Generative Adversarial Networks (GANs) are one of the hottest topics in computer science in recent times. They are a clever way of training a generative model (unsupervised learning) by framing the problem as a supervised learning problem. The main idea is that two different models are trained simultaneously by an adversarial process. Generative adversarial networks are based on a game theoretic scenario in which the generator network must compete against an adversary. The generator network directly produces samples.

Serverless compute abstracts away provisioning, managing severs and configuring software, simplifying model deployment. Aimed towards becoming a Full Stack Data Scientist. Serverless is the next step in Cloud Computing. This means that servers are simply hidden from the picture. In serverless computing, this separation of server and application is managed by using a platform.

This paper conducts a comparative study of various learning models on Hate and Abusive Speech on Twitter dataset. The following script will install required Python packages. We do not provide the code for crawling text data. However, you can ready your dataset by creating crawled_data.pkl While creating the pickle file, you will need to create a python dictionary with the following structure: _dict[_id] [tweet_label, tweet_text, context_text].

Ok, so finally we are at a stage where we can do something we love. Use Data Science to power our Application/Chatbot. Let us start with creating a rough architecture of what we are going to do next. We will need to create two classifiers and save them as .pkl To keep it simple we will create simple TFIDF models.