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Selecting an online course that will match your requirements is very frustrating if you have high standards. Most of them are not comprehensive and a lot of time spent on them is wasted. How would you feel, if someone would provide you a critical path and tell, what modules exactly and in which order will provide you comprehensive, expert-level knowledge? Awesome. That is why I am going to help you with this guide to selecting a Natural Language Processing course, utilizing my 8 years of practical experience in Machine Learning.

Learn how different Word2Vec architectures behave in practice. This is to help you make an informed decision on which architecture to use given the problem you are trying to solve. In this article, we will look at how the different neural network architectures for training a Word2Vec model behave in practice. The idea here is to help you make an informed decision on which architecture to use given the problem you are trying to solve.

BERT stands for Bidirectional Encoder Representations from Transformers. This model is basically a multi-layer bidirectional Transformer encoder(Devlin, Chang, Lee, & Toutanova, 2019), and there are multiple excellent guides about how it works generally, includingthe Illustrated Transformer. What we focus on is one specific component of Transformer architecture known as self-attention. In a nutshell, it is a way to weigh the components of the input and output sequences so as to model relations between them, even long-distance dependencies.

Machine learning is an intimidating topic to tackle for the first time. The term encompasses so many fields, research topics and business use cases, that it can be difficult to even know where to start. To combat this, it’s often a good idea to turn to textbooks that will introduce you to the basic principles of your new field of research. This holds true for AI and machine learning, especially if you have a background in statistics or programming.

I went through 687 papers that were accepted to ICLR 2020 virtual conference (out of 2594 submitted – up 63% since 2019!) and identified 9 papers with the potential to advance the use of deep learning NLP models in everyday use cases. Source: topbots.com

Three months ago, I participated in a data science challenge that took place at my company. The goal was to help a marine researcher better identify whales based on the appearance of their flukes. More specifically, we were asked to predict for each image of a test set, the top 20 most similar images from the full database (train+test). This was not a standard classification task. I spent 3 months prototyping and ended up third at the final (private) leaderboard.

Facebook AI has built and open-sourced Blender, the largest-ever open-domain chatbot. It outperforms others in terms of engagement and also feels more human, according to human evaluators. The culmination of years of research in conversational AI, this is the first chatbot to blend a diverse set of conversational skills — including empathy, knowledge, and personality — together in one system. We achieved this milestone through a new chatbot recipe that includes improved decoding techniques, novel blending of skills, and a model with 9.

If AI is really going to make a difference to patients we need to know how it works when real humans get their hands on it, in real situations. The covid-19 pandemic is stretching hospital resources to the breaking point in many countries in the world. It is no surprise that many people hope AI could speed up patient screening and ease the strain on clinical staff. But a study from Google Health—the first to look at the impact of a deep-learning tool in real clinical settings—reveals that even the most accurate AIs can actually make things worse if not tailored to the clinical environments in which they will work.

Facebook AI and AWS have partnered to release new libraries that target high-performance PyTorch model deployment and large scale model training. As part of the broader PyTorch community, Facebook AI and AWS engineers have partnered to develop new libraries targeted at large-scale elastic and fault-tolerant model training and high-performance PyTorch model deployment. These libraries enable the community to efficiently productionize AI models at scale and push the state of the art on model exploration as model architectures continue to increase in size and complexity.

A team of researchers at the MIT Computer Science & Artificial Intelligence Lab (CSAIL) recently released a framework called TextFooler which successfully tricked state-of-the-art NLP models (such as BERT) into making incorrect predictions. Source: infoq.com