Processing ......
Links to Free Computer, Mathematics, Technical Books all over the World
Machine Learning, Deep Learning, Reinforcement Learning, etc.
Related Book Categories:
  • Deep Learning and Neural Networks

    The first comprehensive introduction to Multi-Agent Reinforcement Learning (MARL), covering MARL’s models, solution concepts, algorithmic ideas, technical challenges, and modern approaches.

  • Multi-Agent Reinforcement Learning (Stefano V. Albrecht, et al.)

    The first comprehensive introduction to Multi-Agent Reinforcement Learning (MARL), covering MARL’s models, solution concepts, algorithmic ideas, technical challenges, and modern approaches.

  • Distributional Reinforcement Learning (Marc G. Bellemare, et al)

    Distributional reinforcement learning is a new mathematical formalism for thinking about decisions. This first comprehensive guide provides a new mathematical formalism for thinking about decisions from a probabilistic perspective.

  • Mathematical Analysis of Machine Learning Algorithms (Tong Zhang)

    This self-contained textbook introduces students and researchers of AI to the main mathematical techniques used to analyze machine learning algorithms, with motivations and applications.

  • Machine Learning Algorithms (Jason Brownlee)

    This book takes you on an enlightening journey through the fascinating world of machine learning, helps you harness the real power of machine learning algorithms in order to implement smarter ways of meeting today's overwhelming data needs.

  • Fairness and Machine Learning: Limitations and Opportunities

    This book is an introduction to the intellectual foundations of this recently emergent field, drawing on a diverse range of disciplinary perspectives to identify the opportunities and hazards of automated decision-making.

  • The Hundred-Page Machine Learning Book (Andriy Burkov)

    Everything you really need to know in Machine Learning in a hundred pages! This book provides a great practical guide to get started and execute on ML within a few days without necessarily knowing much about ML apriori.

  • Introduction to Statistical Learning: with Applications in Python

    This book covers the same materials as Introduction to Statistical Learning: with Applications in R (ISLR) but with labs implemented in Python. These labs will be useful both for Python novices, as well as experienced users.

  • The Shallow and the Deep: Old School Machine Learning

    This book is a collection of lecture notes that offers an accessible introduction to Neural Networks and machine learning in general. The focus lies on classical machine learning techniques, with a bias towards classification and regression.

  • Financial Machine Learning (Bryan T. Kelly, et al.)

    This book is designed for both financial economists interested in grasping machine learning tools, as well as for statisticians and machine learners seeking interesting financial contexts where advanced methods may be deployed.

  • Computational Formalism: Art History and Machine Learning

    How the use of machine learning to analyze art images has revived formalism in art history, presenting a golden opportunity for art historians and computer scientists to learn from one another.

  • Machine Learning under Resource Constraints (Katharina Morik)

    It provides a comprehensive overview of the novel approaches to machine learning research that consider resource constraints, as well as the application of the described methods in various domains of science and engineering.

  • An Introduction to Machine Learning Interpretability

    Understanding and trusting models and their results is a hallmark of good science. Get an applied perspective on how this applies to machine learning, including fairness, accountability, transparency, and explainable AI.

  • Machine Learning with Python Tutorial (Bernd Klein)

    This practical guide provides helps to solve machine learning challenges you may encounter in your work. Go beyond theory and concepts by learning the nuts and bolts you need to construct working machine learning applications.

  • Hyperparameter Tuning for Machine Learning: A Practical Guide

    This open access book provides a wealth of hands-on examples that illustrate how hyperparameter tuning can be applied in practice and gives deep insights into the working mechanisms of machine learning (ML) and deep learning (DL) methods.

  • Approaching (Almost) Any Machine Learning Problem

    This book is for people who have some theoretical knowledge of machine learning and deep learning and want to dive into applied machine learning. The book is best enjoyed with a cup of coffee and a laptop/workstation where you can code along.

