Now that we have some idea of how to build a chatbot using a recurrent neural network, we will build a chatbot using the customer service responses of 20 big brands to tweets posted by customers. The dataset twcs.zip can be located at https://www.kaggle.com/thoughtvector/customer-support-on-twitter. Each tweet is identified by the tweet_id and the tweet content is in the text field. The tweets posted by the customers can be identified by the in_response_to_tweet_id field. This should contain null values for customer tweets. For customer service tweets, this in_response_to_tweet_id field should point to the customer tweet_id to which this tweet is directed.

Intelligent Projects Using Python
By :

Intelligent Projects Using Python
By:
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)
Preface
Foundations of Artificial Intelligence Based Systems
Transfer Learning
Neural Machine Translation
Style Transfer in Fashion Industry using GANs
Video Captioning Application
The Intelligent Recommender System
Mobile App for Movie Review Sentiment Analysis
Conversational AI Chatbots for Customer Service
Autonomous Self-Driving Car Through Reinforcement Learning
CAPTCHA from a Deep-Learning Perspective
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