Deep learning networks can play poker better than professional poker players and defeat a world champion at Go. In this book, Terry Sejnowski explains how deep learning went from being an arcane academic field to a disruptive technology in ...
Author: Terrence J. Sejnowski
Publisher: MIT Press
How deep learning—from Google Translate to driverless cars to personal cognitive assistants—is changing our lives and transforming every sector of the economy. The deep learning revolution has brought us driverless cars, the greatly improved Google Translate, fluent conversations with Siri and Alexa, and enormous profits from automated trading on the New York Stock Exchange. Deep learning networks can play poker better than professional poker players and defeat a world champion at Go. In this book, Terry Sejnowski explains how deep learning went from being an arcane academic field to a disruptive technology in the information economy. Sejnowski played an important role in the founding of deep learning, as one of a small group of researchers in the 1980s who challenged the prevailing logic-and-symbol based version of AI. The new version of AI Sejnowski and others developed, which became deep learning, is fueled instead by data. Deep networks learn from data in the same way that babies experience the world, starting with fresh eyes and gradually acquiring the skills needed to navigate novel environments. Learning algorithms extract information from raw data; information can be used to create knowledge; knowledge underlies understanding; understanding leads to wisdom. Someday a driverless car will know the road better than you do and drive with more skill; a deep learning network will diagnose your illness; a personal cognitive assistant will augment your puny human brain. It took nature many millions of years to evolve human intelligence; AI is on a trajectory measured in decades. Sejnowski prepares us for a deep learning future.
This book is a spin-off from my previous book "The Deep Learning AI Playbook". The Playbook was meant for a professional audience. This is targeted to a much wider audience.
Author: Carlos Perez
Publisher: Createspace Independent Publishing Platform
I challenge you to find a field as interesting and exciting as Deep Learning. This book is a spin-off from my previous book "The Deep Learning AI Playbook." The Playbook was meant for a professional audience. This is targeted to a much wider audience. There are two kinds of audiences, those looking to explore and those looking to optimize. There are two ways to learn, learning by exploration and learning by exploitation. This book is about exploration into the emerging field of Deep Learning. It's more like a popular science book and less of a business book. It's not going to provide any practical advice of how to use or deploy Deep Learning. However, it's a book that will explore this new field in many more perspectives. So at the very least, you'll walk away with the ability to hold a very informative and impressive conversation about this unique subject. It's my hope that having less constraints on what I can express can lead to a more insightful and novel book. There are plenty of ideas that are either too general or too speculative to fit within a business oriented book. With a business book, you always want to manage expectations. Artificial Intelligence is one of those topics that you want to keep speaking in a conservative manner. That's one reason I felt the need for this book. Perhaps the freedom to be more liberal can give readers more ideas as where this field is heading. Also, it's not just business that needs to understand Deep Learning. We are all going to be profoundly impacted by this new kind of Artificial Intelligence and it is critical we all develop at least a good intuition of how it will change the world.The images in the front cover are all generated using Deep Learning technology.
- Tariq Rashid hat eine besondere Fähigkeit, schwierige Konzepte verständlich zu erklären, dadurch werden Neuronale Netze für jeden Interessierten zugänglich und praktisch nachvollziehbar.
Author: Tariq Rashid
Neuronale Netze sind Schlüsselelemente des Deep Learning und der Künstlichen Intelligenz, die heute zu Erstaunlichem in der Lage sind. Dennoch verstehen nur wenige, wie Neuronale Netze tatsächlich funktionieren. Dieses Buch nimmt Sie mit auf eine unterhaltsame Reise, die mit ganz einfachen Ideen beginnt und Ihnen Schritt für Schritt zeigt, wie Neuronale Netze arbeiten. Dafür brauchen Sie keine tieferen Mathematik-Kenntnisse, denn alle mathematischen Konzepte werden behutsam und mit vielen Illustrationen erläutert. Dann geht es in die Praxis: Sie programmieren Ihr eigenes Neuronales Netz mit Python und bringen ihm bei, handgeschriebene Zahlen zu erkennen, bis es eine Performance wie ein professionell entwickeltes Netz erreicht. Zum Schluss lassen Sie das Netz noch auf einem Raspberry Pi Zero laufen. - Tariq Rashid hat eine besondere Fähigkeit, schwierige Konzepte verständlich zu erklären, dadurch werden Neuronale Netze für jeden Interessierten zugänglich und praktisch nachvollziehbar.