  • An Introduction to Quantum Machine Learning for Engineers

    This book provides a self-contained introduction to Quantum Machine Learning for an audience of engineers with a background in probability and linear algebra, describes the necessary background, concepts, and tools, covers parametrized quantum circuits, etc.

  • Mathematics for Machine Learning (Marc P. Deisenroth, et al.)

    This self contained textbook bridges the gap between mathematical and machine learning texts, introducing the mathematical concepts with a minimum of prerequisites. It provides a beautiful exposition of the mathematics underpinning modern machine learning.

  • Mathematics for CS and Machine Learning (Jean Gallier, et al.)

    Covering everything you need to know about machine learning, now you can master the mathematics, computer science and statistics behind this field and develop your very own neural networks!

  • Metalearning: Applications to Automated Machine Learning

    This book offers a comprehensive and thorough introduction to almost all aspects of metalearning and Automated Machine Learning (AutoML). It can help developers to develop systems that can improve themselves through experience.

  • An Introduction to Deep Reinforcement Learning

    Deep reinforcement learning is the combination of reinforcement learning (RL) and deep learning. This field of research has recently been able to solve a wide range of complex decision-making tasks that were previously out of reach for a machine.

  • Probabilistic Machine Learning: An Introduction (Kevin Murphy)

    This book is a comprehensive introduction to machine learning that uses probabilistic models and inference as a unifying approach. It is written in an informal, accessible style, complete with pseudo-code for the most important algorithms.

  • Probabilistic Machine Learning: Advanced Topics (Kevin Murphy)

    This book expands the scope of Machine Learning to encompass more challenging problems, discusses methods for discovering 'insights' about data, and how to use probabilistic models for causal inference and decision making under uncertainty.

  • The Big Book of Machine Learning Use Cases

    This how-to reference guide provides everything you need - including code samples and notebooks - to start putting Machine Learning to work. It's a collection of technical blogs from industry thought leaders with practical use cases you can leverage today.

  • Pen and Paper Exercises in Machine Learning (Michael Gutmann)

    This is a collection of (mostly) pen-and-paper exercises in machine learning. The author assumes basic calculus, linear algebra, probability and statistics but no prior exposure to machine learning.

  • Interpretable Machine Learning: Black Box Models Explainable

    This book explains to you how to make (supervised) machine learning models interpretable. The book focuses on machine learning models for tabular data (also called relational or structured data) and less on computer vision and NLP tasks.

  • Machine Learning with Neural Networks (Bernhard Mehlig)

    This modern and self-contained book offers a clear and accessible introduction to the important topic of machine learning with neural networks. It provides comprehensive coverage of neural networks, their evolution, their structure, their applications, etc.

  • Machine Learning from Scratch (Danny Friedman)

    This book covers the building blocks of the most common methods in machine learning. This set of methods is like a toolbox for machine learning engineers. Those entering the field of machine learning should feel comfortable with this toolbox.

  • Foundations of Machine Learning (Mehryar Mohri, et al)

    This book is a general introduction to machine learning. It covers fundamental modern topics in machine learning while providing the theoretical basis and conceptual tools needed for the discussion and justification of algorithms.

  • The Mechanics of Machine Learning (Terence Parr, et al)

    This book is a primer on machine learning for programmers trying to get up to speed quickly. You'll learn how machine learning works and how to apply it in practice. Focus on just a few powerful models (algorithms) that are extremely effective on real problems,

  • Pattern Recognition and Machine Learning (Christopher Bishop)

    This is the first textbook on pattern recognition to present the Bayesian viewpoint. The book presents approximate inference algorithms that permit fast approximate answers in situations where exact answers are not feasible.

  • Gaussian Processes for Machine Learning (Carl E. Rasmussen)

    This book provides a long-needed systematic and unified treatment of theoretical and practical aspects of Gaussian Processes (GPs) in machine learning. It deals with the supervised-learning problem for both regression and classification.

  • Handbook of Digital Face Manipulation and Detection

    This open access book provides the first comprehensive collection of studies dealing with the hot topic of digital face manipulation such as DeepFakes, Face Morphing, or Reenactment. It combines the both biometrics and media forensics fields.

