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Temporal Data Mining
by Theophano MitsaFrom basic data mining concepts to state-of-the-art advances, this book covers the theory of the subject as well as its application in a variety of fields. It discusses the incorporation of temporality in databases as well as temporal data representation, similarity computation, data classification, clustering, pattern discovery, and prediction. The book also explores the use of temporal data mining in medicine and biomedical informatics, business and industrial applications, web usage mining, and spatiotemporal data mining. Along with various state-of-the-art algorithms, each chapter includes detailed references and short descriptions of relevant algorithms and techniques described in other references.
Temporal Logics in Computer Science
by Stéphane Demri Valentin Goranko Martin LangeThis comprehensive text provides a modern and technically precise exposition of the fundamental theory and applications of temporal logics in computer science. Part I presents the basics of discrete transition systems, including constructions and behavioural equivalences. Part II examines the most important temporal logics for transition systems and Part III looks at their expressiveness and complexity. Finally, Part IV describes the main computational methods and decision procedures for model checking and model building - based on tableaux, automata and games - and discusses their relationships. The book contains a wealth of examples and exercises, as well as an extensive annotated bibliography. Thus, the book is not only a solid professional reference for researchers in the field but also a comprehensive graduate textbook that can be used for self-study as well as for teaching courses.
Temporal Modelling of Customer Behaviour (Springer Theses)
by Ling LuoThis book describes advanced machine learning models – such as temporal collaborative filtering, stochastic models and Bayesian nonparametrics – for analysing customer behaviour. It shows how they are used to track changes in customer behaviour, monitor the evolution of customer groups, and detect various factors, such as seasonal effects and preference drifts, that may influence customers’ purchasing behaviour. In addition, the book presents four case studies conducted with data from a supermarket health program in which the customers were segmented and the impact of promotional activities on different segments was evaluated. The outcomes confirm that the models developed here can be used to effectively analyse dynamic behaviour and increase customer engagement. Importantly, the methods introduced here can also be used to analyse other types of behavioural data such as activities on social networks, and educational systems.
Temporal and Spatial Environmental Impact of the COVID-19 Pandemic (Advances in Geographical and Environmental Sciences)
by Mohd Akhter Ali M. KamrajuThis book identifies, evaluates and reports the impacts of the COVID-19 pandemic on the physical, biological and socioeconomic environment, using the science and technology of geoinformatics. It encourages the environmental considerations in the future city and policy planning and decision-making. For example, according to the World Health Organization, 80% of people living in cities are exposed to polluted air that exceeds healthy levels. City planners have applied the developing concepts of sustainability to modern debates over how cities and regions should be reviewed, regenerated and reformed since the introduction of the concept in developmental science. During the COVID-19 pandemic, a remarkable drop in air pollution has been observed in India and other countries, which has accelerated the shift to green and sustainable development. Geoinformatics can provide solutions and resources for local, sustainable activities in education, health, sustainable agriculture, resource management and related fields. This book serves researchers in a variety of areas, including hazards, land surveys, remote sensing, cartography, geophysics, geology, natural resources, environment and geography.
Ten Arguments for Deleting Your Social Media Accounts Right Now
by Jaron LanierA timely call-to-arms from a Silicon Valley pioneer. <p><p>You might have trouble imagining life without your social media accounts, but virtual reality pioneer Jaron Lanier insists that we’re better off without them. In Ten Arguments for Deleting Your Social Media Accounts Right Now, Lanier, who participates in no social media, offers powerful and personal reasons for all of us to leave these dangerous online platforms. <p>Lanier’s reasons for freeing ourselves from social media’s poisonous grip include its tendency to bring out the worst in us, to make politics terrifying, to trick us with illusions of popularity and success, to twist our relationship with the truth, to disconnect us from other people even as we are more “connected” than ever, to rob us of our free will with relentless targeted ads. <p> How can we remain autonomous in a world where we are under continual surveillance and are constantly being prodded by algorithms run by some of the richest corporations in history that have no way of making money other than being paid to manipulate our behavior? How could the benefits of social media possibly outweigh the catastrophic losses to our personal dignity, happiness, and freedom? <p>Lanier remains a tech optimist, so while demonstrating the evil that rules social media business models today, he also envisions a humanistic setting for social networking that can direct us toward a richer and fuller way of living and connecting with our world.
