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)

CNNs and LSTMs in video captioning

A video minus the audio can be thought of as a collection of images arranged in a sequential manner. The important features from those images can be extracted using a convolutional neural network trained on specific image classification problems, such as ImageNet. The activations of the last fully connected layer of a pre-trained network can be used to derive features from the sequentially sampled images from the video. The frequency rate at which to sample the images sequentially from the video depends on the type of content in the video and can be optimized through training.

Illustrated in the following diagram (Figure 5.1) is a pre-trained neural network used for extracting features from a video:

Figure 5.1: Video image feature extraction using pre-trained neural networks

As we can see from the preceding diagram, the sequentially sampled...