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Adaptive Gradient Algorithm

Dataconomy

The Adaptive Gradient Algorithm (AdaGrad) represents a significant stride in optimization techniques, particularly in the realms of machine learning and deep learning. By dynamically adjusting the learning rates for different parameters during model training, AdaGrad helps tackle challenges of convergence and efficiency.

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Top 10 Deep Learning Platforms in 2024

DagsHub

Source: Author Introduction Deep learning, a branch of machine learning inspired by biological neural networks, has become a key technique in artificial intelligence (AI) applications. Deep learning methods use multi-layer artificial neural networks to extract intricate patterns from large data sets.

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What Is Retrieval-Augmented Generation?

Hacker News

Patrick Lewis “We definitely would have put more thought into the name had we known our work would become so widespread,” Lewis said in an interview from Singapore, where he was sharing his ideas with a regional conference of database developers. Today, LLMs are taking question-answering systems to a whole new level.

Database 181
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How artificial intelligence went from science fiction to science itself?

Dataconomy

Marvin Minsky offers a definition of AI as the development of computer programs that engage in tasks that currently rely on high-level mental processes such as perceptual learning, memory organization, and critical reasoning. This demonstrated the astounding potential of machines to learn and differentiate between various objects.

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Deep text-pair classification with Quora's 2017 question dataset

Explosion

In this post, I’ll explain how to solve text-pair tasks with deep learning, using both new and established tips and technologies. The SNLI dataset is over 100x larger than previous similar resources, allowing current deep-learning models to be applied to the problem.

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Introducing spaCy v2.1

Explosion

It’s widely used in production and research systems for extracting information from text, developing smarter user-facing features, and preprocessing text for deep learning. In 2011, deep learning methods were proving successful for NLP, and techniques for pretraining word representations were already in use.

Python 52
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What Is ChatGPT Doing … and Why Does It Work?

Hacker News

And in fact the big breakthrough in “deep learning” that occurred around 2011 was associated with the discovery that in some sense it can be easier to do (at least approximate) minimization when there are lots of weights involved than when there are fairly few. First comes the embedding module.