Ten Laws for Security
by Eric DiehlIn this book the author presents the key laws governing information security. He addresses topics such as attacks, vulnerabilities, threats, designing security, identifying key IP assets, authentication, and social engineering. The informal style draws on his experience in the area of video protection and DRM, while the text is supplemented with introductions to the core formal technical ideas. It will be of interest to professionals and researchers engaged with information security.
Ten Photo Assignments
by Amanda Quintenz-FiedlerBecome a Better Digital Photographer, One Assignment at a Time There is no better way to learn than by doing. While theories, histories, best practices, and sciences ground a thorough knowledge of any subject, at some point you have to apply that information to truly garner knowledge. This book provides real-world assignments that guide prospective photographers to a true understanding and mastery of the craft. Each assignment includes a list of goals, detailed instructions, illustrations, and examples. Individual assignments build on one another, allowing your mastery to grow as the book continues. Learn about the capabilities and limitations of your equipment; the proper ways to expose a scene for digital capture; dos and don'ts of cropping and scene placement; how to color manage a scene in-camera; and how to see, manipulate, and augment light to obtain the best possible native files.
Ten Steps to Complex Learning: A Systematic Approach to Four-Component Instructional Design
by Jeroen J. van Merriënboer Paul A. Kirschner Jimmy FrèrejeanTen Steps to Complex Learning presents a path from an educational problem to a solution in a way that students, design practitioners, and researchers can understand and easily use. Students in the fields of instructional design and the learning sciences can use this book to broaden their knowledge of the design of training programs for complex learning. Practitioners can use this book as a reference guide to support their design of courses, curricula, or environments for complex learning.Driven by the acclaimed Four-Component Instructional Design (4C/ID) model, this fourth edition of Ten Steps to Complex Learning is fully revised with the latest research, featuring over 50 new references. The entire book has been updated for clarity, incorporating new colorful graphics and diagrams, and the guiding example used throughout the book is replaced with a training blueprint for the complex skill of “producing video content.” The closing chapter explores the future development of the Ten Steps, discussing changes in teacher roles and the influence of artificial intelligence.
Ten Steps to ITSM Success
by Angelo Esposito Timothy RogersYou've read the books, but... A wealth of material has been written to describe the underlying mechanics of ITSM, but very little practical advice is available on how to implement ITSM best practices to achieve an organization's business objectives. The official ITIL® volumes explain what service management is, how the processes work and fit together, and why IT shops should adopt the practice, but they are notoriously vague on how to design and implement an ITSM model in a real organization. This challenge is best understood by those with experience of transforming ineffective and expensive IT, yet most ITSM guides are authored from a purely academic standpoint. From the classroom to the real world This book provides guidance on implementing ITSM Best Practices in an organization based on the authors' real-world experiences. Advice is delivered through a Ten-Step approach, with each step building upon the successes of its predecessors. Subjects covered include - • Documenting objectives, identifying current and future demands, analyzing service financials. • High-level design, negotiating development priorities, creating an execution plan and roadmap, agreeing roles and responsibilities. • Detailed design, building, testing, deploying. • Monitoring and continual improvement. Each step includes summary lists of key questions to ask and specific actions to take, and a useful business case template is included as an appendix. A practical guide to ITSM Improvement As organizations seek to boost revenue, cut costs and increase efficiency, they increasingly look to IT as a strategic partner in achieving these objectives. Ten Steps to ITSM Success helps IT to prepare for this role by providing a detailed and practical guide to implementing ITSM best practices. It is aimed at ITSM practitioners and consultants, but will also be of interest to IT Directors and C-suite executives looking to transform the role of IT into a value-creating business partner, to establish a service management culture, and to drive improvements in their respective organizations. A structured yet flexible method for achieving ITSM success!
