Hands On Machine Learning with Scikit Learn Keras and TensorFlow

Hands On Machine Learning with Scikit Learn  Keras  and TensorFlow

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.

Author: Aurélien Géron

Publisher: "O'Reilly Media, Inc."

ISBN: 9781492032595

Category: Computers

Page: 856

View: 494

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
Categories: Computers

Hands On Machine Learning with Scikit Learn and TensorFlow

Hands On Machine Learning with Scikit Learn and TensorFlow

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.

Author: Aurélien Géron

Publisher: "O'Reilly Media, Inc."

ISBN: 9781491962268

Category: Computers

Page: 574

View: 170

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.
Categories: Computers

Hands On Machine Learning with Scikit Learn Keras and TensorFlow

Hands On Machine Learning with Scikit Learn  Keras  and TensorFlow

Concepts, Tools, and Techniques to Build Intelligent Systems Aurélien Géron. ® OʻREILLY Hands - On Machine Learning with Scikit - Learn , Keras , and TensorFlow Through a series of breakthroughs , Deep Learning has boosted the entire ...

Author: Aurélien Géron

Publisher: O'Reilly Media

ISBN: 9781492032618

Category: Computers

Page: 856

View: 556

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
Categories: Computers

Intelligent Systems and Applications

Intelligent Systems and Applications

32, 1087–1095 (1994) Géron, A.: Hands-on machine learning with Scikit-Learn and TensorFlow: concepts, tools, and techniques to build intelligent systems. O'Reilly Media (2017) Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., ...

Author: Kohei Arai

Publisher: Springer

ISBN: 9783030010577

Category: Technology & Engineering

Page: 1292

View: 623

Gathering the Proceedings of the 2018 Intelligent Systems Conference (IntelliSys 2018), this book offers a remarkable collection of chapters covering a wide range of topics in intelligent systems and computing, and their real-world applications. The Conference attracted a total of 568 submissions from pioneering researchers, scientists, industrial engineers, and students from all around the world. These submissions underwent a double-blind peer review process, after which 194 (including 13 poster papers) were selected to be included in these proceedings. As intelligent systems continue to replace and sometimes outperform human intelligence in decision-making processes, they have made it possible to tackle many problems more effectively. This branching out of computational intelligence in several directions, and the use of intelligent systems in everyday applications, have created the need for such an international conference, which serves as a venue for reporting on cutting-edge innovations and developments. This book collects both theory and application-based chapters on all aspects of artificial intelligence, from classical to intelligent scope. Readers are sure to find the book both interesting and valuable, as it presents state-of-the-art intelligent methods and techniques for solving real-world problems, along with a vision of future research directions.
Categories: Technology & Engineering

Handbook of Artificial Intelligence Techniques in Photovoltaic Systems

Handbook of Artificial Intelligence Techniques in Photovoltaic Systems

[15] A. G ́eron, Hands-On Machine Learning With Scikit-Learn, Keras, and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems, O'Reilly Media, 2019. [16] F.E. Harrell Jr., Regression Modeling Strategies: With ...

Author: Adel Mellit

Publisher: Academic Press

ISBN: 9780128206423

Category: Science

Page: 374

View: 437

Handbook of Artificial Intelligence Techniques in Photovoltaic Systems: Modelling, Control, Optimization, Forecasting and Fault Diagnosis provides readers with a comprehensive and detailed overview of the role of artificial intelligence in PV systems. Covering up-to-date research and methods on how, when and why to use and apply AI techniques in solving most photovoltaic problems, this book will serve as a complete reference in applying intelligent techniques and algorithms to increase PV system efficiency. Sections cover problem-solving data for challenges, including optimization, advanced control, output power forecasting, fault detection identification and localization, and more. Supported by the use of MATLAB and Simulink examples, this comprehensive illustration of AI-techniques and their applications in photovoltaic systems will provide valuable guidance for scientists and researchers working in this area. Includes intelligent methods in real-time using reconfigurable circuits FPGAs, DSPs and MCs Discusses the newest trends in AI forecasting, optimization and control applications Features MATLAB and Simulink examples highlighted throughout
Categories: Science

Software Engineering Perspectives in Intelligent Systems

Software Engineering Perspectives in Intelligent Systems

J. Mach. Learn. Res. 12, 2825– 2830 (2011) 5. Aurélien, G.: Hands-on Machine Learning with Scikit-Learn, Keras, and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems. O'Reilly Media, Inc., Sebastopol (2019) 6.

Author: Radek Silhavy

Publisher: Springer Nature

ISBN: 9783030633196

Category: Technology & Engineering

Page: 954

View: 744

This book constitutes the refereed proceedings of the 4th Computational Methods in Systems and Software 2020 (CoMeSySo 2020) proceedings. Software engineering, computer science and artificial intelligence are crucial topics for the research within an intelligent systems problem domain. The CoMeSySo 2020 conference is breaking the barriers, being held online. CoMeSySo 2020 intends to provide an international forum for the discussion of the latest high-quality research results.
Categories: Technology & Engineering

Programming with TensorFlow

Programming with TensorFlow

Hands-On Machine Learning with Scikit-Learn and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems. [Author: Aurélien Géron] 8. Learn TensorFlow 2.0: Implement Machine Learning And Deep Learning Models With Python.

