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)

Important parameter values for GAN training

In this section, we will discuss the different parameter values that are used for training the DiscoGAN. These are presented in the following table:

Parameter name Variable name and Value set Rationale
Learning rate for Adam optimizer

self.l_r = 2e-4

We should always train a GAN network with a low learning rate for better stability and a DiscoGAN is no different.
Decay rates for Adam optimizer

self.beta1 = 0.5

self.beta2 = 0.99

The parameter beta1 defines the decaying average of gradients, while the parameter beta2 defines the decaying average of the square of the gradients.
Epochs

self.epoch = 200

200 epochs is good enough for the convergence of the DiscoGAN network in this implementation.
Batch size

self.batch_size = 64

A batch size of 64 works well for this implementation. However, because of resource constraint...