Ten Things Video Games Can Teach Us: (about life, philosophy and everything)
by Jordan Erica Webber Daniel GriliopoulosWOULD YOU KILL ONE PERSON TO SAVE FIVE OTHERS?If you could upload all of your memories into a machine, would that machine be you? Is it possible we're all already artificial intelligences, living inside a simulation?These sound like questions from a philosophy class, but in fact they're from modern, popular video games. Philosophical discussion often uses thought experiments to consider ideas that we can't test in real life, and media like books, films, and games can make these thought experiments far more accessible to a non-academic audience. Thanks to their interactive nature, video games can be especially effective ways to explore these ideas.Each chapter of this book introduces a philosophical topic through discussion of relevant video games, with interviews with game creators and expert philosophers. In ten chapters, this book demonstrates how video games can help us to consider the following questions:1. Why do video games make for good thought experiments? (From the ethical dilemmas of the Mass Effect series to 'philosophy games'.)2. What can we actually know? (From why Phoenix Wright is right for the wrong reasons to whether No Man's Sky is a lie.)3. Is virtual reality a kind of reality? (On whether VR headsets like the Oculus Rift, PlayStation VR, and HTC Vive deal in mass-market hallucination.)4. What constitutes a mind? (From the souls of Beyond: Two Souls to the synths of Fallout 4.)5. What can you lose before you're no longer yourself? (Identity crises in the likes of The Swapper and BioShock Infinite.)6. Does it mean anything to say we have choice? (Determinism and free will in Bioshock, Portal 2 and Deus Ex.)7. What does it mean to be a good or dutiful person? (Virtue ethics in the Ultima series and duty ethics in Planescape: Torment.)8. Is there anything better in life than to be happy? (Utilitarianism in Bioshock 2 and Harvest Moon.)10. How should we be governed, for whom and by who? (Government and rights in Eve Online, Crusader Kings, Democracy 3 and Fable 3.)11. Is it ever right to take another life? And how do we cope with our own death? (The Harm Thesis and the good death in To The Moon and Lost Odyssey.)
Ten Things Video Games Can Teach Us: (about life, philosophy and everything)
by Jordan Erica Webber Daniel GriliopoulosWOULD YOU KILL ONE PERSON TO SAVE FIVE OTHERS?If you could upload all of your memories into a machine, would that machine be you? Is it possible we're all already artificial intelligences, living inside a simulation?These sound like questions from a philosophy class, but in fact they're from modern, popular video games. Philosophical discussion often uses thought experiments to consider ideas that we can't test in real life, and media like books, films, and games can make these thought experiments far more accessible to a non-academic audience. Thanks to their interactive nature, video games can be especially effective ways to explore these ideas.Each chapter of this book introduces a philosophical topic through discussion of relevant video games, with interviews with game creators and expert philosophers. In ten chapters, this book demonstrates how video games can help us to consider the following questions:1. Why do video games make for good thought experiments? (From the ethical dilemmas of the Mass Effect series to 'philosophy games'.)2. What can we actually know? (From why Phoenix Wright is right for the wrong reasons to whether No Man's Sky is a lie.)3. Is virtual reality a kind of reality? (On whether VR headsets like the Oculus Rift, PlayStation VR, and HTC Vive deal in mass-market hallucination.)4. What constitutes a mind? (From the souls of Beyond: Two Souls to the synths of Fallout 4.)5. What can you lose before you're no longer yourself? (Identity crises in the likes of The Swapper and BioShock Infinite.)6. Does it mean anything to say we have choice? (Determinism and free will in Bioshock, Portal 2 and Deus Ex.)7. What does it mean to be a good or dutiful person? (Virtue ethics in the Ultima series and duty ethics in Planescape: Torment.)8. Is there anything better in life than to be happy? (Utilitarianism in Bioshock 2 and Harvest Moon.)10. How should we be governed, for whom and by who? (Government and rights in Eve Online, Crusader Kings, Democracy 3 and Fable 3.)11. Is it ever right to take another life? And how do we cope with our own death? (The Harm Thesis and the good death in To The Moon and Lost Odyssey.)
Tencent: The Political Economy of China’s Surging Internet Giant (Global Media Giants)
by Min TangIn this book, author Min Tang examines the political economy of the China-based leading global Internet giant, Tencent. Tracing the historical context and shaping forces, the book illuminates Tencent’s emergence as a joint creation of the Chinese state and transnational financial capital. Tencent reveals interweaving axes of power on different levels, particularly interactions between the global digital industry and contemporary China. The expansion strategies Tencent has employed—horizontal and vertical integration, diversification and transnationalization—speak to the intrinsic trends of capitalist reproduction and the consistent features of the political economy of communications. The book also pinpoints two emerging and entangling trends— transnationalization and financialization—as unfolding trajectories of the global political economy. Understanding Tencent’s dynamics of growth helps to clarify the complex nature of China’s contemporary transformation and the multifaceted characteristics of its increasingly globalized Internet industry. This short and highly topical research volume is perfect for students and scholars of of global media, political economy, and Chinese business, media and communication, and society.
