Dark
Publisher : Independently Published
ISBN 13 : 9781730725906
Total Pages : 158 pages
Book Rating : 4.7/5 (259 download)

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Book Synopsis Deep Learning with Python: A Fundamentals Guide to Understanding Machine Learning and Artificial Intelligence with Scikit-Learn, Tensorflow, and by : Sebastian Dark

Download or read book Deep Learning with Python: A Fundamentals Guide to Understanding Machine Learning and Artificial Intelligence with Scikit-Learn, Tensorflow, and written by Sebastian Dark and published by Independently Published. This book was released on 2018-11 with total page 158 pages. Available in PDF, EPUB and Kindle. Book excerpt: Curious to discover the revolutionary technology that is shaping our future and changing the world? Deep learning is a part of the field of computer science and a subset of machine learning that involves computer systems being able to "learn" unsupervised with data that is unlabeled or unstructured. In 2017, AlphaGo, which is AI developed by Google DeepMind and started off by only knowing the rules of the game, was eventually able to train itself and beat Ke Jie, the world No.1 ranked player at the time. Although this may not seem that impressive at first, it is important to understand that Go is a very complex game that many programmers were not able to trump with AI in the past. Although Go is an interesting example, the possibilities of using machine learning are limitless. From retail to medicine to finance, machine learning has the ability to change each industry it comes into contact with. In fact, this revolution has already begun and will only continue to get bigger. According to statista.com, the artificial intelligence industry is set to grow exponentially in the next few years from $7 Billion in 2018 to $90 Billion in 2025! This isn't something you can afford to miss. Without a doubt it is the future. However, it is as complex as it is revolutionary. If you do not have a background or any experience in the field, it is easy to get bogged down by all the complicated concepts and term. And if you are at a more advanced level, the information you find won't be thorough enough. In this book, you will find the perfect balance between the information being very thorough and being able to understand it. Although tailored for beginners, it won't contain simple and easily accessible information. You will dive deep into the field but will be carefully led through it in a way that will make everything easy to understand even if you do not have a technical background in computer programming. In this Guide, you will discover... What Machine Learning and Deep Learning Is And How You Can Use It To Change The World How The Field Can Be Broken Down And Learned In A Manageable Way Various Applications and Potential of Deep Learning That You Can Utilize - That You May Never Have Even Imagined Supervised And Unsupervised Learning - And Breaking It Down Step By Step How You Can Create And Train Deep Learning Models Where and How To Install the Best Programs So You Can Get Started Today Sample Codes And Datasets To Practice Along With And much more! If you are finally prepared to begin grasping this revolutionary technology at a high level despite what your technical background may be, Click "Add to Cart" Now! **Get the Kindle eBook version for FREE when you buy the Paperback version of this book!**