The Deep Learning Workshop

The Deep Learning Workshop
Author: Mirza Rahim Baig
Publisher: Packt Publishing Ltd
Total Pages: 473
Release: 2020-07-31
Genre: Computers
ISBN: 1839210567


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Take a hands-on approach to understanding deep learning and build smart applications that can recognize images and interpret text Key Features Understand how to implement deep learning with TensorFlow and Keras Learn the fundamentals of computer vision and image recognition Study the architecture of different neural networks Book Description Are you fascinated by how deep learning powers intelligent applications such as self-driving cars, virtual assistants, facial recognition devices, and chatbots to process data and solve complex problems? Whether you are familiar with machine learning or are new to this domain, The Deep Learning Workshop will make it easy for you to understand deep learning with the help of interesting examples and exercises throughout. The book starts by highlighting the relationship between deep learning, machine learning, and artificial intelligence and helps you get comfortable with the TensorFlow 2.0 programming structure using hands-on exercises. You'll understand neural networks, the structure of a perceptron, and how to use TensorFlow to create and train models. The book will then let you explore the fundamentals of computer vision by performing image recognition exercises with convolutional neural networks (CNNs) using Keras. As you advance, you'll be able to make your model more powerful by implementing text embedding and sequencing the data using popular deep learning solutions. Finally, you'll get to grips with bidirectional recurrent neural networks (RNNs) and build generative adversarial networks (GANs) for image synthesis. By the end of this deep learning book, you'll have learned the skills essential for building deep learning models with TensorFlow and Keras. What you will learn Understand how deep learning, machine learning, and artificial intelligence are different Develop multilayer deep neural networks with TensorFlow Implement deep neural networks for multiclass classification using Keras Train CNN models for image recognition Handle sequence data and use it in conjunction with RNNs Build a GAN to generate high-quality synthesized images Who this book is for If you are interested in machine learning and want to create and train deep learning models using TensorFlow and Keras, this workshop is for you. A solid understanding of Python and its packages, along with basic machine learning concepts, will help you to learn the topics quickly.


The Deep Learning Workshop
Language: en
Pages: 473
Authors: Mirza Rahim Baig
Categories: Computers
Type: BOOK - Published: 2020-07-31 - Publisher: Packt Publishing Ltd

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Take a hands-on approach to understanding deep learning and build smart applications that can recognize images and interpret text Key Features Understand how to
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Pages: 285
Authors: Hyatt Saleh
Categories: Computers
Type: BOOK - Published: 2020-07-22 - Publisher: Packt Publishing Ltd

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Take a comprehensive and step-by-step approach to understanding machine learning Key FeaturesDiscover how to apply the scikit-learn uniform API in all types of
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Language: en
Pages: 624
Authors: Jeremy Howard
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Type: BOOK - Published: 2020-06-29 - Publisher: O'Reilly Media

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Deep learning is often viewed as the exclusive domain of math PhDs and big tech companies. But as this hands-on guide demonstrates, programmers comfortable with
The Deep Learning with Keras Workshop
Language: en
Pages: 495
Authors: Matthew Moocarme
Categories: Computers
Type: BOOK - Published: 2020-07-29 - Publisher: Packt Publishing Ltd

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Discover how to leverage Keras, the powerful and easy-to-use open source Python library for developing and evaluating deep learning models Key FeaturesGet to gr
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Language: en
Pages: 329
Authors: Hyatt Saleh
Categories: Computers
Type: BOOK - Published: 2020-07-22 - Publisher: Packt Publishing Ltd

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Get a head start in the world of AI and deep learning by developing your skills with PyTorch Key FeaturesLearn how to define your own network architecture in de