  • Understanding Machine Learning: From Theory to Algorithms

    Explains the principles behind the automated learning approach and the considerations underlying its usage. Provides an extensive theoretical account of the fundamental ideas underlying machine learning and the mathematical derivations.

  • Machine Learning Yearning (Andrew Ng)

    In this book you will learn how to align on ML strategies in a team setting, as well as how to set up development (dev) sets and test sets. After finishing this book, you will have a deep understanding of how to set technical direction for a machine learning project.

  • Statistical Foundations of Machine Learning: The Handbook

    This book aims to present the statistical foundations of machine learning intended as the discipline which deals with the automatic design of models from data. All the examples are implemented in the statistical programming language R.

  • Statistics and Machine Learning in Python (Edouard Duchesnay)

    Illustrates the fundamental concepts that link statistics and machine learning, so that the reader can not only employ statistical and machine learning models using modern Python modules, but also understand their relative strengths and weaknesses.

  • An Introduction to Statistical Learning (Gareth James, et al)

    It provides an accessible overview of the field of Statistical Learning, an essential toolset for making sense of the vast and complex data sets that have emerged in fields ranging from biology to finance to marketing to astrophysics in the past twenty years.

  • The Elements of Statistical Learning: Data Mining, Inference, etc.

    This book describes the important ideas in these areas in a common conceptual framework. While the approach is statistical, the emphasis is on concepts rather than mathematics. Many examples are given, with a liberal use of color graphics.

  • On the Path to AI: Foundations of the Machine Learning Age

    This book explores machine learning and its impact on how we make sense of the world. It introduces readers to the key concepts of machine learning, discusses the potential applications and limitations of predictions generated by machines using data.

  • Algorithmic Aspects of Machine Learning (Ankur Moitra)

    This book bridges theoretical computer science and machine learning by exploring what the two sides can teach each other. It emphasizes the need for flexible, tractable models that better capture not what makes machine learning hard, but what makes it easy.

  • Data Mining and Machine Learning: Concepts and Algorithms

    This textbook for senior undergraduate and graduate courses provides a comprehensive, in-depth overview of data mining, machine learning and statistics. The main parts of the book include exploratory data analysis, pattern mining, clustering, and classification.

  • Lifelong Machine Learning (Zhiyuan Chen, et al)

    This book is an introduction to an advanced Machine Learning paradigm that continuously learns by accumulating past knowledge that it then uses in future learning and problem solving. This learning ability is one of the hallmarks of human intelligence.

  • Reinforcement Learning: An Introduction, Second Edition

    It provides a clear and simple account of the key ideas and algorithms of reinforcement learning that is accessible to readers in all the related disciplines. Focuses on core online learning algorithms, with the more mathematical material set off in shaded boxes.

  • Machine Learning Engineering (Andriy Burkov)

    The most complete applied AI book out there. It is filled with best practices and design patterns of building reliable machine learning solutions that scale. It embraces the most important thing you need to know about machine learning: mistakes are possible.

  • A Brief Introduction to Machine Learning for Engineers

    This book aims at providing an introduction to key concepts, algorithms, and theoretical frameworks in machine learning, including supervised and unsupervised learning, statistical learning theory, probabilistic graphical models and approximate inference.

  • Probabilistic Machine Learning for Civil Engineers

    This comprehensive textbook presents basic machine learning methods for civil engineers who do not have a specialized background in statistics or in computer science. It includes several case studies that students and professionals will appreciate.

  • Automated Machine Learning: Methods, Systems, Challenges

    This book presents the first comprehensive overview of general methods in Automated Machine Learning (AutoML), collects descriptions of existing systems based on these methods, and discusses the first series of international challenges of AutoML systems.

  • Python Machine Learning Projects (Brian Boucheron, et al)

    This book tries to equip the developers of today and tomorrow with tools they can use to better understand, evaluate, and shape machine learning. If you know some Python and you want to use machine learning and deep learning, pick up this book.

