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)

Results from the categorical classification

The categorical classification is performed by using all three of the neural network architectures: VGG16, ResNet50, and InceptionV3. The best results were obtained using the InceptionV3 version of the transfer learning network for this diabetic retinopathy use case. In case of categorical classification we are just converting the class with the maximum predicted class probability as the predicted severity label. However since the classes in the problem has an ordinal sense one of the ways in which we can utilize the softmax probabilities is to take the expectation of the class severity with respect to the softmax probabilities and come up with an expected score as follows:

We can rank order the scores and determine three thresholds to determine which class the image belongs to. These thresholds can be chosen by training a secondary...