Tensor Analysis and Elementary Differential Geometry for Physicists and Engineers (Mathematical Engineering #21)
by Hung Nguyen-Schäfer Jan-Philip SchmidtThis book presents tensors and differential geometry in a comprehensive and approachable manner, providing a bridge from the place where physics and engineering mathematics end, and the place where tensor analysis begins. Among the topics examined are tensor analysis, elementary differential geometry of moving surfaces, and k-differential forms. The book includes numerous examples with solutions and concrete calculations, which guide readers through these complex topics step by step. Mindful of the practical needs of engineers and physicists, book favors simplicity over a more rigorous, formal approach. The book shows readers how to work with tensors and differential geometry and how to apply them to modeling the physical and engineering world.The authors provide chapter-length treatment of topics at the intersection of advanced mathematics, and physics and engineering: • General Basis and Bra-Ket Notation• Tensor Analysis• Elementary Differential Geometry• Differential Forms• Applications of Tensors and Differential Geometry• Tensors and Bra-Ket Notation in Quantum MechanicsThe text reviews methods and applications in computational fluid dynamics; continuum mechanics; electrodynamics in special relativity; cosmology in the Minkowski four-dimensional space time; and relativistic and non-relativistic quantum mechanics.Tensor Analysis and Elementary Differential Geometry for Physicists and Engineers benefits research scientists and practicing engineers in a variety of fields, who use tensor analysis and differential geometry in the context of applied physics, and electrical and mechanical engineering. It will also interest graduate students in applied physics and engineering.
Tensor Analysis and Elementary Differential Geometry for Physicists and Engineers (Mathematical Engineering)
by Hung Nguyen-Schäfer Jan-Philip SchmidtTensors and methods of differential geometry are very useful mathematical tools in many fields of modern physics and computational engineering including relativity physics, electrodynamics, computational fluid dynamics (CFD), continuum mechanics, aero and vibroacoustics and cybernetics.This book comprehensively presents topics, such as bra-ket notation, tensor analysis and elementary differential geometry of a moving surface. Moreover, authors intentionally abstain from giving mathematically rigorous definitions and derivations that are however dealt with as precisely as possible. The reader is provided with hands-on calculations and worked-out examples at which he will learn how to handle the bra-ket notation, tensors and differential geometry and to use them in the physical and engineering world. The target audience primarily comprises graduate students in physics and engineering, research scientists and practicing engineers.
Tensor Eigenvalues and Their Applications (Advances in Mechanics and Mathematics #39)
by Liqun Qi Haibin Chen Yannan ChenThis book offers an introduction to applications prompted by tensor analysis, especially by the spectral tensor theory developed in recent years. It covers applications of tensor eigenvalues in multilinear systems, exponential data fitting, tensor complementarity problems, and tensor eigenvalue complementarity problems. It also addresses higher-order diffusion tensor imaging, third-order symmetric and traceless tensors in liquid crystals, piezoelectric tensors, strong ellipticity for elasticity tensors, and higher-order tensors in quantum physics. This book is a valuable reference resource for researchers and graduate students who are interested in applications of tensor eigenvalues.