  • A Course in Machine Learning (Hal Daume III)

    This is a set of introductory materials that covers most major aspects of modern machine learning (supervised learning, unsupervised learning, large margin methods, probabilistic modeling, learning theory, etc.).

  • Machine Learning with TensorFlow (Nishant Shukla)

    This book gives readers a solid foundation in machine-learning concepts plus hands-on experience coding TensorFlow with Python. You'll learn the basics by working with classic prediction, classification, and clustering algorithms.

  • Introduction to Online Convex Optimization (Elad Hazan)

    This book presents a robust machine learning approach that contains elements of mathematical optimization, game theory, and learning theory: an optimization method that learns from experience as more aspects of the problem are observed.

  • Convex Optimization for Machine Learning (Changho Suh)

    This book covers an introduction to convex optimization, one of the powerful and tractable optimization problems that can be efficiently solved on a computer. The goal is to help develop a sense of what convex optimization is, and how it can be used.

  • AI Crash Course: Hands-on Introduction to Machine Learning

    This book teaches everyone to build an AI to work in their applications. Once you've read this book, you're only limited by your imagination. It gives you everything you need to build AI systems with reinforcement learning and deep learning.

  • Machine Learning: The Complete Guide (WikiBooks)

    Provides an extensive theoretical account of the fundamental ideas underlying machine learning and the mathematical derivations that transform these principles into practical algorithms, covers a wide array of topics that have not been addressed by other books.

  • Machine Learning: A Probabilistic Perspective (Kevin Murphy)

    This textbook offers a comprehensive and self-contained introduction to the field of machine learning, based on a unified, probabilistic approach. It is written in an informal, accessible style, complete with pseudo-code for the most important algorithms.

  • Statistical Learning and Sequential Prediction

    This book focuses on theoretical aspects of Statistical Learning and Sequential Prediction, a unified approach to analyzing learning in both scenarios, brings together ideas from probability and statistics, game theory, algorithms, and optimization.

  • Machine Learning for Cyber Physical Systems (Jurgen Beyerer)

    This book presents new approaches to Machine Learning for Cyber Physical Systems, experiences and visions. Typical Cyber Physical Systems applications are condition monitoring, predictive maintenance, image processing and diagnosis.

  • Algorithms for Reinforcement Learning (Csaba Szepesvari)

    This book focuses on those algorithms of reinforcement learning that build on the powerful theory of dynamic programming. It gives a fairly comprehensive catalog of learning problems, describe the core ideas, etc.

  • Reinforcement Learning and Optimal Control (Dimitri Bertsekas)

    The purpose of the book is to consider large and challenging multistage decision problems, which can be solved in principle by dynamic programming and optimal control, but their exact solution is computationally intractable.

  • Efficient Learning Machines: Theories, Concepts, and Applications

    It weaves together the theoretical exposition, design principles, and practical applications of efficient machine learning, aims to equip engineers, students of engineering, and system designers to design and create new and more efficient machine learning systems.

  • Bayesian Reasoning and Machine Learning (David Barber)

    This practical introduction is ideally suited to computer scientists without a background in calculus and linear algebra. You'll develop analytical and problem-solving skills that equip them for the real world. Numerous examples and exercises are provided.

  • Introduction to Machine Learning (Alex Smola, et al)

    This book is a introductory textbook on the subject, discussesing many methods from different fields, including statistics, pattern recognition, neural networks, artificial intelligence, signal processing, control, and data mining.

  • Free and Open Machine Learning (Maikel Mardjan)

    This book describes an open machine learning architecture. Including key aspects that are involved for real business use. It focuses on Free and Open Source Software (FOSS) machine learning software and open datasets.

  • Boosting: Foundations and Algorithms (Robert E. Schapire, et al)

    This book, written by the inventors of the Boosting method, brings together, organizes, simplifies, and substantially extends two decades of research on boosting, presenting both theory and applications in a way that is accessible to readers from diverse backgrounds.

