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

Marktechpost

With topics ranging from neural networks to graph models, these open-source notebooks enable hands-on learning and bridge the gap between research and education. The notebook “ Neural Networks with NumPy ” introduces the foundational concepts of neural networks and demonstrates their implementation using NumPy.

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SQuARE: Towards Multi-Domain and Few-Shot Collaborating Question Answering Agents

ODSC - Open Data Science

Are you curious about explainability methods like saliency maps but feel lost about where to begin? Moreover, combining expert agents is an immensely easier task to learn by neural networks than end-to-end QA. Iryna is co-director of the NLP program within ELLIS, a European network of excellence in machine learning.

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What is Machine Translation? A Comprehensive Business Guide

Defined.ai blog

In this guide, we’ll demystify it, explaining how it works, its advantages, and how to implement it effectively in your company. Machine translation is a subfield of computational linguistics that uses software to translate text or speech from one language to another. This is precisely where machine translation steps in.

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68 Summaries of Machine Learning and NLP Research

Marek Rei

link] Proposes an explainability method for language modelling that explains why one word was predicted instead of a specific other word. Adapts three different explainability methods to this contrastive approach and evaluates on a dataset of minimally different sentences. Computational Linguistics 2022.

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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. C++’s main advantage is its speed, which allows it to do complex computations more quickly, which is vital for AI development.

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ML and NLP Research Highlights of 2021

Sebastian Ruder

  While pre-trained transformers will likely continue to be deployed as standard baselines for many tasks, we should expect to see alternative architectures particularly in settings where current models fail short, such as modeling long-range dependencies and high-dimensional inputs or where interpretability and explainability are required.

NLP 52
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Parsing English in 500 Lines of Python

Explosion

It would be relatively easy to provide a beam-search version of spaCy…But, I think the gap in accuracy will continue to close, especially given advances in neural network learning. But the parsing algorithm I’ll be explaining deals with projective trees. Syntactic Processing Using the Generalized Perceptron and Beam Search.

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