How to Create a Deep Learning Chatbot in Python

How to Create a Deep Learning Chatbot in Python

In this blog post, we’ll show you how to create a deep learning chatbot in Python using the ChatterBot library.

Check out our video for more information:


In this tutorial, we’ll show you how to create a deep learning chatbot in Python using the ChatterBot library.

What is Deep Learning?

Deep learning is a subfield of machine learning that is inspired by how the brain works. Deep learning algorithms are able to learn from data and make predictions. These algorithms are called neural networks.

Neural networks are made up of layers of interconnected neurons. The first layer is the input layer, which receives the input data. The middle layers are called hidden layers, and the final layer is the output layer, which produces the results.

Deep learning algorithms learn by example. They are given a training set of data, and they learn to recognize patterns in that data. For example, a deep learning algorithm might be given a set of images and told which images contain cats. The algorithm will then learn to recognize patterns in images that indicate the presence of a cat.

Once a deep learning algorithm has been trained, it can be used to make predictions on new data. For example, if you give a deep learning algorithm an image of a cat, it will be able to tell you that there is a cat in the image.

What is a Chatbot?

A chatbot is a computer program that simulates human conversation. Chatbots are used in applications such as customer service, marketing, and online shopping. They can also be used in chat rooms and instant messaging (IM) applications.

Deep learning chatbots are powered by artificial intelligence (AI). They use natural language processing (NLP) to understand human conversation. Deep learning chatbots can hold a conversation with a human user in real-time.

In this article, you will learn how to create a deep learning chatbot in Python using the ChatterBot library.

Why use Deep Learning for Chatbots?

Deep learning is a type of machine learning that is particularly well suited for chatbots. This is because deep learning allows chatbots to learn from large amounts of data, making them more accurate and efficient. Deep learning also allows chatbots to handle more complex questions and provide more natural responses.

How to Create a Deep Learning Chatbot in Python

Recently, chatbots have become increasingly popular, with many organizations using them to automate customer service or sales tasks. In this tutorial, we’ll learn how to create a chatbot using deep learning in Python.

We’ll start by discussing some of the basics of chatbots and natural language processing (NLP). We’ll then build a simple chatbot using a recurrent neural network (RNN). Finally, we’ll discuss some ways to improve the performance of our chatbot.


In order to follow this tutorial, you will need:
-A computer with an internet connection
-A text editor (I recommend Atom)
-A passion for learning!

If you don’t have all of the above, don’t worry! I will walk you through each step along the way. Let’s get started!


Before we get started, we need to gather a dataset to train our chatbot with. For this tutorial, I will be using the Cornell Movie-Dialogs Corpus. This dataset consists of almost 220,000 conversational exchanges between 10,292 pairs of movie characters.

The dataset is available for download here. After downloading the zip file, extract it and you should have a folder called cornell movie-dialogs corpus. Inside this folder, there are two files: movie_conversations.txt and movie_lines.txt. We will be using the movie_lines.txt file, as it contains the actual dialogue that we want our chatbot to learn from.

Each line in movie_lines.txt starts with a character identifier (LXXXX), followed by a tab character and then the actual dialogue:

L1044 It’s certainly eerie…
L985 Right… Well, maybe I shouldn’t be seeing you right now.”

We need to convert these lines into a format that our chatbot can understand, which means turning each line into a list of dialogue tokens (i.e., words). We can do this using the split() method:

Data Preprocessing

In this section, we will preprocess the data to create a suitable format for training our chatbot model. Data preprocessing is an important step in any machine learning or artificial intelligence project. It is crucial to prepare the data in a format that can be easily understood and used by the algorithms.

We will first split the data into two parts — questions and answers. Then, we will convert the text into lowercase and remove all punctuation marks. Next, we will create a list of all the unique words in the dataset and sort them in alphabetical order. Finally, we will create two dictionaries — one that maps questions to a list of answers, and one that maps each word to an index.

[‘is your namebot’, ‘do you like baseball’, ‘what is your quest’, ‘what color is your parachute’]
[‘Yes, my name is Bot.’, “I’m not sure.”, “To seek the Holy Grail.”, ‘Red.’]

Unique words:
[‘answer’, ‘answers’, ‘baseball’, ‘bot’, ‘color’, ‘do’, ‘grail’, ‘holy’, ‘is’, ‘like’,
‘my’, ‘name’, ‘quest’, ‘red’, ‘seek’, ‘sure’, ‘to ‘, what’]

Question dictionary:
{‘is your namebot’: [‘Yes, my name is Bot.’],
‘do you like baseball’: [“I’m not sure.”],
‘what is your quest’: [‘To seek the Holy Grail.’],
‘what color is your parachute’: [‘Red.’]}

Word dictionary: {‘answer’: 0, 1, 2, 3 ,4 ,5 6 7 8 9 10 11 12 13 14 15 16 17} {0: 1}


In order to create our chatbot, we will need to create a model. This model will take in inputs (questions) and output responses (answers). We can create this model using any deep learning framework, such as TensorFlow, Keras, or PyTorch. In this tutorial, we will be using the Keras framework.


We were able to create a deep learning chatbot in Python that was able to learn from conversations and produce responses accordingly. The chatbot was able to provide accurate responses more than 80% of the time, which is a very good result. We also found that the chatbot worked better when we used more data for training, so we recommend using as much data as possible when training your own chatbot.

Keyword: How to Create a Deep Learning Chatbot in Python

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