Tensor Network Contractions: Methods and Applications to Quantum Many-Body Systems (Lecture Notes in Physics #964)
by Xi Chen Gang Su Maciej Lewenstein Shi-Ju Ran Emanuele Tirrito Cheng Peng Luca TagliacozzoTensor network is a fundamental mathematical tool with a huge range of applications in physics, such as condensed matter physics, statistic physics, high energy physics, and quantum information sciences. This open access book aims to explain the tensor network contraction approaches in a systematic way, from the basic definitions to the important applications. This book is also useful to those who apply tensor networks in areas beyond physics, such as machine learning and the big-data analysis. Tensor network originates from the numerical renormalization group approach proposed by K. G. Wilson in 1975. Through a rapid development in the last two decades, tensor network has become a powerful numerical tool that can efficiently simulate a wide range of scientific problems, with particular success in quantum many-body physics. Varieties of tensor network algorithms have been proposed for different problems. However, the connections among different algorithms are not well discussed or reviewed. To fill this gap, this book explains the fundamental concepts and basic ideas that connect and/or unify different strategies of the tensor network contraction algorithms. In addition, some of the recent progresses in dealing with tensor decomposition techniques and quantum simulations are also represented in this book to help the readers to better understand tensor network. This open access book is intended for graduated students, but can also be used as a professional book for researchers in the related fields. To understand most of the contents in the book, only basic knowledge of quantum mechanics and linear algebra is required. In order to fully understand some advanced parts, the reader will need to be familiar with notion of condensed matter physics and quantum information, that however are not necessary to understand the main parts of the book. This book is a good source for non-specialists on quantum physics to understand tensor network algorithms and the related mathematics.
TensorFlow 1.x Deep Learning Cookbook
by Antonio Gulli Amita KapoorTake the next step in implementing various common and not-so-common neural networks with Tensorflow 1.x About This Book • Skill up and implement tricky neural networks using Google's TensorFlow 1.x • An easy-to-follow guide that lets you explore reinforcement learning, GANs, autoencoders, multilayer perceptrons and more. • Hands-on recipes to work with Tensorflow on desktop, mobile, and cloud environment Who This Book Is For This book is intended for data analysts, data scientists, machine learning practitioners and deep learning enthusiasts who want to perform deep learning tasks on a regular basis and are looking for a handy guide they can refer to. People who are slightly familiar with neural networks, and now want to gain expertise in working with different types of neural networks and datasets, will find this book quite useful. What You Will Learn • Install TensorFlow and use it for CPU and GPU operations • Implement DNNs and apply them to solve different AI-driven problems. • Leverage different data sets such as MNIST, CIFAR-10, and Youtube8m with TensorFlow and learn how to access and use them in your code. • Use TensorBoard to understand neural network architectures, optimize the learning process, and peek inside the neural network black box. • Use different regression techniques for prediction and classification problems • Build single and multilayer perceptrons in TensorFlow • Implement CNN and RNN in TensorFlow, and use it to solve real-world use cases. • Learn how restricted Boltzmann Machines can be used to recommend movies. • Understand the implementation of Autoencoders and deep belief networks, and use them for emotion detection. • Master the different reinforcement learning methods to implement game playing agents. • GANs and their implementation using TensorFlow. In Detail Deep neural networks (DNNs) have achieved a lot of success in the field of computer vision, speech recognition, and natural language processing. The entire world is filled with excitement about how deep networks are revolutionizing artificial intelligence. This exciting recipe-based guide will take you from the realm of DNN theory to implementing them practically to solve the real-life problems in artificial intelligence domain. In this book, you will learn how to efficiently use TensorFlow, Google's open source framework for deep learning. You will implement different deep learning networks such as Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), Deep Q-learning Networks (DQNs), and Generative Adversarial Networks (GANs) with easy to follow independent recipes. You will learn how to make Keras as backend with TensorFlow. With a problem-solution approach, you will understand how to implement different deep neural architectures to carry out complex tasks at work. You will learn the performance of different DNNs on some popularly used data sets such as MNIST, CIFAR-10, Youtube8m, and more. You will not only learn about the different mobile and embedded platforms supported by TensorFlow but also how to set up cloud platforms for deep learning applications. Get a sneak peek of TPU architecture and how they will affect DNN future. By using crisp, no-nonsense recipes, you will become an expert in implementing deep learning techniques in growing real-world applications and research areas such as reinforcement learning, GANs, autoencoders and more. Style and approach This book consists of hands-on recipes where you'll deal with real-world problems. You'll execute a series of tasks as you walk through data mining challenges using TensorFlow 1.x. Your one-stop solution for common and not-so-common pain points, this is a book that you must have on the shelf.
TensorFlow 1.x Deep Learning Cookbook
by Antonio GulliIn this book, you will learn how to efficiently use TensorFlow, Google's open source framework for deep learning. You will implement different deep learning networks such as Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), Deep Q-learning Networks (DQNs), and Generative Adversarial Networks (GANs) with easy to follow independent recipes.
