deep learning python von chollet françois (33 Ergebnisse)

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Soft cover. Zustand: New. Contents: Part I: Fundamentals of Deep Learning. 1. What is deep learning. 2. Before we begin: the mathematical building blocks of neural networks. 3. Getting started with neural networks. 4. Fundamentals of machine learning. Part II: Deep Learning in Practice. 5. Deep learning for computer vision. 6. Deep learning for text and sequences. 7. Advanced deep-learning best practices. 8. Generative deep learning. 9. Conclusions.

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Paperback. Zustand: Brand New. 361 pages. 9.25x7.25x0.75 inches. In Stock.

Zustand: New. Deep learning is applicable to a widening range of artificialintelligence problems, such as image classification, speech recognition,text classification, question answering, text-to-speech, and opticalcharacter recognition.Deep Lea.

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Zustand: New. &Uumlber den AutorFran&ccedilois Chollet is a software engineer at Google and creator of Keras.KlappentextDeep Learning with Python introduces the field of deep learning using the Python.

Taschenbuch. Zustand: Neu. Neuware - Deep learning is applicable to a widening range of artificialintelligence problems, such as image classification, speech recognition,text classification, question answering, text-to-speech, and opticalcharacter recognition.Deep Learning with Python is structured around a series of practicalcode examples that illustrate each new concept introduced anddemonstrate best practices. By the time you reach the end of this book,you will have become a Keras expert and will be able to apply deeplearning in your own projects.KEY FEATURES Practical code examples In-depth introduction to Keras Teaches the difference between Deep Learning and AIABOUT THE TECHNOLOGYDeep learning is the technology behind photo tagging systems atFacebook and Google, self-driving cars, speech recognition systems onyour smartphone, and much more.AUTHOR BIOFrancois Chollet is the author of Keras, one of the most widely usedlibraries for deep learning in Python. He has been working with deep neuralnetworks since 2012. Francois is currently doing deep learning research atGoogle. He blogs about deep learning at blog.keras.io.

Zustand: New. Idioma/Language: Español. El aprendizaje automático ha progresado de manera notable en los últimos años. Hemos pasado del discurso casi inutilizable y el reconocimiento de imágenes a una precisión casi humana. Hemos pasado de máquinas que no podían ganar a un jugador de go decente a derrotar al campeón del mundo. Tras este progreso se encuentra el deep learning, una combinación de avances en ingeniería, prácticas adecuadas y teoría que permite crear una gran abundancia de aplicaciones inteligentes que antes eran imposibles. Deep Learning con Python presenta el campo del deep learning utilizando el lenguaje Python y la potente biblioteca Keras. Escrito por François Chollet, creador de Keras e investigador de Google AI, este libro desarrolla su comprensión mediante explicaciones intuitivas y ejemplos prácticos. Explorará conceptos complicados y practicará con aplicaciones en visión por ordenador, procesamiento de lenguaje natural y modelos generativos. Para cuando acabe, tendrá el conocimiento y las habilidades prácticas para aplicar el deep learning a sus propios proyectos. *** Nota: Los envíos a España peninsular, Baleares y Canarias se realizan a través de mensajería urgente. No aceptamos pedidos con destino a Ceuta y Melilla.

Paperback. Zustand: Brand New. 3rd edition. 600 pages. 9.25x7.37x9.25 inches. In Stock.

Taschenbuch. Zustand: Neu. Neuware - Printed in full color! Unlock the groundbreaking advances of deep learning with this extensively revised new edition of the bestselling original. Learn directly from the creator of Keras and master practical Python deep learning techniques that are easy to apply in the real world.In Deep Learning with Python, Second Edition you will learn: Deep learning from first principles Image classification and image segmentation Timeseries forecasting Text classification and machine translation Text generation, neural style transfer, and image generation Full color printing throughout Deep Learning with Python has taught thousands of readers how to put the full capabilities of deep learning into action. This extensively revised full color second edition introduces deep learning using Python and Keras, and is loaded with insights for both novice and experienced ML practitioners. You'll learn practical techniques that are easy to apply in the real world, and important theory for perfecting neural networks. Purchase of the print book includes a free Elektronisches Buch in PDF, Kindle, and ePub formats from Manning Publications. About the technology Recent innovations in deep learning unlock exciting new software capabilities like automated language translation, image recognition, and more. Deep learning is quickly becoming essential knowledge for every software developer, and modern tools like Keras and TensorFlow put it within your reacheven if you have no background in mathematics or data science. This book shows you how to get started. About the book Deep Learning with Python, Second Edition introduces the field of deep learning using Python and the powerful Keras library. In this revised and expanded new edition, Keras creator François Chollet offers insights for both novice and experienced machine learning practitioners. As you move through this book, you'll build your understanding through intuitive explanations, crisp color illustrations, and clear examples. You'll quickly pick up the skills you need to start developing deep-learning applications. What's inside Deep learning from first principles Image classification and image segmentation Time series forecasting Text classification and machine translation Text generation, neural style transfer, and image generation Full color printing throughout About the reader For readers with intermediate Python skills. No previous experience with Keras, TensorFlow, or machine learning is required. About the author François Chollet is a software engineer at Google and creator of the Keras deep-learning library. Table of Contents 1 What is deep learning 2 The mathematical building blocks of neural networks 3 Introduction to Keras and TensorFlow 4 Getting started with neural networks: Classification and regression 5 Fundamentals of machine learning 6 The universal workflow of machine learning 7 Working with Keras: A deep dive 8 Introduction to deep learning for computer vision 9 Advanced deep learning for computer vision 10 Deep learning for timeseries 11 Deep learning for text 12 Generative deep learning 13 Best practices for the real world 14 Conclusions.

