Book Image

Intelligent Projects Using Python

By : Santanu Pattanayak
Book Image

Intelligent Projects Using Python

By: Santanu Pattanayak

Overview of this book

This book will be a perfect companion if you want to build insightful projects from leading AI domains using Python. The book covers detailed implementation of projects from all the core disciplines of AI. We start by covering the basics of how to create smart systems using machine learning and deep learning techniques. You will assimilate various neural network architectures such as CNN, RNN, LSTM, to solve critical new world challenges. You will learn to train a model to detect diabetic retinopathy conditions in the human eye and create an intelligent system for performing a video-to-text translation. You will use the transfer learning technique in the healthcare domain and implement style transfer using GANs. Later you will learn to build AI-based recommendation systems, a mobile app for sentiment analysis and a powerful chatbot for carrying customer services. You will implement AI techniques in the cybersecurity domain to generate Captchas. Later you will train and build autonomous vehicles to self-drive using reinforcement learning. You will be using libraries from the Python ecosystem such as TensorFlow, Keras and more to bring the core aspects of machine learning, deep learning, and AI. By the end of this book, you will be skilled to build your own smart models for tackling any kind of AI problems without any hassle.
Table of Contents (12 chapters)

The discriminators of the DiscoGAN

The discriminators of the DiscoGAN would learn to distinguish the real images from the fake ones in a specific domain. We will have two discriminators: one for domain A, and one for domain B. The discriminators are also convolutional networks that can perform binary classification. Unlike the traditional classification-based convolutional networks, the discriminators don't have any fully connected layers. The input images are down-sampled using convolution with a stride of two until the final layer, where the output is 1 x 1. Again, we use leaky ReLU as the activation function and batch normalization for stable and fast convergence. The following code shows the discriminator build function implementation in TensorFlow:

def build_discriminator(self,image,reuse=False,name='discriminator'):
with tf.variable_scope(name):