Systems That Learn
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Systems That Learn
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Author :
language : en
Publisher:
Release Date : 1999
Systems That Learn written by and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 1999 with Human information processing categories.
Systems That Learn
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Author : Daniel N. Osherson
language : en
Publisher:
Release Date : 1986
Systems That Learn written by Daniel N. Osherson and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 1986 with Learning categories.
Hands On Machine Learning With Scikit Learn Keras And Tensorflow
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Author : Aurélien Géron
language : en
Publisher: O'Reilly Media
Release Date : 2019-09-05
Hands On Machine Learning With Scikit Learn Keras And Tensorflow written by Aurélien Géron and has been published by O'Reilly Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019-09-05 with Computers categories.
Through a series of recent breakthroughs, deep learning has boosted the entire field of machine learning. Now, even programmers who know close to nothing about this technology can use simple, efficient tools to implement programs capable of learning from data. This practical book shows you how. By using concrete examples, minimal theory, and two production-ready Python frameworks—Scikit-Learn and TensorFlow—author Aurélien Géron helps you gain an intuitive understanding of the concepts and tools for building intelligent systems. You’ll learn a range of techniques, starting with simple linear regression and progressing to deep neural networks. With exercises in each chapter to help you apply what you’ve learned, all you need is programming experience to get started. Explore the machine learning landscape, particularly neural nets Use Scikit-Learn to track an example machine-learning project end-to-end Explore several training models, including support vector machines, decision trees, random forests, and ensemble methods Use the TensorFlow library to build and train neural nets Dive into neural net architectures, including convolutional nets, recurrent nets, and deep reinforcement learning Learn techniques for training and scaling deep neural nets
Learning How To Learn
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Author : Barbara Oakley, PhD
language : en
Publisher: Penguin
Release Date : 2018-08-07
Learning How To Learn written by Barbara Oakley, PhD and has been published by Penguin this book supported file pdf, txt, epub, kindle and other format this book has been release on 2018-08-07 with Juvenile Nonfiction categories.
A surprisingly simple way for students to master any subject--based on one of the world's most popular online courses and the bestselling book A Mind for Numbers A Mind for Numbers and its wildly popular online companion course "Learning How to Learn" have empowered more than two million learners of all ages from around the world to master subjects that they once struggled with. Fans often wish they'd discovered these learning strategies earlier and ask how they can help their kids master these skills as well. Now in this new book for kids and teens, the authors reveal how to make the most of time spent studying. We all have the tools to learn what might not seem to come naturally to us at first--the secret is to understand how the brain works so we can unlock its power. This book explains: Why sometimes letting your mind wander is an important part of the learning process How to avoid "rut think" in order to think outside the box Why having a poor memory can be a good thing The value of metaphors in developing understanding A simple, yet powerful, way to stop procrastinating Filled with illustrations, application questions, and exercises, this book makes learning easy and fun.
Learn Systems Thinking
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Author : Wallace Wright
language : en
Publisher: Charlie Creative Lab
Release Date : 2020-11-06
Learn Systems Thinking written by Wallace Wright and has been published by Charlie Creative Lab this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-11-06 with categories.
The challenges of the 21st century - from humanitarian to economic to environmental-demand new ways of thinking and more complex, flexible ways of acting. We no longer live in a disconnected world, due to the advances in technology and travel; a globalized world and economy require different approaches. "Systems thinking" is a highly developed and influential way of looking at the myriad and complicated interactions between humans, institutions, and natural processes.This book will help you understand the basics of systems thinking while providing you with the motivation to apply these tenants to your professional and personal life. From a thorough grounding in its basic principles to examples of how systems thinking works in real-time situations, the lessons and suggestions herein will guide you through the basic tenants, such as interconnectedness, synthesis, emergence, feedback loops, causality, and systems mapping. Move past the traditional forms of linear, mechanistic thinking to a more complex and dynamic way to solve problems, plan strategically, and make smarter decisions.Some of the specific material you will encounter in this book includes: An overall understanding of systems thinking and how each basic tenant leads to a greater understanding of this new approach to professional and personal success A detailed understanding of the archetypes that are identified within systems thinking, such as drifting goals and success to the successful, and how to utilize those archetypes in developing plans Chapters on how to specifically cultivate problem-solving skills, strategic planning, and forward-thinking decision making An understanding of mental modes and how we use them and how to change them to incorporate into our larger vision for the future A pragmatic guide to achieving success within a complex and dynamic world that requires new and original ways of thinking about how we interact with others and with systems themselves Whether you implement the practices of systems thinking within an organization or in your own interactions with the world, you will find it to be a dynamic and creative way to confront whatever challenges stand before you. The world in which we live isn't static; therefore, our responses to problem-solving and making smart decisions must also be active and engaged. Employing the new tools proposed by systems thinking will assist us cultivating this kind of adaptive and responsive skill set. Systems thinking encourages us to think in a three dimensional way and learning the terms and tools of this new approach to business, and the world can assist us in solving the complex problems that we face, as well as encourage us to plan well and make smarter decisions for our future.
Efficient Learning Machines
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Author : Mariette Awad
language : en
Publisher: Apress
Release Date : 2015-04-27
Efficient Learning Machines written by Mariette Awad and has been published by Apress this book supported file pdf, txt, epub, kindle and other format this book has been release on 2015-04-27 with Computers categories.
