Isbn: 9781789344158 - hands-on deep learning algorithms with python: master deep learning algorithms with extensive math by implementing them using tensorflow (23 resultados)

Condición: good. Book is in good condition. Minimal signs of wear. It May have markings or highlights, but kept to only a few pages. May not come with supplemental materials if applicable.

Condición: New.

paperback. Condición: New. Ships in a BOX from Central Missouri! UPS shipping for most packages, (Priority Mail for AK/HI/APO/PO Boxes).

Condición: As New. Unread book in perfect condition.

Condición: New.

Condición: New. Hands-On Deep Learning Algorithms with Python (Paperback or Softback).

Paperback. Condición: New. Understand basic-to-advanced deep learning algorithms, the mathematical principles behind them, and their practical applicationsKey FeaturesGet up to speed with building your own neural networks from scratchGain insights into the mathematical principles behind deep learning algorithmsImplement popular deep learning algorithms such as CNNs, RNNs, and more using TensorFlowBook DescriptionDeep learning is one of the most popular domains in the AI space that allows you to develop multi-layered models of varying complexities. This book introduces you to popular deep learning algorithms-from basic to advanced-and shows you how to implement them from scratch using TensorFlow. Throughout the book, you will gain insights into each algorithm, the mathematical principles involved, and how to implement it in the best possible manner. The book starts by explaining how you can build your own neural networks, followed by introducing you to TensorFlow, the powerful Python-based library for machine learning and deep learning. Moving on, you will get up to speed with gradient descent variants, such as NAG, AMSGrad, AdaDelta, Adam, and Nadam. The book will then provide you with insights into recurrent neural networks (RNNs) and LSTM and how to generate song lyrics with RNN. Next, you will master the math necessary to work with convolutional and capsule networks, widely used for image recognition tasks. You will also learn how machines understand the semantics of words and documents using CBOW, skip-gram, and PV-DM. Finally, you will explore GANs, including InfoGAN and LSGAN, and autoencoders, such as contractive autoencoders and VAE. By the end of this book, you will be equipped with all the skills you need to implement deep learning in your own projects.What you will learnImplement basic-to-advanced deep learning algorithmsMaster the mathematics behind deep learning algorithmsBecome familiar with gradient descent and its variants, such as AMSGrad, AdaDelta, Adam, and NadamImplement recurrent networks, such as RNN, LSTM, GRU, and seq2seq modelsUnderstand how machines interpret images using CNN and capsule networksImplement different types of generative adversarial network, such as CGAN, CycleGAN, and StackGANExplore various types of autoencoder, such as Sparse autoencoders, DAE, CAE, and VAEWho this book is forIf you are a machine learning engineer, data scientist, AI developer, or anyone looking to delve into neural networks and deep learning, this book is for you. Those who are completely new to deep learning, but have some experience in machine learning and Python programming will also find the book very helpful.…

Paperback. Condición: New.

Condición: New.

Condición: New. In English.

Condición: New. pp. 512.

Condición: New. 2019. paperback. . . . . .

Condición: As New. Unread book in perfect condition.

Condición: New. 2019. paperback. . . . . . Books ship from the US and Ireland.

Paperback. Condición: New. Understand basic-to-advanced deep learning algorithms, the mathematical principles behind them, and their practical applicationsKey FeaturesGet up to speed with building your own neural networks from scratchGain insights into the mathematical principles behind deep learning algorithmsImplement popular deep learning algorithms such as CNNs, RNNs, and more using TensorFlowBook DescriptionDeep learning is one of the most popular domains in the AI space that allows you to develop multi-layered models of varying complexities. This book introduces you to popular deep learning algorithms-from basic to advanced-and shows you how to implement them from scratch using TensorFlow. Throughout the book, you will gain insights into each algorithm, the mathematical principles involved, and how to implement it in the best possible manner. The book starts by explaining how you can build your own neural networks, followed by introducing you to TensorFlow, the powerful Python-based library for machine learning and deep learning. Moving on, you will get up to speed with gradient descent variants, such as NAG, AMSGrad, AdaDelta, Adam, and Nadam. The book will then provide you with insights into recurrent neural networks (RNNs) and LSTM and how to generate song lyrics with RNN. Next, you will master the math necessary to work with convolutional and capsule networks, widely used for image recognition tasks. You will also learn how machines understand the semantics of words and documents using CBOW, skip-gram, and PV-DM. Finally, you will explore GANs, including InfoGAN and LSGAN, and autoencoders, such as contractive autoencoders and VAE. By the end of this book, you will be equipped with all the skills you need to implement deep learning in your own projects.What you will learnImplement basic-to-advanced deep learning algorithmsMaster the mathematics behind deep learning algorithmsBecome familiar with gradient descent and its variants, such as AMSGrad, AdaDelta, Adam, and NadamImplement recurrent networks, such as RNN, LSTM, GRU, and seq2seq modelsUnderstand how machines interpret images using CNN and capsule networksImplement different types of generative adversarial network, such as CGAN, CycleGAN, and StackGANExplore various types of autoencoder, such as Sparse autoencoders, DAE, CAE, and VAEWho this book is forIf you are a machine learning engineer, data scientist, AI developer, or anyone looking to delve into neural networks and deep learning, this book is for you. Those who are completely new to deep learning, but have some experience in machine learning and Python programming will also find the book very helpful.…

PAP. Condición: New. New Book. Shipped from UK. THIS BOOK IS PRINTED ON DEMAND. Established seller since 2000.

PAP. Condición: New. New Book. Delivered from our UK warehouse in 4 to 14 business days. THIS BOOK IS PRINTED ON DEMAND. Established seller since 2000.

Condición: New. Print on Demand pp. 512.

Condición: New. PRINT ON DEMAND pp. 512.

Paperback / softback. Condición: New. This item is printed on demand. New copy - Usually dispatched within 5-9 working days.

Taschenbuch. Condición: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering.

Condición: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. This book introduces basic-to-advanced deep learning algorithms used in a production environment by AI researchers and principal data scientists it explains algorithms intuitively, including the underlying math, and shows how to implement them using popula. …

Taschenbuch. Condición: Neu. Hands-On Deep Learning Algorithms with Python | Sudharsan Ravichandiran | Taschenbuch | Kartoniert / Broschiert | Englisch | 2019 | Packt Publishing | EAN 9781789344158 | Verantwortliche Person für die EU: Libri GmbH, Europaallee 1, 36244 Bad Hersfeld, gpsr[at]libri[dot]de | Anbieter: preigu Print on Demand. …