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Just like electricity, Machine Learning will revolutionize our life in many ways – some of which are not even conceivable today. This book provides a thorough conceptual understanding of Machine Learning techniques and algorithms.  Many of the mathematical concepts are explained in an intuitive manner.  The book starts with an overview of machine learning and the underlying Mathematical and Statistical concepts before moving onto machine learning topics. It gradually builds up the depth, covering many of the present day machine learning algorithms, ending in Deep Learning and Reinforcement Learning algorithms. The book also covers some of the popular Machine Learning applications. The material in this book is agnostic to any specific programming language or hardware so that readers can try these concepts on whichever platforms they are already familiar with.     Offers a comprehensive introduction to Machine Learning, while not assuming any prior knowledge of the topic; Provides a comp

An Introduction to Machine Learning

ENbook.pl

pstrongDiscover recipes for developing AI applications to solve a variety of real-world business problems using reinforcement learningstrongppbrppstrongKey FeaturesstrongpulliDevelop and deploy deep reinforcement learning-based solutions to production pipelines, products, and servicesliliExplore popular reinforcement learning algorithms such as Q-learning, SARSA, and the actor-critic methodliliCustomize and build RL-based applications for performing real-world tasksliulpbrppstrongBook DescriptionstrongppWith 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.ppStarting with an introduction to the fundamentals of deep reinforceme

TensorFlow 2 Reinforcement Learning Cookbook | Palanisamy Praveen | Twarda

pstrongemMachine Learning Theory and Practiceemstrong provides an introduction to the most popular methods in machine learning. The book covers regression including regularization, tree-based methods including Random Forests and Boosted Trees, Artificial Neural Networks including Convolutional Neural Networks CNNs, reinforcement learning, and unsupervised learning focused on clustering. Topics are introduced in a conceptual manner along with necessary mathematical details. The explanations are lucid, illustrated with figures and examples. For each machine learning method discussed, the book presents appropriate libraries in the R programming language along with programming examples. ppstrongFeatures strongpul p p liProvides an easy-to-read presentation of commonly used machine learning algorithms in a manner suitable for advanced undergraduate or beginning graduate students, and mathematically andor programming-oriented individuals who want to learn machine learning on their own.li p p

Machine Learning | Kalita Jugal | Twarda

bA project-based guide to the basics of deep learning.bpThis concise, project-driven guide to deep learning takes readers through a series of program-writing tasks that introduce them to the use of deep learning in such areas of artificial intelligence as computer vision, natural-language processing, and reinforcement learning. The author, a longtime artificial intelligence researcher specializing in natural-language processing, covers feed-forward neural nets, convolutional neural nets, word embeddings, recurrent neural nets, sequence-to-sequence learning, deep reinforcement learning, unsupervised models, and other fundamental concepts and techniques. Students and practitioners learn the basics of deep learning by working through programs in Tensorflow, an open-source machine learning framework. I find I learn computer science material best by sitting down and writing programs, the author writes, and the book reflects this approach.ppEach chapter includes a programming project, exerci

Introduction to Deep Learning | Charniak Eugene | Twarda | Twarda

Optimization is a ubiquitous modeling tool and is often deployed in settings which repeatedly solve similar instances of the same problem. Amortized optimization methods use learning to predict the solutions to problems in these settings, exploiting the shared structure between similar problem instances. These methods have been crucial in variational inference and reinforcement learning and are capable of solving optimization problems many orders of magnitudes times faster than traditional optimization methods that do not use amortization. p pIn this tutorial, the author presents an introduction to the amortized optimization foundations behind these advancements and overviews their applications in variational inference, sparse coding, gradient-based meta-learning, control, reinforcement learning, convex optimization, optimal transport, and deep equilibrium networks. Of practical use for the reader, is the source code accompanying the Implementation and Software Examples chapter. p pThi

Tutorial on Amortized Optimization | Amos Brandon | Paperback | Twarda

pbAn Introduction to Universal Artificial Intelligenceb provides a gentle introduction to Universal Artificial Intelligence UAI, a theory that provides a formal underpinning of what it means for an agent to act intelligently in a general class of environments. First presented in Universal Artificial Intelligence Hutter, 2004, UAI presents a model in which most other problems in AI can be presented, and unifies ideas from sequential decision theory, Bayesian inference and information theory to construct AIXI, an optimal reinforcement learning agent that learns to act optimally in unknown environments. AIXI represents a theoretical bound on intelligent behaviour, and so we also discuss tractable approximations of this optimal agent.ppThe book covers important practical approaches including efficient Bayesian updating with context tree weighting, and stochastic planning, approximated by sampling with Monte Carlo tree search. Algorithms are also included for the reader to implement, along 

An Introduction to Universal Artificial Intelligence | Hutter Marcus | Paperback | Twarda

