If you are interested in natural language processing (NLP) and Deep Learning, the following textbooks or tutorial materials provide an understanding of the field of NLP and its applications in health. You can acquire hands-on experience with Python programming and the tool kit will provide useful skills for managing text data for solving a variety of problems in the health domain.

This section will cover what the next word prediction model built will exactly perform. The model will consider the last word of a particular sentence and predict the next possible word. We will be using methods of natural language processing, language modeling, and deep learning. We will start by analyzing the data followed by the pre-processing of the data. We will then tokenize this data and finally build the deep learning model.

In this article, we will look at toxic speech detection, the problem of text moderation and understand the different challenges that one might encounter trying to automate the process. We look at several NLP and deep learning approaches to solve the problem and finally implement a toxic speech classifier using BERT embeddings. As of June 2019 there are now over 4.4 billion internet users. According to the latest Domo Data Never Sleeps report, Twitter users send 511,200 tweets per minute. While that happens, TikTok gets banned in Indonesia, Discord sees an increasing number of neo-Nazi posts, tech and film celebrity accounts get hacked so hackers can spurt out several racist slurs and hate speech volumes rise in India on facebook due to the controversial Citizenship Amendment Act (CAA). Social media continues to be used by several to incite violence, spread hate and target minorities based on religion, sex, race and disabilities.

As the amount of data continues to grow at an almost incomprehensible rate, being able to understand and process data is becoming a key differentiator for competitive organizations. Machine Learning applications are everywhere, from self-driving cars to spam detection, document search, and trading strategies, to speech recognition. This makes machine learning well-suited to the present-day era of Big Data and Data Science. The main challenge is how to transform data into actionable knowledge. In this course, you'll be introduced to the Natural Processing Language and Recommendation Systems, which help you run multiple algorithms simultaneously. Also, you'll learn about Deep learning and TensorFlow.

Convolutional Neural Network (CNNs) are typically associated with Computer Vision. CNNs are responsible for major breakthroughs in Image Classification and are the core of most Computer Vision systems today. More recently CNNs have been applied to problems in Natural Language Processing and gotten some interesting results. In this paper, we will try to explain the basics of CNNs, its different variations and how they have been applied to NLP. This is a more concise survey than the paper below, and does a good job at 1/5 the length.

Autonomous or driverless vehicles are a hot topic on the AI scene right now. Google, Volvo, Tesla, Uber… these are just some of the big names in the race to prove that driverless or autonomous vehicles are better and maybe even safer than human-driven vehicles. I was at a family event recently and two guests were chatting about the Artificial Intelligence (AI) component of driverless or autonomous vehicles and more specifically, how these vehicles are currently unable to detect human movement at high speed. One cited the example of a child stepping into the road whilst a vehicle was approaching at high speed. Some debate ensued about the width of lanes in the road (surrounding the vehicle) and the impact they have on the judgement of the driverless/autonomous vehicles.