Author: Kolla Bhanu Prakash

Publisher: Springer Nature

ISBN: 9783030570774

Category: Technology & Engineering

Page: 190

View: 895

This practical book provides an end-to-end guide to TensorFlow, the leading open source software library that helps you build and train neural networks for deep learning, Natural Language Processing (NLP), speech recognition, and general predictive analytics. The book provides a hands-on approach to TensorFlow fundamentals for a broad technical audience—from data scientists and engineers to students and researchers. The authors begin by working through some basic examples in TensorFlow before diving deeper into topics such as CNN, RNN, LSTM, and GNN. The book is written for those who want to build powerful, robust, and accurate predictive models with the power of TensorFlow, combined with other open source Python libraries. The authors demonstrate TensorFlow projects on Single Board Computers (SBCs).
Categories: Technology & Engineering

Hybrid Artificial Intelligent Systems

Hybrid Artificial Intelligent Systems

... A.: Hands-On Machine Learning with Scikit-Learn and TensorFlow: Concepts, Tools, and Techniques for Building Intelligent Systems. O'Reilly Media (2017). https://books.google.es/books?id=I6qkDAEACAAJ Gonzalez-Cava, J.M., et al.

Author: Francisco Javier de Cos Juez

Publisher: Springer

ISBN: 9783319926391

Category: Computers

Page: 755

View: 223

This volume constitutes the refereed proceedings of the 13th International Conference on Hybrid Artificial Intelligent Systems, HAIS 2018, held in Oviedo, Spain, in June 2018. The 62 full papers published in this volume were carefully reviewed and selected from 104 submissions. They are organized in the following topical sections: Neurocomputing, fuzzy systems, rough sets, evolutionary algorithms, Agents andMultiagent Systems, and alike.
Categories: Computers

Intelligent Systems and Applications

Intelligent Systems and Applications

Proceedings of the 2019 Intelligent Systems Conference (IntelliSys) Volume 2 Yaxin Bi, Rahul Bhatia, Supriya Kapoor ... Géron, A.: Hands-on Machine Learning with Scikit-Learn and TensorFlow: Concepts, Tools, and Techniques to Build ...

Author: Yaxin Bi

Publisher: Springer Nature

ISBN: 9783030295134

Category: Technology & Engineering

Page: 1312

View: 655

The book presents a remarkable collection of chapters covering a wide range of topics in the areas of intelligent systems and artificial intelligence, and their real-world applications. It gathers the proceedings of the Intelligent Systems Conference 2019, which attracted a total of 546 submissions from pioneering researchers, scientists, industrial engineers, and students from all around the world. These submissions underwent a double-blind peer-review process, after which 190 were selected for inclusion in these proceedings. As intelligent systems continue to replace and sometimes outperform human intelligence in decision-making processes, they have made it possible to tackle a host of problems more effectively. This branching out of computational intelligence in several directions and use of intelligent systems in everyday applications have created the need for an international conference as a venue for reporting on the latest innovations and trends. This book collects both theory and application based chapters on virtually all aspects of artificial intelligence; presenting state-of-the-art intelligent methods and techniques for solving real-world problems, along with a vision for future research, it represents a unique and valuable asset.
Categories: Technology & Engineering

Probabilistic Machine Learning

Probabilistic Machine Learning

In : Advances in neural information processing systems . 2005 , pp . 529-536 ( page 633 ) . ... Hands - On Machine Learning with Scikit - Learn and TensorFlow Concepts , Tools , and Techniques for Building Intelligent Systems , en .

Author: Kevin P. Murphy

Publisher: MIT Press

ISBN: 9780262369305

Category: Computers

Page: 864

View: 230

A detailed and up-to-date introduction to machine learning, presented through the unifying lens of probabilistic modeling and Bayesian decision theory. This book offers a detailed and up-to-date introduction to machine learning (including deep learning) through the unifying lens of probabilistic modeling and Bayesian decision theory. The book covers mathematical background (including linear algebra and optimization), basic supervised learning (including linear and logistic regression and deep neural networks), as well as more advanced topics (including transfer learning and unsupervised learning). End-of-chapter exercises allow students to apply what they have learned, and an appendix covers notation. Probabilistic Machine Learning grew out of the author’s 2012 book, Machine Learning: A Probabilistic Perspective. More than just a simple update, this is a completely new book that reflects the dramatic developments in the field since 2012, most notably deep learning. In addition, the new book is accompanied by online Python code, using libraries such as scikit-learn, JAX, PyTorch, and Tensorflow, which can be used to reproduce nearly all the figures; this code can be run inside a web browser using cloud-based notebooks, and provides a practical complement to the theoretical topics discussed in the book. This introductory text will be followed by a sequel that covers more advanced topics, taking the same probabilistic approach.
Categories: Computers