It is estimated that each year many people, most of whom are teenagers and young adults die by suicide worldwide. Suicide receives special attention with many countries developing national strategies for prevention. It is found that, social media is one of the most powerful tool from where we can analyze the text and estimate the chances of suicidal thoughts. Using nlp we can analyze twitter and reddit texts monitor the actions of that person. The most difficult part to prevent suicide is to detect and understand the complex risk factors and warning signs that may lead to suicide.

Robots being able to understand and react to language is a prerequisite for them to be able to interact with us. Therefore, it is a very active topic of research with newer, better and mostly bigger neural networks coming out on a very regular basis. It is extremely difficult to keep up with the state-of-the-art. Especially for people new to the topic, it can be very cumbersome to start off. Having recently gotten into Natural Language Processing (NLP), I want to give an overview of the basics of NLP for the interested reader.

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By using this framework, anyone can build neural networks with graphs. This also depicts operations as nodes. PyTorch is one of the most important frameworks in artificial intelligence. However, it is super adaptable in terms of integrations and languages. It was released by Facebook's AI research lab. This also acts as an open source library useful in deep learning, computer vision and natural language processing software. Another feature is its greater affinity with iOS as well as Android etc. It uses debugging tools like IPDB and PDB.

If you are a software developer who wants to build scalable AI-powered algorithms, you need to understand how to use the tools to build them. This Specialization will teach you best practices for using TensorFlow, a popular open-source framework for machine learning. In Course 3 of the deeplearning.ai TensorFlow Specialization, you will build natural language processing systems using TensorFlow. You will learn to process text, including tokenizing and representing sentences as vectors, so that they can be input to a neural network.

The Deep Learning Specialization is a foundational program that will help you understand the capabilities, challenges, and consequences of deep learning and prepare you to participate in the development of leading-edge AI technology. In this Specialization, you will build and train neural network architectures such as Convolutional Neural Networks, Recurrent Neural Networks, LSTMs, Transformers, and learn how to make them better with strategies such as Dropout, BatchNorm, Xavier/He initialization, and more. Get ready to master theoretical concepts and their industry applications using Python and TensorFlow and tackle real-world cases such as speech recognition, music synthesis, chatbots, machine translation, natural language processing, and more. AI is transforming many industries. The Deep Learning Specialization provides a pathway for you to take the definitive step in the world of AI by helping you gain the knowledge and skills to level up your career.

The "black-box" conundrum is one of the biggest roadblocks preventing banks from executing their artificial intelligence (AI) strategies. It's easy to see why: Picture a large bank known for its technology prowess designing a new neural network model that predicts creditworthiness among the underserved community more accurately than any other algorithm in the marketplace. This model processes dozens of variables as inputs, including never-before-used alternative data. The developers are thrilled, senior management is happy that they can expand their services to the underserved market, and business executives believe they now have a competitive differentiator. But there is one pesky problem: The developers who built the model cannot explain how it arrives at the credit outcomes, let alone identify which factors had the biggest influence on them.

Natural Language Processing (NLP) has long played a significant role in the compliance processes for major banks around the world. By implementing the different NLP techniques into the production processes, compliance departments can maintain detailed checks and keep up with regulator demands. All of these areas can benefit from document processing and the use of NLP techniques to get through the process more effectively. Certain verification tasks fall beyond the realm of using traditional, rules-based NLP systems. This is where deep learning can help fill these gaps, providing smoother and more efficient compliance checks. There are several challenges that make the rules-based system more complicated to use when undergoing check routines.

Are you looking for the Best Certification Courses for Artificial Intelligence?. If yes, then your search will end after reading this article. In this article, I will discuss the 10 Best Certification Courses for Artificial Intelligence. So, give your few minutes to this article and find out the Best AI Certification Course for you. Artificial Intelligence is changing our lives.

This article was published as a part of the Data Science Blogathon. A few days ago, I came across a question on "Quora" that boiled down to: "How can I learn Natural Language Processing in just only four months?".