Alex Moltzau 莫战

Sep 4, 2019

2 min read
Photo by @ozgomz

What is Transfer Learning?

A short introduction to the concept

My first encounter with this concept was through the article on www.climatechange.ai in one of the papers on tackling climate change with machine learning. Therefore I thought I would explore the term shortly.

It is a deep learning technique that enables developers to harness a neural network used for one task and apply it to another domain. A blog post on NVIDIA attempts to explain the process in layman terms with convolutional horse prediction:

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This can be used for image recognition, but it can also be used for sound. However, you’ll need a lot of data, sometimes as much as a million hours of speech.

Transfer learning is useful when you have insufficient data for a new domain you want handled by a neural network and there is a big pre-existing data pool that can be transferred to your problem.

NVIDIA Transfer Learning Toolkit offers GPU-accelerated pre-trained models and functions to fine-tune your model for various domains such as intelligent video analytics and medical imaging.

TensorFlow has a tutorial on how to do this for beginners in Keras and those that have a machine learning background.

In an energy efficiency perspectivea blog post from IBM argues:

I stil have not made up my mind, but transfer learning is a topic that I will have to pursue further.

This is day 93 of #500daysofAI. My current focus for day 50–100 is on AI Safety. If you enjoy this please give me a response as I do want to improve my writing or discover new research, companies and projects.