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A Collection Of Best Practices And Insights
Tech Talks
CPUs may replace GPUs for training Deep Learning Models
CPUs can be four to fifteen times faster than GPUs at training Deep Learning models using new methods being developed at Rice University. This is almost the exact opposite of the current situation.
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Tech Talks
The Pile: Dream Training Dataset
The Pile is an amazing new dataset for training language models. It is like an all-you-can-eat buffet for those of us creating models in the GPT-3 space.
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East Agile
EAST AGILE HOLIDAYS US 2021
EAST AGILE HOLIDAYS US 2021
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East Agile
EAST AGILE HOLIDAYS VIETNAM 2021
EAST AGILE HOLIDAYS VIETNAM 2021
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Tech Talks
Generative Pre-Training: GPT-2 vs. GPT-3
OpenAI’s language models GPT-2 and GPT-3 have revolutionized natural language processing (NLP) technology through their Transformer-based language models. When GPT-2 was first released in 2019 in OpenAI’s paper Language Models are Unsupervised Multitask Learners [1] it was groundbreaking, leading to extensions by Nvidia (Megatron-LM, 2020) and by Microsoft (Turing-NLG, 2020). In this review, I will summarize the two key papers by OpenAI that have motivated much of the recent R&D around notional.ai by East Agile.
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Tech Talks
Half-baked AI might be better than you think.
Some folks have observed that language models like GPT-3, GPT-2, Megatron-LM, Turing-NLG, continue to improve in terms of evaluation tests as they are trained further. This could also mean that businesses do not need to train AI models to completion to get just as much value from a language model in terms of their actual business needs.
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