Deep Learning Essentials
Di, Wei
Vendido por Chiron Media, Wallingford, Reino Unido
Vendedor de AbeBooks desde 2 de agosto de 2010
Nuevos - Encuadernación de tapa blanda
Condición: Nuevo
Cantidad disponible: 10 disponibles
Añadir al carritoVendido por Chiron Media, Wallingford, Reino Unido
Vendedor de AbeBooks desde 2 de agosto de 2010
Condición: Nuevo
Cantidad disponible: 10 disponibles
Añadir al carritoN° de ref. del artículo 6666-IUK-9781785880360
Get to grips with the essentials of deep learning by leveraging the power of Python
Deep Learning a trending topic in the field of Artificial Intelligence today and can be considered to be an advanced form of machine learning, which is quite tricky to master.
This book will help you take your first steps in training efficient deep learning models and applying them in various practical scenarios. You will model, train, and deploy different kinds of neural networks such as Convolutional Neural Network and Recurrent Neural Network, and will see some of their applications in real-world domains including computer vision, natural language processing, and speech recognition. You will build practical projects such as chatbots, implement reinforcement learning to build smart games, and develop expert systems for image captioning and processing. Popular Python library such as TensorFlow is used in this book to build the models. This book also covers solutions for different problems you might come across while training models, such as noisy datasets and small datasets
By the end of this book, you will have a firm understanding of the basics of deep learning and neural network modeling, along with their practical applications.
Aspiring data scientists and machine learning experts who have limited or no exposure to deep learning will find this book to be very useful. If you are looking for a resource that gets you up and running with the fundamentals of deep learning and neural networks, this book is for you. As the models in the book are trained using the popular Python-based libraries such as TensorFlow and Keras, it would be useful to have sound programming knowledge of Python. Prior knowledge of deep learning is not required.
Get to grips with the essentials of deep learning by leveraging the power of Python
Key Features
Book Description
Deep Learning a trending topic in the field of Artificial Intelligence today and can be considered to be an advanced form of machine learning, which is quite tricky to master.
This book will help you take your first steps in training efficient deep learning models and applying them in various practical scenarios. You will model, train, and deploy different kinds of neural networks such as Convolutional Neural Network and Recurrent Neural Network, and will see some of their applications in real-world domains including computer vision, natural language processing, and speech recognition. You will build practical projects such as chatbots, implement reinforcement learning to build smart games, and develop expert systems for image captioning and processing. Popular Python library such as TensorFlow is used in this book to build the models. This book also covers solutions for different problems you might come across while training models, such as noisy datasets and small datasets
By the end of this book, you will have a firm understanding of the basics of deep learning and neural network modeling, along with their practical applications.
What you will learn
Who This Book Is For
Aspiring data scientists and machine learning experts who have limited or no exposure to deep learning will find this book to be very useful. If you are looking for a resource that gets you up and running with the fundamentals of deep learning and neural networks, this book is for you. As the models in the book are trained using the popular Python-based libraries such as TensorFlow and Keras, it would be useful to have sound programming knowledge of Python. Prior knowledge of deep learning is not required.
Table of Contents
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Cantidad del pedido | De 14 a 21 días hábiles | De 14 a 21 días hábiles |
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