... 1 Introducing deep learning and the PyTorch Library 3 1.1 The deep learning
revolution 4 1.2 PyTorch for deep learning ... 7 The deep learning competitive
landscape 8 1.4 An overview of how PyTorch supports deep learning projects 10
Author: Eli Stevens
Publisher: Manning Publications
Every other day we hear about new ways to put deep learning to good use: improved medical imaging, accurate credit card fraud detection, long range weather forecasting, and more. PyTorch puts these superpowers in your hands, providing a comfortable Python experience that gets you started quickly and then grows with you as you—and your deep learning skills—become more sophisticated. Deep Learning with PyTorch will make that journey engaging and fun. Summary Every other day we hear about new ways to put deep learning to good use: improved medical imaging, accurate credit card fraud detection, long range weather forecasting, and more. PyTorch puts these superpowers in your hands, providing a comfortable Python experience that gets you started quickly and then grows with you as you—and your deep learning skills—become more sophisticated. Deep Learning with PyTorch will make that journey engaging and fun. Foreword by Soumith Chintala, Cocreator of PyTorch. Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications. About the technology Although many deep learning tools use Python, the PyTorch library is truly Pythonic. Instantly familiar to anyone who knows PyData tools like NumPy and scikit-learn, PyTorch simplifies deep learning without sacrificing advanced features. It’s excellent for building quick models, and it scales smoothly from laptop to enterprise. Because companies like Apple, Facebook, and JPMorgan Chase rely on PyTorch, it’s a great skill to have as you expand your career options. It’s easy to get started with PyTorch. It minimizes cognitive overhead without sacrificing the access to advanced features, meaning you can focus on what matters the most - building and training the latest and greatest deep learning models and contribute to making a dent in the world. PyTorch is also a snap to scale and extend, and it partners well with other Python tooling. PyTorch has been adopted by hundreds of deep learning practitioners and several first-class players like FAIR, OpenAI, FastAI and Purdue. About the book Deep Learning with PyTorch teaches you to create neural networks and deep learning systems with PyTorch. This practical book quickly gets you to work building a real-world example from scratch: a tumor image classifier. Along the way, it covers best practices for the entire DL pipeline, including the PyTorch Tensor API, loading data in Python, monitoring training, and visualizing results. After covering the basics, the book will take you on a journey through larger projects. The centerpiece of the book is a neural network designed for cancer detection. You'll discover ways for training networks with limited inputs and start processing data to get some results. You'll sift through the unreliable initial results and focus on how to diagnose and fix the problems in your neural network. Finally, you'll look at ways to improve your results by training with augmented data, make improvements to the model architecture, and perform other fine tuning. What's inside Training deep neural networks Implementing modules and loss functions Utilizing pretrained models from PyTorch Hub Exploring code samples in Jupyter Notebooks About the reader For Python programmers with an interest in machine learning. About the author Eli Stevens had roles from software engineer to CTO, and is currently working on machine learning in the self-driving-car industry. Luca Antiga is cofounder of an AI engineering company and an AI tech startup, as well as a former PyTorch contributor. Thomas Viehmann is a PyTorch core developer and machine learning trainer and consultant. consultant based in Munich, Germany and a PyTorch core developer. Table of Contents PART 1 - CORE PYTORCH 1 Introducing deep learning and the PyTorch Library 2 Pretrained networks 3 It starts with a tensor 4 Real-world data representation using tensors 5 The mechanics of learning 6 Using a neural network to fit the data 7 Telling birds from airplanes: Learning from images 8 Using convolutions to generalize PART 2 - LEARNING FROM IMAGES IN THE REAL WORLD: EARLY DETECTION OF LUNG CANCER 9 Using PyTorch to fight cancer 10 Combining data sources into a unified dataset 11 Training a classification model to detect suspected tumors 12 Improving training with metrics and augmentation 13 Using segmentation to find suspected nodules 14 End-to-end nodule analysis, and where to go next PART 3 - DEPLOYMENT 15 Deploying to production
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.
