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Natural language processing (NLP) technologies are widely deployed to process rich natural language text data for search and recommender systems. Achieving high-quality search and recommendation results requires that information, such as user queries and documents, be processed and understood in an efficient and effective manner. In recent years, the rapid development of deep learning models has been proven successful for improving various NLP tasks, indicating the vast potential for further improving the accuracy of search and recommender systems.
Technique can trigger hundreds of attacks in a fraction of the time required for classic trojan assaults against deep learning systems.
In this article, we’ll study a popular statistic model – Gaussian mixture model (GMM) and see how it can be readily applied for the unsupervised task of clustering. We’ll look at what these models are, and how they work both mathematically as well as “Pythonically”. So, before we begin, what’s our motivation to study these models?
Transformers-based models, such as BERT, have been one of the most successful deep learning models for NLP. Unfortunately, one of their core limitations is the quadratic dependency (mainly in terms of memory) on the sequence length due to their full attention mechanism. To remedy this, we propose, BigBird, a sparse attention mechanism that reduces this quadratic dependency to linear.
Understanding the concepts of Machine Learning starts with understanding the fundamentals. On your way to mastery, it's crucial for you to understand how certain concepts were derived, and why things work like they do. Starting with these resources is the best way to do so.