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I recently saw a video lecture from Jeremy Howard of fast.ai in which he states that transfer learning is better than reinforcement learning. But I was unable to understand the reasoning behind it.

Can someone explain to me or point to any evidence stating which is better and why?

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  • $\begingroup$ Could you please link the video or provide a more specific quote with a bit of context? In machine learning, there is often no "better" solution in general, it depends very much on the problem you are trying to solve. It would be very useful to know what problem Jeremy Howard was trying to solve with transfer learning vs reinforcement learning, or whether he meant something vague such as "a more practical skill to learn" or "more fun". $\endgroup$ Aug 12, 2019 at 6:50
  • $\begingroup$ It was from Course.fast.ai lesson7 of part 1. The discussion about Reinforcement Learning VS Transfer Learning occurs @2:08:21 of this lecture. $\endgroup$ Aug 12, 2019 at 16:31

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I didn't watch this lecture, but, the way I see it, reinforcement learning and transfer learning are absolutely different things. Transfer learning is about fine-tuning a model, which was trained on one data and then striving to work with another data and another task. For example if you use weights of pretrained model on imagenet and then implement it to your dataset, while your dataset consists of small amount of different species of birds images (which might be not sufficient to train for example unet from a scrath). Reinforcement learning is about how some agent should response to environment condition to receive high reward. There is an illustrative example with a drone making a delivery, when there is some range of restrictions of the environment. There are two links, which might be useful: https://machinelearningmastery.com/transfer-learning-for-deep-learning/
https://skymind.com/wiki/deep-reinforcement-learning

I guess, I can't answer, which approach is better, because they aim to solve different challenges.

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