Author: Yusuke Sugomori
Publisher: Packt Publishing Ltd
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.
This book attempts to help the reader on their AI journey by covering the concepts of AI, Machine Learning, and Deep Learning in its many forms; key ML and DL algorithms data scientists should learn; ethical challenges for the use of AI; ...
Author: Steven Astorino
Publisher: MC Press
From humble evolutions in research papers and labs, artificial intelligence (AI)--which encompasses Machine Learning (ML) and Deep Learning (DL)--has matured in its many forms, infused in applications that can learn on their own and become progressively smarter with each interaction and transaction. Coupled with immense stores of data, maturity in CPU and GPU hardware, the invention of new, open source deep learning algorithms, and cloud technologies, operational AI has become available to the masses, setting the wheels in motion for a worldwide AI revolution that has never been seen before. This book attempts to help the reader on their AI journey by covering the concepts of AI, Machine Learning, and Deep Learning in its many forms; key ML and DL algorithms data scientists should learn; ethical challenges for the use of AI; how AI is being used across industries; possible future outlook for AI, and an AI Ladder to help accelerate the AI journey.
NVIDIA has been at the forefront of the deep learning revolution, enabling
research‐ers and developers with powerful hardware and software. And in 2019,
they took the next step to enable makers, too, by releasing the Jetson Nano.
Author: Anirudh Koul
Publisher: O'Reilly Media
Whether you’re a software engineer aspiring to enter the world of deep learning, a veteran data scientist, or a hobbyist with a simple dream of making the next viral AI app, you might have wondered where to begin. This step-by-step guide teaches you how to build practical deep learning applications for the cloud, mobile, browsers, and edge devices using a hands-on approach. Relying on years of industry experience transforming deep learning research into award-winning applications, Anirudh Koul, Siddha Ganju, and Meher Kasam guide you through the process of converting an idea into something that people in the real world can use. Train, tune, and deploy computer vision models with Keras, TensorFlow, Core ML, and TensorFlow Lite Develop AI for a range of devices including Raspberry Pi, Jetson Nano, and Google Coral Explore fun projects, from Silicon Valley’s Not Hotdog app to 40+ industry case studies Simulate an autonomous car in a video game environment and build a miniature version with reinforcement learning Use transfer learning to train models in minutes Discover 50+ practical tips for maximizing model accuracy and speed, debugging, and scaling to millions of users
Deep learning and the concept of connected learning systems that function
similarly to a biological brain have been around ... A fantastic book on the history
and revolution of deep learning is called the “The Deep Learning Revolution” by
Author: Micheal Lanham
Publisher: O'Reilly Media
Working with AI is complicated and expensive for many developers. That's why cloud providers have stepped in to make it easier, offering free (or affordable) state-of-the-art models and training tools to get you started. With this book, you'll learn how to use Google's AI-powered cloud services to do everything from creating a chatbot to analyzing text, images, and video. Author Micheal Lanham demonstrates methods for building and training models step-by-step and shows you how to expand your models to accomplish increasingly complex tasks. If you have a good grasp of math and the Python language, you'll quickly get up to speed with Google Cloud Platform, whether you want to build an AI assistant or a simple business AI application. Learn key concepts for data science, machine learning, and deep learning Explore tools like Video AI and AutoML Tables Build a simple language processor using deep learning systems Perform image recognition using CNNs, transfer learning, and GANs Use Google's Dialogflow to create chatbots and conversational AI Analyze video with automatic video indexing, face detection, and TensorFlow Hub Build a complete working AI agent application
A new and updated edition of the hugely successful Learning Revolution. >
Author: Gordon Dryden
Publisher: A&C Black
A new and updated edition of the hugely successful Learning Revolution. >
This book presents a collection of eleven chapters where each individual chapter explains the deep learning principles of a specific topic, introduces reviews of up-to-date techniques, and presents research findings to the computer vision ...