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Taschenbuch. Zustand: Neu. Deep Learning with Python, Second Edition | Francois Chollet | Taschenbuch | Kartoniert / Broschiert | Englisch | 2021 | Manning Publications | EAN 9781617296864 | Verantwortliche Person für die EU: Libri GmbH, Europaallee 1, 36244 Bad Hersfeld, gpsr[at]libri[dot]de | Anbieter: preigu.

Taschenbuch. Zustand: Neu. Neuware - The bestselling book on Python deep learning, now covering generative AI, Keras 3, PyTorch, and JAX!Deep Learning with Python, Third Edition puts the power of deep learning in your hands. This new edition includes the latest Keras and TensorFlow features, generative AI models, and added coverage of PyTorch and JAX. Learn directly from the creator of Keras and step confidently into the world of deep learning with Python. In Deep Learning with Python, Third Edition you'll discover: Deep learning from first principles The latest features of Keras 3 A primer on JAX, PyTorch, and TensorFlow Image classification and image segmentation Time series forecasting Large Language models Text classification and machine translation Text and image generationbuild your own GPT and diffusion models! Scaling and tuning models With over 100,000 copies sold, Deep Learning with Python makes it possible for developers, data scientists, and machine learning enthusiasts to put deep learning into action. In this expanded and updated third edition, Keras creator François Chollet offers insights for both novice and experienced machine learning practitioners. You'll master state-of-the-art deep learning tools and techniques, from the latest features of Keras 3 to building AI models that can generate text and images. About the technology In less than a decade, deep learning has changed the worldtwice. First, Python-based libraries like Keras, TensorFlow, and PyTorch elevated neural networks from lab experiments to high-performance production systems deployed at scale. And now, through Large Language Models and other generative AI tools, deep learning is again transforming business and society. In this new edition, Keras creator François Chollet invites you into this amazing subject in the fluid, mentoring style of a true insider. About the book Deep Learning with Python, Third Edition makes the concepts behind deep learning and generative AI understandable and approachable. This complete rewrite of the bestselling original includes fresh chapters on transformers, building your own GPT-like LLM, and generating images with diffusion models. Each chapter introduces practical projects and code examples that build your understanding of deep learning, layer by layer. What's inside Hands-on, code-first learning Comprehensive, from basics to generative AI Intuitive and easy math explanations Examples in Keras, PyTorch, JAX, and TensorFlow About the reader For readers with intermediate Python skills. No previous experience with machine learning or linear algebra required. About the author François Chollet is the co-founder of Ndea and the creator of Keras. Matthew Watson is a software engineer at Google working on Gemini and a core maintainer of Keras. Table of Contents 1 What is deep learning 2 The mathematical building blocks of neural networks 3 Introduction to TensorFlow, PyTorch, JAX, and Keras 4 Classification and regression 5 Fundamentals of machine learning 6 The universal workflow of machine learning 7 A deep dive on Keras 8 Image classification 9 ConvNet architecture patterns 10 Interpreting what ConvNets learn 11 Image segmentation 12 Object detection 13 Timeseries forecasting 14 Text classification 15 Language models and the Transformer 16 Text generation 17 Image generation 18 Best practices for the real world 19 The future of AI 20 Conclusions Get a free Elektronisches Buch (PDF or ePub) from Manning as well as access to the online liveBook format (and its AI assistant that will answer your questions in any language) when you purchase the print book.

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