  • Azure Cognitive Services Succinctly (Ed Freitas)

    A practical guide to working with cognitive APIs developed by Microsoft and provided with the Azure platform to developers and businesses. It delivers an accessible guide to integrating computer vision, decision-making, speech, and more into your apps.

  • Support Vector Machines Succinctly (Alexandre Kowalczyk)

    This book guides readers through the building blocks of Support Vector Machines (SVMs), from basic concepts to crucial problem-solving algorithms. It also includes numerous code examples and a lengthy bibliography for further study.

  • The LION Way: Machine Learning Plus Intelligent Optimization

    This book captures the state of the art of the interaction between optimization and machine learning in a way that is accessible to researchers in both fields. Optimization approaches have enjoyed prominence in machine learning because of their wide applicability and attractive theoretical properties.

  • Introduction to Machine Learning (Amnon Shashua)

    This book will be exploring machine learning, the concepts that run these technologies and by the time you get to the end you will have more knowledge than many and will be equipped to start building your own applications.

  • Machine Learning for Data Streams: Practical Examples in MOA

    This book presents algorithms and techniques used in data stream mining and real-time analytics. Taking a hands-on approach, it demonstrates the techniques using MOA (Massive Online Analysis), a popular, freely available open-source software framework, allowing readers to try out the techniques after reading the explanations.

  • Machine Learning and Data Mining (Aaron Hertzmann)

    This is an introduction to the main issues associated with the basics of machine learning and the algorithms used in data mining. It offers a grounding in machine learning concepts as well as practical advice on techniques in real-world data mining.

  • Machine Learning, Neural and Statistical Classification (D. Michie)

    Statistical, machine learning and neural network approaches to classification are all covered in this book to provide an objective assessment of the potential for machine learning algorithms in solving significant commercial and industrial problems, widening the foundation for exploitation of these and related algorithms.

  • Deep Learning (Ian Goodfellow, et al)

    Written by three experts, this is the only comprehensive book on the subject. It offers mathematical and conceptual background, covering relevant concepts in linear algebra, probability theory and information theory, numerical computation, and machine learning.

  • O'Reilly® Machine Learning for Designers (Patrick Hebron)

    This book not only introduces you to contemporary machine learning systems, but also provides a conceptual framework to help you integrate machine-learning capabilities into your user-facing designs, using tangible, real-world examples.

  • O'Reilly® The Future of Machine Intelligence (David Beyer)

    This exclusive report unpacks concepts and innovations that represent the frontiers of ever-smarter machines. You’ll get a rare glimpse into this exciting field through the eyes of some of its leading minds.

  • Deep Learning Tutorials (LISA Lab)

    The tutorials presented here will introduce you to some of the most important deep learning algorithms and will also show you how to run them using Theano. Theano is a python library that makes writing deep learning models easy, and gives the option of training them on a GPU.

  • Foundation Models for Natural Language Processing

    This open access book provides a comprehensive overview of the state of the art in research and applications of Foundation Models and is intended for readers familiar with basic Natural Language Processing (NLP) concepts.

  • Speech and Language Processing (Dan Jurafsky, et al)

    This text takes an empirical approach to the subject, based on applying statistical and other machine-learning algorithms to large corporations. It describes a unified vision of speech and language processing. Emphasis is on practical and scientific applications.

  • New Advances in Machine Learning (Yagang Zhang)

    This book provides an up-to-date and systematical introduction to the principles and algorithms of machine learning, as well as a good introduction to many approaches of machine learning, and the source of useful bibliographical information.

  • Application of Machine Learning (Yagang Zhang)

    Present the latest applications of machine learning, which mainly include: speech recognition, traffic and fault classification, surface quality prediction in laser machining, network security and bioinformatics, enterprise credit risk evaluation, and so on.

  • Numerical Algorithms: Computer Vision, Machine Learning, etc.

    This book presents a new approach to numerical analysis for modern computer scientists, covers a wide range of topics - from numerical linear algebra to optimization and differential equations - focusing on real-world motivation and unifying themes.

