What is Chatbot?
A deep learning chatbot learns right from scratch through a process called “Deep Learning.” In this process, the chatbot is created using machine learning algorithms. A deep learning chatbot learns everything from its data and human-to-human dialogue.
Building a Deep Learning Chatbot
Below the some steps which is used to Build a deep learning chatbot:
The first step of any machine learning related process is that of preparing data.
Depending on your data source, you may or may not need this step. If your data isn’t segregated well, you will need to reshape your data into single rows of observations.
The next step in building a deep learning chatbot is that of pre-processing. In this step, you need to add grammar into the machine learning so that your chatbot can understand spelling errors correctly.
Select the Type of Chatbot
The two major types of chatbots that you can make are:
Generative – In the generative model, the chatbot doesn’t use any sort of predefined repository. This is an advanced form of chatbot that uses deep learning to respond to queries.
Retrieval-Based – In this form, the chatbot has a repository of responses that it uses to solve the queries. You need to choose an appropriate response based on the questions, and the chatbot will comply.
Generate Word Vectors
Word vectors are needed when you have frequent usage of words such as LOL, LMAO, etc. They are common words that are used on social media but aren’t part of many datasets.
Create a Seq2Seq Model
To create the Seq2Seq model, you can use TensorFlow. For this, you’ll need to use a Python script that looks like the one here.
Add it to an Application
Deploy Your TensorFlow Model
Test Your Deep Learning Chatbot