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Paperback or Softback. Zustand: New. Python Deep Learning: Next generation techniques to revolutionize computer vision, AI, speech and data analysis. Book.

Zustand: As New. Unread book in perfect condition.

Zustand: As New. Unread book in perfect condition.

Digital. Zustand: New. Take your machine learning skills to the next level by mastering Deep Learning concepts and algorithms using Python.About This Book. Explore and create intelligent systems using cutting-edge deep learning techniques. Implement deep learning algorithms and work with revolutionary libraries in Python. Get real-world examples and easy-to-follow tutorials on Theano, TensorFlow, H2O and moreWho This Book Is ForThis book is for Data Science practitioners as well as aspirants who have a basic foundational understanding of Machine Learning concepts and some programming experience with Python. A mathematical background with a conceptual understanding of calculus and statistics is also desired.What You Will Learn. Get a practical deep dive into deep learning algorithms. Explore deep learning further with Theano, Caffe, Keras, and TensorFlow. Learn about two of the most powerful techniques at the core of many practical deep learning implementations: Auto-Encoders and Restricted Boltzmann Machines. Dive into Deep Belief Nets and Deep Neural Networks. Discover more deep learning algorithms with Dropout and Convolutional Neural Networks. Get to know device strategies so you can use deep learning algorithms and libraries in the real worldIn DetailWith an increasing interest in AI around the world, deep learning has attracted a great deal of public attention. Every day, deep learning algorithms are used broadly across different industries.The book will give you all the practical information available on the subject, including the best practices, using real-world use cases. You will learn to recognize and extract information to increase predictive accuracy and optimize results.Starting with a quick recap of important machine learning concepts, the book will delve straight into deep learning principles using Sci-kit learn. Moving ahead, you will learn to use the latest open source libraries such as Theano, Keras, Google's TensorFlow, and H20. Use this guide to uncover the difficulties of pattern recognition, scaling data with greater accuracy and discussing deep learning algorithms and techniques.Whether you want to dive deeper into Deep Learning, or want to investigate how to get more out of this powerful technology, you'll find everything inside.Style and approachPython Machine Learning by example follows practical hands on approach. It walks you through the key elements of Python and its powerful machine learning libraries with the help of real world projects.

Zustand: New.

Paperback. Zustand: New. 2nd Revised edition. Learn advanced state-of-the-art deep learning techniques and their applications using popular Python librariesKey FeaturesBuild a strong foundation in neural networks and deep learning with Python librariesExplore advanced deep learning techniques and their applications across computer vision and NLPLearn how a computer can navigate in complex environments with reinforcement learningBook DescriptionWith the surge in artificial intelligence in applications catering to both business and consumer needs, deep learning is more important than ever for meeting current and future market demands. With this book, you'll explore deep learning, and learn how to put machine learning to use in your projects.This second edition of Python Deep Learning will get you up to speed with deep learning, deep neural networks, and how to train them with high-performance algorithms and popular Python frameworks. You'll uncover different neural network architectures, such as convolutional networks, recurrent neural networks, long short-term memory (LSTM) networks, and capsule networks. You'll also learn how to solve problems in the fields of computer vision, natural language processing (NLP), and speech recognition. You'll study generative model approaches such as variational autoencoders and Generative Adversarial Networks (GANs) to generate images. As you delve into newly evolved areas of reinforcement learning, you'll gain an understanding of state-of-the-art algorithms that are the main components behind popular games Go, Atari, and Dota.By the end of the book, you will be well-versed with the theory of deep learning along with its real-world applications.What you will learnGrasp the mathematical theory behind neural networks and deep learning processesInvestigate and resolve computer vision challenges using convolutional networks and capsule networksSolve generative tasks using variational autoencoders and Generative Adversarial NetworksImplement complex NLP tasks using recurrent networks (LSTM and GRU) and attention modelsExplore reinforcement learning and understand how agents behave in a complex environmentGet up to date with applications of deep learning in autonomous vehiclesWho this book is forThis book is for data science practitioners, machine learning engineers, and those interested in deep learning who have a basic foundation in machine learning and some Python programming experience. A background in mathematics and conceptual understanding of calculus and statistics will help you gain maximum benefit from this book.

Paperback. Zustand: New.

Paperback. Zustand: New.

Zustand: New.

Zustand: New.