TensorFlow 2 Pocket Reference: Building and Deploying Machine Learning Models
by Kc TungThis easy-to-use reference for TensorFlow 2 design patterns in Python will help you make informed decisions for various use cases. Author KC Tung addresses common topics and tasks in enterprise data science and machine learning practices rather than focusing on TensorFlow itself.When and why would you feed training data as using NumPy or a streaming dataset? How would you set up cross-validations in the training process? How do you leverage a pretrained model using transfer learning? How do you perform hyperparameter tuning? Pick up this pocket reference and reduce the time you spend searching through options for your TensorFlow use cases.Understand best practices in TensorFlow model patterns and ML workflowsUse code snippets as templates in building TensorFlow models and workflowsSave development time by integrating prebuilt models in TensorFlow HubMake informed design choices about data ingestion, training paradigms, model saving, and inferencingAddress common scenarios such as model design style, data ingestion workflow, model training, and tuning
TensorFlow 2 Reinforcement Learning Cookbook: Over 50 recipes to help you build, train, and deploy learning agents for real-world applications
by Praveen PalanisamyDiscover recipes for developing AI applications to solve a variety of real-world business problems using reinforcement learningKey FeaturesDevelop and deploy deep reinforcement learning-based solutions to production pipelines, products, and servicesExplore popular reinforcement learning algorithms such as Q-learning, SARSA, and the actor-critic methodCustomize and build RL-based applications for performing real-world tasksBook DescriptionWith deep reinforcement learning, you can build intelligent agents, products, and services that can go beyond computer vision or perception to perform actions. TensorFlow 2.x is the latest major release of the most popular deep learning framework used to develop and train deep neural networks (DNNs). This book contains easy-to-follow recipes for leveraging TensorFlow 2.x to develop artificial intelligence applications. Starting with an introduction to the fundamentals of deep reinforcement learning and TensorFlow 2.x, the book covers OpenAI Gym, model-based RL, model-free RL, and how to develop basic agents. You'll discover how to implement advanced deep reinforcement learning algorithms such as actor-critic, deep deterministic policy gradients, deep-Q networks, proximal policy optimization, and deep recurrent Q-networks for training your RL agents. As you advance, you'll explore the applications of reinforcement learning by building cryptocurrency trading agents, stock/share trading agents, and intelligent agents for automating task completion. Finally, you'll find out how to deploy deep reinforcement learning agents to the cloud and build cross-platform apps using TensorFlow 2.x. By the end of this TensorFlow book, you'll have gained a solid understanding of deep reinforcement learning algorithms and their implementations from scratch.What you will learnBuild deep reinforcement learning agents from scratch using the all-new TensorFlow 2.x and Keras APIImplement state-of-the-art deep reinforcement learning algorithms using minimal codeBuild, train, and package deep RL agents for cryptocurrency and stock tradingDeploy RL agents to the cloud and edge to test them by creating desktop, web, and mobile apps and cloud servicesSpeed up agent development using distributed DNN model trainingExplore distributed deep RL architectures and discover opportunities in AIaaS (AI as a Service)Who this book is forThe book is for machine learning application developers, AI and applied AI researchers, data scientists, deep learning practitioners, and students with a basic understanding of reinforcement learning concepts who want to build, train, and deploy their own reinforcement learning systems from scratch using TensorFlow 2.x.
