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 diabetic retinopathy dataset

The dataset for the building the Diabetic Retinopathy detection application is obtained from Kaggle and can be downloaded from following the link: https://www.kaggle.com/c/ classroom-diabetic-retinopathy-detection-competition/data.

Both the training and the holdout test datasets are present within the train_dataset.zip file, which is available at the preceding link.

We will use the labeled training data to build the model through cross-validation. We will evaluate the model on the holdout dataset.

Since we are dealing with class prediction, accuracy will be a useful validation metric. Accuracy is defined as follows:

Here, c is the number of correctly classified samples, and N is the total number of evaluated samples.

We will also use the quadratic weighted kappa statistics to determine the quality of the model, and to have a benchmark as to how...