Digital. Zustand: New. Take your machine learning skills to the next level by mastering Deep Learning concepts and algorithms using Python.About This Book. Explore and create intelligent systems using cutting-edge deep learning techniques. Implement deep learning algorithms and work with revolutionary libraries in Python. Get real-world examples and easy-to-follow tutorials on Theano, TensorFlow, H2O and moreWho This Book Is ForThis book is for Data Science practitioners as well as aspirants who have a basic foundational understanding of Machine Learning concepts and some programming experience with Python. A mathematical background with a conceptual understanding of calculus and statistics is also desired.What You Will Learn. Get a practical deep dive into deep learning algorithms. Explore deep learning further with Theano, Caffe, Keras, and TensorFlow. Learn about two of the most powerful techniques at the core of many practical deep learning implementations: Auto-Encoders and Restricted Boltzmann Machines. Dive into Deep Belief Nets and Deep Neural Networks. Discover more deep learning algorithms with Dropout and Convolutional Neural Networks. Get to know device strategies so you can use deep learning algorithms and libraries in the real worldIn DetailWith an increasing interest in AI around the world, deep learning has attracted a great deal of public attention. Every day, deep learning algorithms are used broadly across different industries.The book will give you all the practical information available on the subject, including the best practices, using real-world use cases. You will learn to recognize and extract information to increase predictive accuracy and optimize results.Starting with a quick recap of important machine learning concepts, the book will delve straight into deep learning principles using Sci-kit learn. Moving ahead, you will learn to use the latest open source libraries such as Theano, Keras, Google's TensorFlow, and H20. Use this guide to uncover the difficulties of pattern recognition, scaling data with greater accuracy and discussing deep learning algorithms and techniques.Whether you want to dive deeper into Deep Learning, or want to investigate how to get more out of this powerful technology, you'll find everything inside.Style and approachPython Machine Learning by example follows practical hands on approach. It walks you through the key elements of Python and its powerful machine learning libraries with the help of real world projects.

Zustand: New. pp. 406.

Zustand: As New. Unread book in perfect condition.

Zustand: As New. Unread book in perfect condition.

Digital. Zustand: New. Take your machine learning skills to the next level by mastering Deep Learning concepts and algorithms using Python.About This Book. Explore and create intelligent systems using cutting-edge deep learning techniques. Implement deep learning algorithms and work with revolutionary libraries in Python. Get real-world examples and easy-to-follow tutorials on Theano, TensorFlow, H2O and moreWho This Book Is ForThis book is for Data Science practitioners as well as aspirants who have a basic foundational understanding of Machine Learning concepts and some programming experience with Python. A mathematical background with a conceptual understanding of calculus and statistics is also desired.What You Will Learn. Get a practical deep dive into deep learning algorithms. Explore deep learning further with Theano, Caffe, Keras, and TensorFlow. Learn about two of the most powerful techniques at the core of many practical deep learning implementations: Auto-Encoders and Restricted Boltzmann Machines. Dive into Deep Belief Nets and Deep Neural Networks. Discover more deep learning algorithms with Dropout and Convolutional Neural Networks. Get to know device strategies so you can use deep learning algorithms and libraries in the real worldIn DetailWith an increasing interest in AI around the world, deep learning has attracted a great deal of public attention. Every day, deep learning algorithms are used broadly across different industries.The book will give you all the practical information available on the subject, including the best practices, using real-world use cases. You will learn to recognize and extract information to increase predictive accuracy and optimize results.Starting with a quick recap of important machine learning concepts, the book will delve straight into deep learning principles using Sci-kit learn. Moving ahead, you will learn to use the latest open source libraries such as Theano, Keras, Google's TensorFlow, and H20. Use this guide to uncover the difficulties of pattern recognition, scaling data with greater accuracy and discussing deep learning algorithms and techniques.Whether you want to dive deeper into Deep Learning, or want to investigate how to get more out of this powerful technology, you'll find everything inside.Style and approachPython Machine Learning by example follows practical hands on approach. It walks you through the key elements of Python and its powerful machine learning libraries with the help of real world projects.

Zustand: New. &Uumlber den AutorrnrnGianmario Spacagna is a senior data scientist at Pirelli, processing sensors and telemetry data for internet of things (IoT) and connected-vehicle applications. He works closely with tire mechanics, engineers, and business.

Paperback. Zustand: New. New. book.