Author: Mahmoud Hassaballah
Publisher: CRC Press
Deep learning algorithms have brought a revolution to the computer vision community by introducing non-traditional and efficient solutions to several image-related problems that had long remained unsolved or partially addressed. This book presents a collection of eleven chapters where each individual chapter explains the deep learning principles of a specific topic, introduces reviews of up-to-date techniques, and presents research findings to the computer vision community. The book covers a broad scope of topics in deep learning concepts and applications such as accelerating the convolutional neural network inference on field-programmable gate arrays, fire detection in surveillance applications, face recognition, action and activity recognition, semantic segmentation for autonomous driving, aerial imagery registration, robot vision, tumor detection, and skin lesion segmentation as well as skin melanoma classification. The content of this book has been organized such that each chapter can be read independently from the others. The book is a valuable companion for researchers, for postgraduate and possibly senior undergraduate students who are taking an advanced course in related topics, and for those who are interested in deep learning with applications in computer vision, image processing, and pattern recognition.
This research book is directed towards professors, researchers, scientists, engineers and students in computer science-related disciplines.
Author: George A. Tsihrintzis
Publisher: Springer Nature
At the dawn of the 4th Industrial Revolution, the field of Deep Learning (a sub-field of Artificial Intelligence and Machine Learning) is growing continuously and rapidly, developing both theoretically and towards applications in increasingly many and diverse other disciplines. The book at hand aims at exposing its reader to some of the most significant recent advances in deep learning-based technological applications and consists of an editorial note and an additional fifteen (15) chapters. All chapters in the book were invited from authors who work in the corresponding chapter theme and are recognized for their significant research contributions. In more detail, the chapters in the book are organized into six parts, namely (1) Deep Learning in Sensing, (2) Deep Learning in Social Media and IOT, (3) Deep Learning in the Medical Field, (4) Deep Learning in Systems Control, (5) Deep Learning in Feature Vector Processing, and (6) Evaluation of Algorithm Performance. This research book is directed towards professors, researchers, scientists, engineers and students in computer science-related disciplines. It is also directed towards readers who come from other disciplines and are interested in becoming versed in some of the most recent deep learning-based technological applications. An extensive list of bibliographic references at the end of each chapter guides the readers to probe deeper into their application areas of interest.
By the end of this course, you'll be ready to tackle deep learning with Java. Whether you come from a data science background or are a Java developer, you will become part of the deep learning revolution!
Author: Tomasz Lelek
Build sophisticated algorithms that are fundamental to deep learning and AI with Java 12 About This Video Learn key algorithms needed to enhance your understanding of deep learning Use Java and deep neural networks to solve problems with the help of image processing, speech recognition, and natural language modeling Use the DL4J library and apply deep learning concepts to real-world use cases In Detail Deep learning (DL) is used across a broad range of industries as the fundamental driver of AI. Being able to apply deep learning with Java will be a vital and valuable skill, not only within the tech world but also the wider global economy, which depends upon solving problems with higher accuracy and much more predictability than other AI techniques could provide. This step-by-step, practical tutorial teaches you how to implement key concepts and adopts a hands-on approach to key algorithms to help you develop a greater understanding of deep learning. You will learn how to use the DL4J library and apply deep learning to a range of real-world use cases. This course will also help you solve challenging problems in image processing, speech recognition, and natural language modeling; it will make you rethink what you can do with Java, showing you how to use it for truly cutting-edge predictive insights. By the end of this course, you'll be ready to tackle deep learning with Java. Whether you come from a data science background or are a Java developer, you will become part of the deep learning revolution! Downloading the example code for this course: You can download the example code files for this course on GitHub at the following link: https://github.com/PacktPublishing/Deep-Learning-with-Java . If you require support please email: [email protected]
A: Yes. Under Amazon's Kindle Book Lending program, you can lend this book to friends and family for a duration of 14 days. Q: Does this book include everything I need to become a data science expert? A: Unfortunately, no.