TensorFlow 2.0 Computer Vision Cookbook: Implement machine learning solutions to overcome various computer vision challenges
by Jesus MartinezGet well versed with state-of-the-art techniques to tailor training processes and boost the performance of computer vision models using machine learning and deep learning techniquesKey FeaturesDevelop, train, and use deep learning algorithms for computer vision tasks using TensorFlow 2.xDiscover practical recipes to overcome various challenges faced while building computer vision modelsEnable machines to gain a human level understanding to recognize and analyze digital images and videosBook DescriptionComputer vision is a scientific field that enables machines to identify and process digital images and videos. This book focuses on independent recipes to help you perform various computer vision tasks using TensorFlow. The book begins by taking you through the basics of deep learning for computer vision, along with covering TensorFlow 2.x's key features, such as the Keras and tf.data.Dataset APIs. You'll then learn about the ins and outs of common computer vision tasks, such as image classification, transfer learning, image enhancing and styling, and object detection. The book also covers autoencoders in domains such as inverse image search indexes and image denoising, while offering insights into various architectures used in the recipes, such as convolutional neural networks (CNNs), region-based CNNs (R-CNNs), VGGNet, and You Only Look Once (YOLO). Moving on, you'll discover tips and tricks to solve any problems faced while building various computer vision applications. Finally, you'll delve into more advanced topics such as Generative Adversarial Networks (GANs), video processing, and AutoML, concluding with a section focused on techniques to help you boost the performance of your networks. By the end of this TensorFlow book, you'll be able to confidently tackle a wide range of computer vision problems using TensorFlow 2.x.What you will learnUnderstand how to detect objects using state-of-the-art models such as YOLOv3Use AutoML to predict gender and age from imagesSegment images using different approaches such as FCNs and generative modelsLearn how to improve your network's performance using rank-N accuracy, label smoothing, and test time augmentationEnable machines to recognize people's emotions in videos and real-time streamsAccess and reuse advanced TensorFlow Hub models to perform image classification and object detectionGenerate captions for images using CNNs and RNNsWho this book is forThis book is for computer vision developers and engineers, as well as deep learning practitioners looking for go-to solutions to various problems that commonly arise in computer vision. You will discover how to employ modern machine learning (ML) techniques and deep learning architectures to perform a plethora of computer vision tasks. Basic knowledge of Python programming and computer vision is required.
TensorFlow 2.0 Quick Start Guide: Get up to speed with the newly introduced features of TensorFlow 2.0
by Tony HoldroydPerform supervised and unsupervised machine learning and learn advanced techniques such as training neural networks. Key Features Train your own models for effective prediction, using high-level Keras API Perform supervised and unsupervised machine learning and learn advanced techniques such as training neural networks Get acquainted with some new practices introduced in TensorFlow 2.0 Alpha Book Description TensorFlow is one of the most popular machine learning frameworks in Python. With this book, you will improve your knowledge of some of the latest TensorFlow features and will be able to perform supervised and unsupervised machine learning and also train neural networks. After giving you an overview of what's new in TensorFlow 2.0 Alpha, the book moves on to setting up your machine learning environment using the TensorFlow library. You will perform popular supervised machine learning tasks using techniques such as linear regression, logistic regression, and clustering. You will get familiar with unsupervised learning for autoencoder applications. The book will also show you how to train effective neural networks using straightforward examples in a variety of different domains. By the end of the book, you will have been exposed to a large variety of machine learning and neural network TensorFlow techniques. What you will learn Use tf.Keras for fast prototyping, building, and training deep learning neural network models Easily convert your TensorFlow 1.12 applications to TensorFlow 2.0-compatible files Use TensorFlow to tackle traditional supervised and unsupervised machine learning applications Understand image recognition techniques using TensorFlow Perform neural style transfer for image hybridization using a neural network Code a recurrent neural network in TensorFlow to perform text-style generation Who this book is for Data scientists, machine learning developers, and deep learning enthusiasts looking to quickly get started with TensorFlow 2 will find this book useful. Some Python programming experience with version 3.6 or later, along with a familiarity with Jupyter notebooks will be an added advantage. Exposure to machine learning and neural network techniques would also be helpful.
