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Suicidal Text Analysis Using NLP

#artificialintelligence

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.


On Natural Language Processing

#artificialintelligence

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.


An AI-Based Approach to Monitor, Expose, and Counter Anti-Hindu Hate

#artificialintelligence

MyIndMakers enables the exchange of Global Ideas and Solutions from India. All day news updates related to Business, Hindu, Hinduism, India, Indic, Culture, Travel, Religion, Politics, Foreign Policy, Modi, Swami, BJP, Congress, Trump, Biden, Israel, Jihad, Christianity, China, Japan, Book Reviews, Movie Reviews, Indian Artciles, Blogs, Interviews, Podcasts, Videos, MyIndBook, MyIndMakers, myind.net,Hindumisia, hindumisia.ai, AI-based approach, Deep Learning, Anti-Defamation League, Online Hate Index


6 Artificial Intelligence Frameworks to Learn

#artificialintelligence

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.


Natural Language Processing in TensorFlow

#artificialintelligence

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.


Convolutional Neural Networks

#artificialintelligence

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.


Deep Learning For Compliance Checks: What's New? - KDnuggets

#artificialintelligence

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.


Roadmap to Master NLP in 2022 NLP

#artificialintelligence

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?".


What is Deep Learning?

#artificialintelligence

What do you achieve with deep learning? Deep learning is a part of our daily life. For example, when you upload a photo to Facebook, deep learning helps by automatically tagging your friends. If you use digital assistants like Siri, Cortana or Alexa, they serve you to the benefit with the help of natural language processing and speech recognition. When you meet with overseas customers on Skype, it translates in real time.


Intel brings more powerful AI training and inference to the data center

ZDNet

Stephanie Condon is a senior staff writer for Red Ventures based in Portland, Oregon, covering business technology for ZDNet. Intel on Tuesday launched the latest generation of its deep learning processors for training and inference, Habana Gaudi2 and Habana Greco, making AI more accessible and valuable for its data center customers. At its Intel Vision event, the chipmaker also shared details about its IPU and GPU portfolios, all aimed business customers. "AI is driving the data center," Eitan Medina, COO of Habana Labs, Intel's data center team focused on AI deep learning processor technologies, said to reporters earlier. But different customers are using different mixes for different applications."