Learn more about startup with this collection
How to analyze churn data and make data-driven decisions
The importance of customer feedback
How to improve customer experience
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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MORE IDEAS ON THIS
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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92 reads
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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105 reads
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 ...
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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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109 reads
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