Articles

NLP Models for Writing Code: Program Synthesis

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Copilot, Codex, and AlphaCode: How Good are Computer Programs that Program Computers Now?

Enabled by the rise of transformers in Natural Language Processing (NLP), we’ve seen a flurry of astounding deep learning models for writing code in recent years. Computer programs that can write computer programs, generally known as the program synthesis problem, have been of research interest since at least the late 1960s (pdf) and early 1970s.

In the 2010s and 2020s, program synthesis research has been re-invigorated by the success of attention-based models in other sequence domains, namely the strategy of pre-training massive attention-based neural models (transformers) with millions or billions of parameters on hundreds of gigabytes of text.

Source de l’article sur DZONE

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2 Things You Need to Know About Reinforcement Learning: Computational and Sample Efficiency

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The High Cost of Deep Learning

Have you ever put on a sweater because the air conditioning was too cold? Forgotten to turn off the lights in another room before heading to bed? Do you commute to work more than 30 minutes every day just for the sake of “filling seats” at the office, even though everything you do at work could be done via laptop from home? 

In the counter-intuitive trade-offs between sample and computational efficiency in Reinforcement Learning, choosing evolution strategies can be smarter than it looks.

Source de l’article sur DZONE

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Building a Recommendation System Using Deep Learning Models

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In this article, I am going to explain how we integrate some deep learning models, in order to make an outfit recommendation system. We want to build an outfit recommendation system. We used four deep learning models to get some important characteristics of the clothing used by the user.

The recommendation systems can be classified into 4 groups:

Source de l’article sur DZONE

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