Author: Mike Krebbs
Publisher: Createspace Independent Publishing Platform
***** Buy now (Will soon return to $47.99 + Special Offer Below) ***** Free Kindle eBook for customers who purchase the print book from Amazon Are you thinking of learning more about Deep Learning From Scratch by using Python and TensorFlow? The overall aim of this book is to give you an application of deep learning techniques with python. Deep Learning is a type of artificial intelligence and machine learning that has become extremely important in the past few years. Deep Learning allows us to teach machines how to complete complex tasks without explicitly programming them to do so. As a result people with the ability to teach machines using deep learning are in extremely high demand. It is also leading to them getting huge increases in salaries. Deep Learning is revolutionizing the world around us and hence the need to understand and learn it becomes significant. In this book we shall cover what is deep learning, how you can get started with deep learning and what deep learning can do for you. By the end of this book you should be able to know what is deep learning and the tools technology and trends driving the artificial intelligence revolution. Several Visual Illustrations and Examples Instead of tough math formulas, this book contains several graphs and images, which detail all-important deep learning concepts and their applications. This Is a Practical Guide Book This book will help you explore exactly the most important deep learning techniques by using python and real data. It is a step-by-step book. You will build our Deep Learning Models by using Python Target Users The book designed for a variety of target audiences. The most suitable users would include: Beginners who want to approach data science, but are too afraid of complex math to start Newbies in computer science techniques and machine learning Professionals in data science and social sciences Professors, lecturers or tutors who are looking to find better ways to explain the content to their students in the simplest and easiest way Students and academicians, especially those focusing on data science What's Inside This Great Book? Introduction Deep Learning Techniques Applications Next Steps Practical Sentiment Analysis using TensorFlow with Neural Networks Performing Sequence Classification with RNNs Implementing Sequence Classification Using RNNs in TensorFlow Glossary of Some Useful Terms in Deep Learning Sources & References Bonus Chapter: Anaconda Setup & Python Crash Course Frequently Asked Questions Q: Is this book for me and do I need programming experience? A: f you want to smash Data Science from scratch, this book is for you. Little programming experience is required. If you already wrote a few lines of code and recognize basic programming statements, you'll be OK. Q: Can I loan this book to friends? A: Yes. Under Amazon's Kindle Book Lending program, you can lend this book to friends and family for a duration of 14 days. Q: Does this book include everything I need to become a data science expert? A: Unfortunately, no. This book is designed for readers taking their first steps in data science and further learning will be required beyond this book to master all aspects of data science. Q: Can I have a refund if this book is not fitted for me? A: Yes, Amazon refund you if you aren't satisfied, for more information about the amazon refund service please go to the amazon help platform. I will also be happy to help you if you send us an email at [email protected]
Python Deep Learning from Basics: Fundamental Approach for Beginners- Neural Networks, Scikit-Learn, Deep Learning, TensorFlow, Data Analytics, Python, Data Science The deep learning revolution In this book, you will learn how to build ...
Author: Prof John Smith
Publisher: Independently Published
Python Deep Learning from Basics: Fundamental Approach for Beginners- Neural Networks, Scikit-Learn, Deep Learning, TensorFlow, Data Analytics, Python, Data Science The deep learning revolution In this book, you will learn how to build remarkable algorithms intelligent algorithms capable of solving very complex problems that just a decade ago was not even feasible to solveAnd let's just start with this notion of intelligence so at a very high level In this book, you'll actually learn how to build complex vision systems building a computer that how to seeIn addition, you will learn how to build an algorithm that will take as input x-ray images, and as output, it will detect if that person has a pneumothorax just from that single input image.You
A Lifelong Learning Programme for the World's Finest Computer : Your Amazing
Brain Gordon Dryden, Jeannette Vos ... Thinking about it , and deep memory
storage Education is , of course , not only about absorbing new information .
Author: Gordon Dryden
"A detailed report on how to achieve the learning revolution that is urgently required to match the revolution in formation and technology. A working guide for everyone, with special interest to teachers at all grade and college levels."--Page 10.
If you think that this is something that may have a huge impact on your life please keep reading, because you are right... it is! If you are reading this you probably already know something about Deep Learning.