  • Machine Translation: An Introductory Guide (Doug Arnold, et al)

    This introductory book looks at all aspects of Machine Translation: covering questions of what it is like to use a modern Machine Translation system, through questions about how it is done, to questions of evaluating systems, and what developments can be foreseen.

  • Inductive Logic Programming: Techniques and Applications

    This book is an introduction to inductive logic programming (ILP), which aims at a formal framework as well as practical algorithms for inductively learning relational descriptions in the form of logic programs.

  • Learning Deep Architectures for AI (Yoshua Bengio)

    This book discusses the motivations for and principles of learning algorithms for deep architectures. By analyzing and comparing recent results with different learning algorithms for deep architectures, explanations for their success are proposed.

  • Neural Networks and Deep Learning (Michael Nielsen)

    Neural networks and deep learning currently provide the best solutions to many problems in image recognition, speech recognition, and natural language processing. This book will teach you the core concepts behind neural networks and deep learning.

  • Theory and Novel Applications of Machine Learning (Meng Joo Er)

    This books reports the latest developments and futuristic trends in Machine Learning. It involves broad topics including Fuzzy Logic, Neural Networks (NNs), Evolutionary Algorithms (EAs), Probability and Statistics, Decision Trees, etc.

  • Computational Engineering of Historical Memories (Andrea Nanetti)

    This book outlines a methodology for deploying artificial intelligence and machine learning to enhance historical research. It presents the theory behind this methodology from a humanities perspective and discusses its practical applications.

  • Machine Learning Using C# Succinctly (James McCaffrey)

    This book shows several different approaches to applying machine learning to data analysis and prediction problems. It also demonstrates different clustering and classification techniques, and explains how effective these techniques can be.

  • Computer Vision: Models, Learning, and Inference (Simon Prince)

    The book starts from the basics of probability and model fitting and works up to real examples that the reader can implement and modify to build useful vision systems. The detailed methodological presentation is useful for practitioners of computer vision.

  • Robot Learning (Suraiya Jabin)

    It gives a focused introduction to the primary themes in a Robot learning course and demonstrates the relevance and practicality of various Machine Learning algorithms to a wide variety of real-world applications from evolutionary techniques to reinforcement learning, classification, control, uncertainty and many other important fields.

  • Machine Learning (Yagang Zhang)

    This book presents today's state and development tendencies of machine learning. Taking into account the large amount of knowledge about machine learning and practice presented in the book, it is divided into three major parts: Introduction, Machine Learning Theory and Applications.

  • Machine Learning (Abdelhamid Mellouk, et al)

    The book provides an extensive theoretical account of the fundamental ideas underlying machine learning and the mathematical derivations that transform these principles into practical algorithms.

  • Information Theory, Inference, and Learning Algorithms (D MacKay)

    This textbook introduces Information theory in tandem with applications. Information theory is taught alongside practical communication systems, such as arithmetic coding for data compression and sparse-graph codes for error-correction.

  • AI Algorithms, Data Structures, and Idioms in Prolog, Lisp, and Java

    Illustrateing how to program AI algorithms in Lisp, Prolog, and Java. Topics include: simple production-like system based on logic, logic-based learning, and natural language parsing.

  • Recent Advances in Face Recognition (Kresimir Delac, et al)

    This book provides a broad overview on face recognition and identified trends for future developments and the means for implementing robust systems..

  • From Bricks to Brains: Embodied Cognitive Science of LEGO Robots

    This book introduces embodied cognitive science and illustrates its foundational ideas through the construction and observation of LEGO Mindstorms robots.

  • Mind, Body, World: Foundations of Cognitive Science (M. Dawson)

    Intended to introduce the foundations of cognitive science, this book addresses a number of questions currently being asked by those practicing in the field of cognitive science. It highlights the fundamental tensions and lines of fragmentation of cognitive science.

Book Categories
Other Categories
Resources and Links