Pre-trained networks give smaller teams a leg up - Deepstash
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Pre-trained networks give smaller teams a leg up

The use of pre-trained networks allows a startup, for example, to build a product with much less data and compute resources than would otherwise be needed if starting from scratch. This approach is also becoming popular in academia, where researchers can quickly fine-tune a pre-trained network for a new task, and then publish the results.


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The tradeoff

The opportunities and risk around using hosted and pre-trained models has led many companies to leverage cloud APIs in the “experimentation phase” to kickstart product development.

Once a company has determined it has a product-market fit, it...


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Foundational models

Foundational models

Pre-training has continued to evolve with the emergence of foundation models such as BERT, GPT, DALL-EC...


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The risks of foundation models: Outsourced innovation

Dataset alignment can also be a challenge for those using foundation models. Pre-training on a large general-purpose dataset is no guarantee that the network will be able to perform a new task on proprietary data. The network may be so lacking in context or biased based on its pre-training, that ...


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Expensive to train?

Expensive to train?

Some neural networks are very expensive to train. This led to the popularization of an approach known as pre-training, whereby a neural network is first trained on a large general-purpose dataset using significant amounts of computational resources, and then fine-tuned ...


234 reads

The risks of foundation models: Size & Cost

One of the risks associated with foundation models is their ever-increasing scale. Neural networks such as Google’s T5-11b (open sourced in 2019) already require a cluster of expensive GPUs simply ...


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