Applied Neural Networks with TensorFlow 2: API Oriented Deep Learning with Python
Yal????n, Orhan Gazi
ISBN 10: 1484265122 ISBN 13: 9781484265123
Published by Apress, 2020
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About this Item
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Synopsis:
Implement deep learning applications using TensorFlow while learning the “why” through in-depth conceptual explanations.
You’ll start by learning what deep learning offers over other machine learning models. Then familiarize yourself with several technologies used to create deep learning models. While some of these technologies are complementary, such as Pandas, Scikit-Learn, and Numpy―others are competitors, such as PyTorch, Caffe, and Theano. This book clarifies the positions of deep learning and Tensorflow among their peers.
You'll then work on supervised deep learning models to gain applied experience with the technology. A single-layer of multiple perceptrons will be used to build a shallow neural network before turning it into a deep neural network. After showing the structure of the ANNs, a real-life application will be created with Tensorflow 2.0 Keras API. Next, you’ll work on data augmentation and batch normalization methods. Then, the Fashion MNIST dataset will be used to train a CNN. CIFAR10 and Imagenet pre-trained models will be loaded to create already advanced CNNs.
Finally, move into theoretical applications and unsupervised learning with auto-encoders and reinforcement learning with tf-agent models. With this book, you’ll delve into applied deep learning practical functions and build a wealth of knowledge about how to use TensorFlow effectively.
What You'll Learn
- Compare competing technologies and see why TensorFlow is more popular
- Generate text, image, or sound with GANs
- Predict the rating or preference a user will give to an item
- Sequence data with recurrent neural networks
Who This Book Is For
About the Author:
Orhan Gazi Yalçın is a joint Ph.D. candidate at the University of Bologna & the Polytechnic University of Madrid. After completing his double major in business and law, he began his career in Istanbul, working for a city law firm, Allen & Overy, and a global entrepreneurship network, Endeavor. During his academic and professional career, he taught himself programming and excelled in machine learning. He currently conducts research on hotly debated law & AI topics such as explainable artificial intelligence and the right to explanation by combining his technical and legal skills. In his spare time, he enjoys free-diving, swimming, exercising as well as discovering new countries, cultures, and cuisines.
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Bibliographic Details
Title: Applied Neural Networks with TensorFlow 2: ...
Publisher: Apress
Publication Date: 2020
Binding: Paperback
Condition: New
Edition: 1st ed.
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Applied Neural Networks with TensorFlow 2 (Paperback)
Published by
APress, Berkley, 2020
ISBN 10: 1484265122
ISBN 13: 9781484265123
New
Paperback
First Edition
Seller: Grand Eagle Retail, Bensenville, IL, U.S.A.
Seller rating 5 out of 5 stars
Paperback. Condition: new. Paperback. Implement deep learning applications using TensorFlow while learning the why through in-depth conceptual explanations. Youll start by learning what deep learning offers over other machine learning models. Then familiarize yourself with several technologies used to create deep learning models. While some of these technologies are complementary, such as Pandas, Scikit-Learn, and Numpyothers are competitors, such as PyTorch, Caffe, and Theano. This book clarifies the positions of deep learning and Tensorflow among their peers. You'll then work on supervised deep learning models to gain applied experience with the technology. A single-layer of multiple perceptrons will be used to build a shallow neural network before turning it into a deep neural network. After showing the structure of the ANNs, a real-life application will be created with Tensorflow 2.0 Keras API. Next, youll work on data augmentation and batch normalization methods. Then, the Fashion MNIST dataset will be used to train a CNN. CIFAR10 and Imagenet pre-trained models will be loaded to create already advanced CNNs. Finally, move into theoretical applications and unsupervised learning with auto-encoders and reinforcement learning with tf-agent models. With this book, youll delve into applied deep learning practical functions and build a wealth of knowledge about how to use TensorFlow effectively. What You'll Learn Compare competing technologies and see why TensorFlow is more popularGenerate text, image, or sound with GANsPredict the rating or preference a user will give to an itemSequence data with recurrent neural networks Who This Book Is For Data scientists and programmers new to the fields of deep learning and machine learning APIs. Shipping may be from multiple locations in the US or from the UK, depending on stock availability. Seller Inventory # 9781484265123
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Applied Neural Networks with TensorFlow 2 (Paperback)
Published by
APress, Berkley, 2020
ISBN 10: 1484265122
ISBN 13: 9781484265123
New
Paperback
First Edition
Seller: AussieBookSeller, Truganina, VIC, Australia
Seller rating 5 out of 5 stars
Paperback. Condition: new. Paperback. Implement deep learning applications using TensorFlow while learning the why through in-depth conceptual explanations. Youll start by learning what deep learning offers over other machine learning models. Then familiarize yourself with several technologies used to create deep learning models. While some of these technologies are complementary, such as Pandas, Scikit-Learn, and Numpyothers are competitors, such as PyTorch, Caffe, and Theano. This book clarifies the positions of deep learning and Tensorflow among their peers. You'll then work on supervised deep learning models to gain applied experience with the technology. A single-layer of multiple perceptrons will be used to build a shallow neural network before turning it into a deep neural network. After showing the structure of the ANNs, a real-life application will be created with Tensorflow 2.0 Keras API. Next, youll work on data augmentation and batch normalization methods. Then, the Fashion MNIST dataset will be used to train a CNN. CIFAR10 and Imagenet pre-trained models will be loaded to create already advanced CNNs. Finally, move into theoretical applications and unsupervised learning with auto-encoders and reinforcement learning with tf-agent models. With this book, youll delve into applied deep learning practical functions and build a wealth of knowledge about how to use TensorFlow effectively. What You'll Learn Compare competing technologies and see why TensorFlow is more popularGenerate text, image, or sound with GANsPredict the rating or preference a user will give to an itemSequence data with recurrent neural networks Who This Book Is For Data scientists and programmers new to the fields of deep learning and machine learning APIs. Shipping may be from our Sydney, NSW warehouse or from our UK or US warehouse, depending on stock availability. Seller Inventory # 9781484265123