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)

Creating a word vocabulary for the captions

In this section, we create the word vocabulary for the video captions. We create some additional words that are required as follows:

eos => End of Sentence
bos => Beginning of Sentence
pad => When there is no word to feed,required by the LSTM 2 in the initial N time steps
unk => A substitute for a word that is not included in the vocabulary

The LSTM 2, in which a word is an input, would require these four additional symbols. For the (N+1) time step, when we start generating the captions, we feed the word of the previous time step wt-1. For the first word to be generated, there is no valid previous time step word, and so we feed the dummy word <bos>, which signifies the start of sentence. Similarly, when we reach the last time step, wt-1 is the last word of the caption. We train the model to output the final word as &lt...