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NeurIPS 2021 – 10 Papers You Shouldn’t Miss
The 2021 edition of the most beloved conference in Artificial Intelligence is here to end the year with a ‘grand finale’. The growth of the conference hasn’t ceased: last year’s conference — which we also reviewed — had 1899 main-track papers accepted, compared to this year’s 2334. Some of the published papers have been on arxiv.org for…
A Guide To Knowledge Graphs
A consolidation of notes that briefly but gently introduces Knowledge graphs and shines a light on several practical aspects.
Why Graph Theory Is Cooler Than You Thought
What are Graphs? Talk to a scientist in just about any discipline, and ask them the question — based on their discipline — “how does that stuff work?” You’ll likely find that there are systems and networks that you have to consider before you can really understand how any given thing works: whether that’s the human body, a food…
10 Leading Language Models For NLP In 2021
The introduction of transfer learning and pretrained language models in natural language processing (NLP) pushed forward the limits of language understanding and generation. Transfer learning and applying transformers to different downstream NLP tasks have become the main trend of the latest research advances. At the same time, there is a controversy in the NLP community…
BERT Inner Workings
I created this notebook to better understand the inner workings of Bert. I followed a lot of tutorials to try to understand the architecture, but I was never able to really understand what was happening under the hood. For me it always helps to see the actual code instead of just simple abstract diagrams that…
Pretrain Transformers Models in PyTorch Using Hugging Face Transformers
This notebook is used to pretrain transformers models using Hugging Face on your own custom dataset. What do I mean by pretrain transformers? The definition of pretraining is to train in advance. That is exactly what I mean! Train a transformer model to use it as a pretrained transformers model which can be used to fine-tune it on a specific task!…
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