Applied Neural Networks with Tensorflow 2

Chapter 1: Introduction

• How to Make the Most out of this Book
  
• What is Tensorflow?
  
• What's New in Tensorflow 2.0
  
• Google Colab and Jupyter Notebook
  
• Installation and Environment Setup
  

Chapter 3: Deep Learning● Introduction to Deep Learning
● Introduction to Perceptron
● Activation Functions
● Cost (Loss) Function
● Gradient Descent Backpropagation
● Normalization and Standardization
● Loss Function and Optimization Functions
● Optimizer

Chapter 4: Relevant Technologies Used for Machine Learning● Numpy
● Matplotlib
● Pandas
● Scikit Learn
● Deployment with Flask

Chapter 5: TensorFlow 2.0● Tensorflow vs. Other Deep Learning Libraries
● Keras API vs. Estimator
● Keras API Syntax
● Hardware Options and Performance Evaluation: CPUs vs. GPUs vs. TPUs

Chapter 6: Artificial Neural Networks (ANNs)● Introduction to ANNs
● Perceptron Model
● Linear (Shallow) Neural Networks
● Deep Neural Networks
● ANN Application Example with TF 2.0 Keras API

Chapter 7: Convolutional Neural Networks (CNNs)● Introduction to CNN Architecture
● CNN Basics: Strides and Filtering
● Dealing with Image Data
● Batch Normalization
● Data Augmentation
● CNN for Fashion MNIST with TF 2.0 Keras API
● CNN for CIFAR10 with TF 2.0 Keras API (Pre-Trained Model)
● CNN with Imagenet with TF 2.0 Keras API (Pre-Trained Model)

Chapter 8: Recurrent Neural Networks (RNNs)● Introduction to RNN Architectures
● Sequence Data (incl. Time Series)
● Data Preparation
● Simple RNN Architecture● Gated Recurrent Unit (GRU) Architecture● Long-Short Term Memory (LSTM) Architecture● Simple RNN, GRU, and LSTM Comparison

Chapter 9: Natural Language Processing (RNN and CNN applications)● Introduction to Natural Language Processing
● Text Processing
● NLP Application with RNN
● NLP Application with CNN
● Text Generation

Chapter 10: Recommender Systems● Introduction to Recommender Systems
● Recommender System Using MovieLens Dataset
● Recommender S
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.