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ML and NLP Research Highlights of 2021

Sebastian Ruder

2021) 2021 saw many exciting advances in machine learning (ML) and natural language processing (NLP). Pre-trained models were applied in many different domains and started to be considered critical for ML research [1]. 8) ML for Science The architecture of AlphaFold 2.0. Credit for the title image: Liu et al. What happened?  

NLP 52
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DL4Proteins Notebook Series Bridging Machine Learning and Protein Engineering: A Practical Guide to Deep Learning Tools for Protein Design

Marktechpost

It explains how CNNs utilize convolutional layers to extract spatial features from input data. It explains the core principles behind AlphaFold, including its reliance on deep learning and the use of multiple sequence alignments (MSAs) to predict protein folding. Dont Forget to join our 60k+ ML SubReddit.

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Sentiment Analysis With SparkNLP and Comet

Heartbeat

Picture by Anna Nekrashevich , Pexels.com Introduction Sentiment analysis is a natural language processing technique which identifies and extracts subjective information from source materials using computational linguistics and text analysis. We’re committed to supporting and inspiring developers and engineers from all walks of life.

NLP 52
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A Gentle Introduction to GPTs

Mlearning.ai

It combines techniques from computational linguistics, probabilistic modeling, deep learning to make computers intelligent enough to grasp the context and the intent of the language. As explained earlier, to get a better and robust model it has to be trained on large dataset.

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Instruction fine-tuning for FLAN T5 XL with Amazon SageMaker Jumpstart

AWS Machine Learning Blog

We provided code explaining how to retrain the model using data for the target task and deploy the fine-tuned model behind an endpoint. Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers). He loves developing user friendly ML systems.

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Natural Language Processing with R

Heartbeat

Natural Language Processing (NLP) plays a crucial role in advancing research in various fields, such as computational linguistics, computer science, and artificial intelligence. We’d also do a little NLP project in R with the “sentimentr” package. We pay our contributors, and we don’t sell ads.

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How Grammarly strives for superhuman communication assistance

Snorkel AI

Timo Mertens is the Head of ML and NLP Products at Grammarly. These two systems come together, and ultimately we classify the sets of transformations and explain them to the user. Ultimately, explainability is key. His talk was followed by an audience Q&A moderated by SnorkelAI’s Priyal Aggarwal.