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 environment for the self-driving car

The environment for the self-driving car is CarRacing-v0 from the OpenAI Gym. The states presented to the agent from this OpenAI environment are images from the front of the simulated car in CarRacing-v0. The environment also returns a reward based on the action taken by the agent at a given state. We penalize the reward if the car treads on grass and also normalize the reward to be in the range of (-1,1) for stable training. The detailed code for the environment is as below

import gym
from gym import envs
import numpy as np
from helper_functions import rgb2gray,action_list,sel_action,sel_action_index
from keras import backend as K

seed_gym = 3
action_repeat_num = 8
patience_count = 200
epsilon_greedy = True
max_reward = 10
grass_penalty = 0.8
max_num_steps = 200
max_num_episodes = action_repeat_num*100

Enviroment to interact with...