Applied Neural Networks with TensorFlow 2

1. Front Matter

   Pages i-xix

   PDF

2. Introduction

   • Orhan Gazi Yalçın

   Pages 1-32

3. Introduction to Machine Learning

   • Orhan Gazi Yalçın

   Pages 33-55

4. Deep Learning and Neural Networks Overview

   • Orhan Gazi Yalçın

   Pages 57-80

5. Complementary Libraries to TensorFlow 2.x

   • Orhan Gazi Yalçın

   Pages 81-94

6. A Guide to TensorFlow 2.0 and Deep Learning Pipeline

   • Orhan Gazi Yalçın

   Pages 95-120

7. Feedforward Neural Networks

   • Orhan Gazi Yalçın

   Pages 121-143

8. Convolutional Neural Networks

   • Orhan Gazi Yalçın

   Pages 145-160

9. Recurrent Neural Networks

   • Orhan Gazi Yalçın

   Pages 161-185

10. Natural Language Processing

    • Orhan Gazi Yalçın

    Pages 187-213

11. Recommender Systems

    • Orhan Gazi Yalçın

    Pages 215-236

12. Autoencoders

    • Orhan Gazi Yalçın

    Pages 237-257

13. Generative Adversarial Network

    • Orhan Gazi Yalçın

    Pages 259-284

14. Back Matter

    Pages 285-295

    PDF

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.

• Deep Learning
• TensorFlow
• API
• Machine Learning
• DL
• ML
• Artificial Intelligence
• AI
• Data Science
• programming
• Python
• Data Analytics

• Istanbul, Turkey

  Orhan Gazi Yalçın

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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