Applied Neural Networks with TensorFlow 2: API Oriented Deep Learning with Python - Brossura

Yalçın, Orhan Gazi

Brossura

ISBN 10: 1484265122 ISBN 13: 9781484265123

Casa editrice: Apress, 2020

Riassunto

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

Data scientists and programmers new to the fields of deep learning and machine learning APIs.

Su questo libro

Informazioni sull?autore

Orhan Gazi Yalçin 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.

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.

You will:

• 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

Dettagli dell'edizione

• Editore: Apress
• Data di pubblicazione: 2020
• Lingua: Inglese
• ISBN 10: 1484265122
• ISBN 13: 9781484265123
• Rilegatura: Copertina flessibile
• Numero edizione: 1
• Numero di pagine: 316
• Contatto del produttore: Springer Nature Customer Service Center GmbH; ProductSafety@springernature.com; ; Europaplatz 3,69115 Heidelberg, Germany; Heidelberg; 69115; Germania