Covering formulation, algorithms, and structural results, and linking theory to real-world applications in controlled sensing including social learning, adaptive radars and sequential detection, this book focuses on the conceptual foundations of partially observed Markov decision processes POMDPs. It emphasizes structural results in stochastic dynamic programming, enabling graduate students and researchers in engineering, operations research, and economics to understand the underlying unifying themes without getting weighed down by mathematical technicalities. Bringing together research from across the literature, the book provides an introduction to nonlinear filtering followed by a systematic development of stochastic dynamic programming, lattice programming and reinforcement learning for POMDPs. Questions addressed in the book include when does a POMDP have a threshold optimal policy When are myopic policies optimal How do local and global decision makers interact in adaptive decisi

Partially Observed Markov Decision Processes | Krishnamurthy Vikram | Twarda | Twarda

pArtificial Intelligence, or AI, is no doubt one of the hottest buzz words at the moment. AI has penetrated into many aspects of our lives. To know AI and to be able to use AI will bring enormous benefits to our work and life. However, to learn AI is a daunting task for many people, largely due to the complex mathematics and sophisticated coding. This book aims to demystify AI and teach readers about AI from scratch, by using simple, plain, languages and simple, illustrative, code examples. This book is divided into three parts.br br In part one, readers will follow an easy to read introduction about AI, including the history, the types of AI, the current status and possible future trend. It then introduces Python, A widely used programming tool for AI. In part two, we introduce Machine Learning aspect of AI, topics include classifications, regressions, and Clustering. It also includes the most popular Reinforcement Learning. In part three, it introduces Deep Learning aspect of AI, top

Artificial Intelligence Programming with Python | Xiao Perry | Twarda

1. Build TensorFlow Input Pipelines2. Increase the Diversity of your Dataset with Data Augmentation3. TensorFlow Datasets4. Deep Learning with TensorFlow Datasets5. Introduction to Tensor Processing Units6. Simple Transfer Learning with TensorFlow Hub7. Advanced Transfer Learning8. Stacked Autoencoders9. Convolutional and Variational Autoencoders10. Generative Adversarial Networks11. Progressive Growing Generative Adversarial Networks12. Fast Style Transfer13. Object Detection14. An Introduction to Reinforcement Learning

State | Of | The | Art Deep Learning Models in Tensorflow | Paper David | Twarda

pstrongA hands-on guide enriched with examples to master deep reinforcement learning algorithms with PythonstrongppbrppstrongKey Features strongpulliYour entry point into the world of artificial intelligence using the power of PythonliliAn example-rich guide to master various RL and DRL algorithmsliliExplore various state-of-the-art architectures along with mathliulpbrppstrongBook Description strongppReinforcement Learning RL is the trending and most promising branch of artificial intelligence. Hands-On Reinforcement learning with Python will help you master not only the basic reinforcement learning algorithms but also the advanced deep reinforcement learning algorithms.ppbrppThe book starts with an introduction to Reinforcement Learning followed by OpenAI Gym, and TensorFlow. You will then explore various RL algorithms and concepts, such as Markov Decision Process, Monte Carlo methods, and dynamic programming, including value and policy iteration. This example-rich guide will introduc

Hands | On Reinforcement Learning with Python | Ravichandiran Sudharsan | Twarda

An Introduction to Federated and Transfer Learning.- Federated Learning for Resource-Constrained IoT Devices Panoramas and State of the Art.- Federated and Transfer Learning A Survey on Adversaries and Defense Mechanisms.- Cross-silo Federated Neural Architecture Search for Heterogeneous and Cooperative Systems.- A Unifying Framework for Federated Learning.- A Contract Theory based Incentive Mechanism for Federated Learning.- A Study of Blockchain-based Federated Learning.- Swarm Meta Learning.- Rethinking Importance Weighting for Transfer Learning.- Transfer Learning via Representation Learning.- Modeling Individual Humans via a Secondary Task Transfer Learning Method.- From Theoretical to Practical Transfer Learning The Adapt Library.- Lyapunov Robust Constrained-MDPs for Sim2Real Transfer Learning.- A Study on Efficient Reinforcement Learning Through Knowledge Transfer.- Federated Transfer Reinforcement Learning for Autonomous Driving.pp

Federated and Transfer Learning | Razavi | Far Roozbeh | Twarda | Twarda

pMachines Learning for Beginners A Beginner's Guide to the World of Machine Learning is your essential handbook for stepping into the realm of machine learning with confidence. This beginner-friendly book provides a solid foundation in machine learning concepts, making complex ideas accessible to anyone eager to explore this cutting-edge field.ppstrongInside this introductory guide, you'll uncover strongpullistrongMachine Learning Fundamentals strong A clear and concise introduction to the core principles and terminology of machine learning.lilistrongTypes of Machine Learning strong An exploration of supervised, unsupervised, and reinforcement learning, along with their real-world applications.lilistrongAlgorithms Demystified strong Insights into popular machine learning algorithms like linear regression, decision trees, and neural networks, explained in plain language.lilistrongData Preparation strong How to collect, clean, and preprocess data to make it suitable for machine learning 