Paperback. Zustand: New. 2nd Revised edition. Learn advanced state-of-the-art deep learning techniques and their applications using popular Python librariesKey FeaturesBuild a strong foundation in neural networks and deep learning with Python librariesExplore advanced deep learning techniques and their applications across computer vision and NLPLearn how a computer can navigate in complex environments with reinforcement learningBook DescriptionWith the surge in artificial intelligence in applications catering to both business and consumer needs, deep learning is more important than ever for meeting current and future market demands. With this book, you'll explore deep learning, and learn how to put machine learning to use in your projects.This second edition of Python Deep Learning will get you up to speed with deep learning, deep neural networks, and how to train them with high-performance algorithms and popular Python frameworks. You'll uncover different neural network architectures, such as convolutional networks, recurrent neural networks, long short-term memory (LSTM) networks, and capsule networks. You'll also learn how to solve problems in the fields of computer vision, natural language processing (NLP), and speech recognition. You'll study generative model approaches such as variational autoencoders and Generative Adversarial Networks (GANs) to generate images. As you delve into newly evolved areas of reinforcement learning, you'll gain an understanding of state-of-the-art algorithms that are the main components behind popular games Go, Atari, and Dota.By the end of the book, you will be well-versed with the theory of deep learning along with its real-world applications.What you will learnGrasp the mathematical theory behind neural networks and deep learning processesInvestigate and resolve computer vision challenges using convolutional networks and capsule networksSolve generative tasks using variational autoencoders and Generative Adversarial NetworksImplement complex NLP tasks using recurrent networks (LSTM and GRU) and attention modelsExplore reinforcement learning and understand how agents behave in a complex environmentGet up to date with applications of deep learning in autonomous vehiclesWho this book is forThis book is for data science practitioners, machine learning engineers, and those interested in deep learning who have a basic foundation in machine learning and some Python programming experience. A background in mathematics and conceptual understanding of calculus and statistics will help you gain maximum benefit from this book.

Digital. Zustand: New. Take your machine learning skills to the next level by mastering Deep Learning concepts and algorithms using Python.About This Book. Explore and create intelligent systems using cutting-edge deep learning techniques. Implement deep learning algorithms and work with revolutionary libraries in Python. Get real-world examples and easy-to-follow tutorials on Theano, TensorFlow, H2O and moreWho This Book Is ForThis book is for Data Science practitioners as well as aspirants who have a basic foundational understanding of Machine Learning concepts and some programming experience with Python. A mathematical background with a conceptual understanding of calculus and statistics is also desired.What You Will Learn. Get a practical deep dive into deep learning algorithms. Explore deep learning further with Theano, Caffe, Keras, and TensorFlow. Learn about two of the most powerful techniques at the core of many practical deep learning implementations: Auto-Encoders and Restricted Boltzmann Machines. Dive into Deep Belief Nets and Deep Neural Networks. Discover more deep learning algorithms with Dropout and Convolutional Neural Networks. Get to know device strategies so you can use deep learning algorithms and libraries in the real worldIn DetailWith an increasing interest in AI around the world, deep learning has attracted a great deal of public attention. Every day, deep learning algorithms are used broadly across different industries.The book will give you all the practical information available on the subject, including the best practices, using real-world use cases. You will learn to recognize and extract information to increase predictive accuracy and optimize results.Starting with a quick recap of important machine learning concepts, the book will delve straight into deep learning principles using Sci-kit learn. Moving ahead, you will learn to use the latest open source libraries such as Theano, Keras, Google's TensorFlow, and H20. Use this guide to uncover the difficulties of pattern recognition, scaling data with greater accuracy and discussing deep learning algorithms and techniques.Whether you want to dive deeper into Deep Learning, or want to investigate how to get more out of this powerful technology, you'll find everything inside.Style and approachPython Machine Learning by example follows practical hands on approach. It walks you through the key elements of Python and its powerful machine learning libraries with the help of real world projects.

paperback. Zustand: New. Paperback. Pub Date: 2021-03-01 Pages: 286 Language: Chinese Publisher: People's Posts and Telecommunications Publishing House This book introduces the practical application of deep learning algorithms (including best practices) with real cases. and aims to help readers understand how to identify and extract Information to improve forecast accuracy and optimize results.?The book consists of 10 chapters. which are machine learning-introduction. neural networks. basic principles of deep learnin.

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Zustand: New. PRINT ON DEMAND.

Zustand: New. Print on Demand pp. 406.