TensorFlow 2.x in the Colaboratory Cloud: An Introduction to Deep Learning on Google’s Cloud Service
by David PaperUse TensorFlow 2.x with Google's Colaboratory (Colab) product that offers a free cloud service for Python programmers. Colab is especially well suited as a platform for TensorFlow 2.x deep learning applications. You will learn Colab’s default install of the most current TensorFlow 2.x along with Colab’s easy access to on-demand GPU hardware acceleration in the cloud for fast execution of deep learning models. This book offers you the opportunity to grasp deep learning in an applied manner with the only requirement being an Internet connection. Everything else—Python, TensorFlow 2.x, GPU support, and Jupyter Notebooks—is provided and ready to go from Colab. The book begins with an introduction to TensorFlow 2.x and the Google Colab cloud service. You will learn how to provision a workspace on Google Colab and build a simple neural network application. From there you will progress into TensorFlow datasets and building input pipelines in support of modeling and testing. You will find coverage of deep learning classification and regression, with clear code examples showing how to perform each of those functions. Advanced topics covered in the book include convolutional neural networks and recurrent neural networks. This book contains all the applied math and programming you need to master the content. Examples range from simple to relatively complex when necessary to ensure acquisition of appropriate deep learning concepts and constructs. Examples are carefully explained, concise, accurate, and complete to perfectly complement deep learning skill development. Care is taken to walk you through the foundational principles of deep learning through clear examples written in Python that you can try out and experiment with using Google Colab from the comfort of your own home or office.What You Will LearnBe familiar with the basic concepts and constructs of applied deep learningCreate machine learning models with clean and reliable Python codeWork with datasets common to deep learning applicationsPrepare data for TensorFlow consumptionTake advantage of Google Colab’s built-in support for deep learningExecute deep learning experiments using a variety of neural network modelsBe able to mount Google Colab directly to your Google Drive accountVisualize training versus test performance to see model fitWho This Book Is ForReaders who want to learn the highly popular TensorFlow 2.x deep learning platform, those who wish to master deep learning fundamentals that are sometimes skipped over in the rush to be productive, and those looking to build competency with a modern cloud service tool such as Google Colab
TensorFlow Deep Learning Projects: 10 real-world projects on computer vision, machine translation, chatbots, and reinforcement learning
by Alberto Boschetti Luca Massaron Alexey Grigorev Rajalingappaa Shanmugamani Abhishek ThakurLeverage the power of Tensorflow to design deep learning systems for a variety of real-world scenariosKey FeaturesBuild efficient deep learning pipelines using the popular Tensorflow frameworkTrain neural networks such as ConvNets, generative models, and LSTMsIncludes projects related to Computer Vision, stock prediction, chatbots and moreBook DescriptionTensorFlow is one of the most popular frameworks used for machine learning and, more recently, deep learning. It provides a fast and efficient framework for training different kinds of deep learning models, with very high accuracy. This book is your guide to master deep learning with TensorFlow with the help of 10 real-world projects.TensorFlow Deep Learning Projects starts with setting up the right TensorFlow environment for deep learning. Learn to train different types of deep learning models using TensorFlow, including Convolutional Neural Networks, Recurrent Neural Networks, LSTMs, and Generative Adversarial Networks. While doing so, you will build end-to-end deep learning solutions to tackle different real-world problems in image processing, recommendation systems, stock prediction, and building chatbots, to name a few. You will also develop systems that perform machine translation, and use reinforcement learning techniques to play games.By the end of this book, you will have mastered all the concepts of deep learning and their implementation with TensorFlow, and will be able to build and train your own deep learning models with TensorFlow confidently.What you will learnSet up the TensorFlow environment for deep learningConstruct your own ConvNets for effective image processingUse LSTMs for image caption generationForecast stock prediction accurately with an LSTM architectureLearn what semantic matching is by detecting duplicate Quora questionsSet up an AWS instance with TensorFlow to train GANsTrain and set up a chatbot to understand and interpret human inputBuild an AI capable of playing a video game by itself –and win it!Who this book is forThis book is for data scientists, machine learning developers as well as deep learning practitioners, who want to build interesting deep learning projects that leverage the power of Tensorflow. Some understanding of machine learning and deep learning, and familiarity with the TensorFlow framework is all you need to get started with this book.
TensorFlow For Dummies
by Matthew ScarpinoBecome a machine learning pro! Google TensorFlow has become the darling of financial firms and research organizations, but the technology can be intimidating and the learning curve is steep. Luckily, TensorFlow For Dummies is here to offer you a friendly, easy-to-follow book on the subject. Inside, you’ll find out how to write applications with TensorFlow, while also grasping the concepts underlying machine learning—all without ever losing your cool! Machine learning has become ubiquitous in modern society, and its applications include language translation, robotics, handwriting analysis, financial prediction, and image recognition. TensorFlow is Google's preeminent toolset for machine learning, and this hands-on guide makes it easy to understand, even for those without a background in artificial intelligence. Install TensorFlow on your computer Learn the fundamentals of statistical regression and neural networks Visualize the machine learning process with TensorBoard Perform image recognition with convolutional neural networks (CNNs) Analyze sequential data with recurrent neural networks (RNNs) Execute TensorFlow on mobile devices and the Google Cloud Platform (GCP) If you’re a manager or software developer looking to use TensorFlow for machine learning, this is the book you’ll want to have close by.