Machines Learning for Beginners | Foster Bobby | Twarda

bAn advanced book for researchers and graduate students working in machine learning and statistics who want to learn about deep learning, Bayesian inference, generative models, and decision making under uncertainty.b pAn advanced counterpart to iProbabilistic Machine Learning An Introduction, i this high-level textbook provides researchers and graduate students detailed coverage of cutting-edge topics in machine learning, including deep generative modeling, graphical models, Bayesian inference, reinforcement learning, and causality. This volume puts deep learning into a larger statistical context and unifies approaches based on deep learning with ones based on probabilistic modeling and inference. With contributions from top scientists and domain experts from places such as Google, DeepMind, Amazon, Purdue University, NYU, and the University of Washington, this rigorous book is essential to understanding the vital issues in machine learning. pulliCovers generation of high dimensional o

Probabilistic Machine Learning | Murphy Kevin P. | Twarda

pbAn introduction to decision making under uncertainty from a computational perspective, covering both theory and applications ranging from speech recognition to airborne collision avoidance.bppMany important problems involve decision making under uncertainty--that is, choosing actions based on often imperfect observations, with unknown outcomes. Designers of automated decision support systems must take into account the various sources of uncertainty while balancing the multiple objectives of the system. This book provides an introduction to the challenges of decision making under uncertainty from a computational perspective. It presents both the theory behind decision making models and algorithms and a collection of example applications that range from speech recognition to aircraft collision avoidance.ppFocusing on two methods for designing decision agents, planning and reinforcement learning, the book covers probabilistic models, introducing Bayesian networks as a graphical model th

Decision Making Under Uncertainty | Kochenderfer Mykel J. | Twarda

pThe ultimate aim of artificial intelligence A.I. is to understand intelligence and to build intelligent software and robots that come close to the performance of humans. On their way towards this goal, A.I. researchers have developed a number of quite different subdisciplines.ppThis concise and accessible iIntroduction to Artificial Intelligencei supports a foundation or module course on A.I., covering a broad selection of the subdisciplines within this field. The textbook presents concrete algorithms and applications in the areas of agents, logic, search, reasoning under uncertainty, machine learning, neural networks and reinforcement learning.ppTopics and features presents an application-focused and hands-on approach to learning the subject provides study exercises of varying degrees of difficulty at the end of each chapter, with solutions given at the end of the book supports the text with highlighted examples, definitions, theorems, and illustrative cartoons includes chapters on p

Introduction to Artificial Intelligence | Ertel Wolfgang | Paperback | Twarda

1 An Introduction to Artificial Intelligence.- 2 Searching State Spaces.- 3 Multiagent Search.- 4 Propositional Logic.- 5 First-Order Logic.- 6 Machine Learning The Inductive View.- 7 Neural Networks.- 8 Domain-Specific Neural Architectures.- 9 Unsupervised Learning.- 10 Reinforcement Learning.- 11 Probabilistic Graphical Models.- 12 Knowledge Graphs.- 13 Integrating Reasoning and Learning.

Artificial Intelligence | Aggarwal Charu C. | Twarda

pstrongStart with the basics of reinforcement learning and explore deep learning concepts such as deep Q-learning, deep recurrent Q-networks, and policy-based methods with this practical guidestrongppstrongKey Featuresstrongp ul liUse TensorFlow to write reinforcement learning agents for performing challenging tasksli liLearn how to solve finite Markov decision problemsli liTrain models to understand popular video games like Breakoutli ul pstrongBook Descriptionstrongp pVarious intelligent applications such as video games, inventory management software, warehouse robots, and translation tools use reinforcement learning RL to make decisions and perform actions that maximize the probability of the desired outcome. This book will help you to get to grips with the techniques and the algorithms for implementing RL in your machine learning models.p pStarting with an introduction to RL, you'll be guided through different RL environments and frameworks. You'll learn how to implement your own c

The Reinforcement Learning Workshop | Palmas Alessandro | Twarda

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pHow can music withstand the death and destruction brought on by war Global conflicts of the 20th century fundamentally transformed not only national boundaries, power relations, and global economies, but also the arts and culture of every nation involved. An important, unacknowledged aspect of these conflicts is that they have unique musical soundtracks. iMusic in World War IIi explores how music and sound took on radically different dimensions in the United States and Europe before, during, and after World War II. Additionally, the collection examines the impact of radio and film as the disseminators of the war's musical soundtrack. Contributors contend that the European and American soundtrack of World War II was largely one of escapism rather than the lofty, solemn, heroic, and celebratory mode of war music in the past. Furthermore, they explore the variety of experiences of populations forced from their homes and interned in civilian and POW camps in Europe and the United States, 

Music in World War II | Potter Pamela M. | Twarda

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reinforcement learning an introduction