Author: Russel R. Russo
What if you could teach your computer how to learn the way the human brain does?And what if you could do that even without having any background in programming? If you think that this is something that may have a huge impact on your life please keep reading, because you are right... it is! If you are reading this you probably already know something about Deep Learning. You probably know that this is maybe the number one secret behind the success of the big ones, like Google, Facebook and Amazon. Maybe you are also aware that it has been crucial in the tremendous growth of the greatest startups of the last decade, like Airbnb, Uber or Spotify, just to name some. So, you know what we are talking about, still, you will probably have some questions too, like... Is this for me? Is this something I can learn? And once I have learned it, can I also use it in everyday business or it concerns only the big ones? Well, the answer is YES! YES, this is for you (if you want to)! YES, you can learn it (if you commit to)! YES, you can use it for your own business (but it can also open you many doors in finding a great job)! So, either if you want to apply Artificial Intelligence to your own startup, or use it to grow your current business to the next level, or just to find a great job based on your skills and passion, Deep Learning is a great point to start. With Deep Learning for Beginners you will learn: The most effective starting points when training deep neural nets How to talk with deep neural networks What libraries are and which one is the best for you Why a decision tree is the smartest way to go The TensorFlow parts that are going to make your coding life easy If you don't know anything about programming, understanding Deep Learning is the ideal place to start. Still, if you already know something about programming but not about how to apply it to Artificial Intelligence, Deep Learning is what you want to understand. Buy now Deep Learning for Beginners to start your path of Artificial Intelligence.
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 ...
Author: John D. Kelleher
Publisher: MIT Press
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.
Introduction; Unsupervised learning; Local synaptic learning rules suffice to maximize mutual information in a linear network; Convergent algorithm for sensory receptive field development; Emergence of position-independent detectors of ...
Author: Geoffrey E. Hinton
Publisher: MIT Press
Introduction; Unsupervised learning; Local synaptic learning rules suffice to maximize mutual information in a linear network; Convergent algorithm for sensory receptive field development; Emergence of position-independent detectors of snese of rotation and dilation with hebbian learning: an analysis; Learning invariance from transformation sequences; Learning perceptually salient visual parameters using spatiotemporal smoothness constraints; Wht is the goal of sensory coding?; An information-maximization approach to blind separation and blid deconvolution; Natural gradient works efficiently in learning; A fast fixed-point algorithm for independent component analysis; Feature extraction using an unsupervised neural network; Learning mixture models of spatial coherence; Baynesian self-organization driven byprior probability distributions; Finding minimum entropy codes; Learning population codes by minimizing description lengththe Helmholtz machine; factor analysis using delta-rule wake-sleep learning; Dimension reduction by local principal component analysis; A resource-allocating network for function interpolation; 20. Learning with preknowledge: clustering with point and graph matching distance measures; 21. Learning to generalize from single examples in the dynamic ling architecture; Index.
Second Edition.With the Convolutional Neural Network (CNN) breakthrough in 2012, the deep learning is widely appliedto our daily life, automotive, retail, healthcare and finance.
Author: Albert Liu Oscar Law
Second Edition.With the Convolutional Neural Network (CNN) breakthrough in 2012, the deep learning is widely appliedto our daily life, automotive, retail, healthcare and finance. In 2016, Alpha Go with ReinforcementLearning (RL) further proves new Artificial Intelligent (AI) revolution gradually changes our society, likepersonal computer (1977), internet (1994) and smartphone (2007) before. However, most of effortfocuses on software development and seldom addresses the hardware challenges: - Big input data- Deep neural network- Massive parallel processing- Reconfigurable network- Memory bottleneck- Intensive computation- Network pruning- Data sparsityThis book reviews various hardware designs range from CPU, GPU to NPU and list out special features toresolve above problems. New hardware can be evolved from those designs for performance and powerimprovement- Parallel architecture- Convolution optimization- In-memory computation- Near-memory architecture- Network optimizationOrganization of the Book1. Chapter 1 introduces neural network and discuss neural network development history2. Chapter 2 reviews Convolutional Neural Network model and describes each layer function and itsexample3. Chapter 3 list out several parallel architectures, Intel CPU, Nvidia GPU, Google TPU and MicrosoftNPU4. Chapter 4 highlights how to optimize convolution with UCLA DCNN accelerator and MIT EyerissDNN accelerator as example5. Chapter 5 illustrates GT Neurocube architecture and Stanford Tetris DNN process with in-memorycomputation using Hybrid Memory Cube (HMC)6. Chapter 6 proposes near-memory architecture with ICT DaDianNao supercomputer and UofTCnvlutin DNN accelerator7. Chapter 7 chooses energy efficient inference engine for network pruning3We continue to study new approaches to enhance deep learning hardware designs and several topics willbe incorporated into future revision- Distributive graph theory- High speed arithmetic- 3D neural processing