Machine learning techniques provide cost-effective alternatives to traditional methods for extracting underlying relationships between information and data and for predicting future events by processing existing information to train models. Efficient Learning Machines explores the major topics of machine learning, including knowledge discovery, classifications, genetic algorithms, neural networking, kernel methods, and biologically-inspired techniques. Mariette Awad and Rahul Khanna’s synthetic approach weaves together the theoretical exposition, design principles, and practical applications of efficient machine learning. Their experiential emphasis, expressed in their close analysis of sample algorithms throughout the book, aims to equip engineers, students of engineering, and system designers to design and create new and more efficient machine learning systems. Readers of Efficient Learning Machines will learn how to recognize and analyze the problems that machine learning technology can solve for them, how to implement and deploy standard solutions to sample problems, and how to design new systems and solutions. Advances in computing performance, storage, memory, unstructured information retrieval, and cloud computing have coevolved with a new generation of machine learning paradigms and big data analytics, which the authors present in the conceptual context of their traditional precursors. Awad and Khanna explore current developments in the deep learning techniques of deep neural networks, hierarchical temporal memory, and cortical algorithms. Nature suggests sophisticated learning techniques that deploy simple rules to generate highly intelligent and organized behaviors with adaptive, evolutionary, and distributed properties. The authors examine the most popular biologically-inspired algorithms, together with a sample application to distributed datacenter management. They also discuss machine learning techniques for addressing problems of multi-objective optimization in which solutions in real-world systems are constrained and evaluated based on how well they perform with respect to multiple objectives in aggregate. Two chapters on support vector machines and their extensions focus on recent improvements to the classification and regression techniques at the core of machine learning.
Deep Learning
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Author : Ian Goodfellow
language : en
Publisher: MIT Press
Release Date : 2016-11-10
Deep Learning written by Ian Goodfellow and has been published by MIT Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2016-11-10 with Computers categories.
An introduction to a broad range of topics in deep learning, covering mathematical and conceptual background, deep learning techniques used in industry, and research perspectives. “Written by three experts in the field, Deep Learning is the only comprehensive book on the subject.” —Elon Musk, cochair of OpenAI; cofounder and CEO of Tesla and SpaceX Deep learning is a form of machine learning that enables computers to learn from experience and understand the world in terms of a hierarchy of concepts. Because the computer gathers knowledge from experience, there is no need for a human computer operator to formally specify all the knowledge that the computer needs. The hierarchy of concepts allows the computer to learn complicated concepts by building them out of simpler ones; a graph of these hierarchies would be many layers deep. This book introduces a broad range of topics in deep learning. The text offers mathematical and conceptual background, covering relevant concepts in linear algebra, probability theory and information theory, numerical computation, and machine learning. It describes deep learning techniques used by practitioners in industry, including deep feedforward networks, regularization, optimization algorithms, convolutional networks, sequence modeling, and practical methodology; and it surveys such applications as natural language processing, speech recognition, computer vision, online recommendation systems, bioinformatics, and videogames. Finally, the book offers research perspectives, covering such theoretical topics as linear factor models, autoencoders, representation learning, structured probabilistic models, Monte Carlo methods, the partition function, approximate inference, and deep generative models. Deep Learning can be used by undergraduate or graduate students planning careers in either industry or research, and by software engineers who want to begin using deep learning in their products or platforms. A website offers supplementary material for both readers and instructors.
Learning Classifier Systems
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Author : Jaume Bacardit
language : en
Publisher: Springer
Release Date : 2008-10-17
Learning Classifier Systems written by Jaume Bacardit and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2008-10-17 with Computers categories.
This book constitutes the thoroughly refereed joint post-conference proceedings of two consecutive International Workshops on Learning Classifier Systems that took place in Seattle, WA, USA in July 2006, and in London, UK, in July 2007 - all hosted by the Genetic and Evolutionary Computation Conference, GECCO. The 14 revised full papers presented were carefully reviewed and selected from the workshop contributions. The papers are organized in topical sections on knowledge representation, analysis of the system, mechanisms, new directions, as well as applications.
Hands On Machine Learning With R
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Author : Brad Boehmke
language : en
Publisher: CRC Press
Release Date : 2019-11-07
Hands On Machine Learning With R written by Brad Boehmke and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019-11-07 with Business & Economics categories.
Hands-on Machine Learning with R provides a practical and applied approach to learning and developing intuition into today’s most popular machine learning methods. This book serves as a practitioner’s guide to the machine learning process and is meant to help the reader learn to apply the machine learning stack within R, which includes using various R packages such as glmnet, h2o, ranger, xgboost, keras, and others to effectively model and gain insight from their data. The book favors a hands-on approach, providing an intuitive understanding of machine learning concepts through concrete examples and just a little bit of theory. Throughout this book, the reader will be exposed to the entire machine learning process including feature engineering, resampling, hyperparameter tuning, model evaluation, and interpretation. The reader will be exposed to powerful algorithms such as regularized regression, random forests, gradient boosting machines, deep learning, generalized low rank models, and more! By favoring a hands-on approach and using real word data, the reader will gain an intuitive understanding of the architectures and engines that drive these algorithms and packages, understand when and how to tune the various hyperparameters, and be able to interpret model results. By the end of this book, the reader should have a firm grasp of R’s machine learning stack and be able to implement a systematic approach for producing high quality modeling results. Features: · Offers a practical and applied introduction to the most popular machine learning methods. · Topics covered include feature engineering, resampling, deep learning and more. · Uses a hands-on approach and real world data.
Learning Classifier Systems
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Author : Pier Luca Lanzi
language : en
Publisher: Springer Science & Business Media
Release Date : 2003-11-24
Learning Classifier Systems written by Pier Luca Lanzi and has been published by Springer Science & Business Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2003-11-24 with Computers categories.
This book constitutes the refereed proceedings of the 5th International Workshop on Learning Classifier Systems, IWLCS 2003, held in Granada, Spain in September 2003 in conjunction with PPSN VII. The 10 revised full papers presented together with a comprehensive bibliography on learning classifier systems were carefully reviewed and selected during two rounds of refereeing and improvement. All relevant issues in the area are addressed.