5

Humans are set loose in the world and go about their days doing stuff. Whenever they do specific things, their brain sends them good signals (endorphins, joy, etc.) or bad signals (pain, sadness, etc.). They learn through these signals which things they should be doing and which things they shouldn't be doing. Sometimes the signal is immediate and you know ...


5

The famous book Reinforcement learning: an introduction by Sutton and Barto provides an intuitive description of reinforcement learning (that everyone is possibly able to understand). Reinforcement learning is learning what to do — how to map situations to actions — so as to maximize a numerical reward signal. The learner is not told which actions to take, ...


5

Reinforcement Learning can be explained by a few equations. However I assume that this is not what you are looking at since the explanation should be for someone having a non-STEM background. Not to say non-STEM folks are not able to understand math equations, but intuition comes easier with words and examples in my opinion. Reinforcement Learning is about ...


3

Most model-fitting is stochastic, so you get different parameters every time you train, and you usually can't say that one algorithm will always give you a better-performing model. However, since you can retrain many times to get a distribution of models, you can use a statistical test like the T-Test to say "algorithm A usually produces a better model ...


3

There are many computer vision (CV) algorithms and models that are used for different purposes. So, of course, I cannot list all of them, but I can enumerate some of them based on my experience and knowledge. Of course, this answer will only give you a flavor of the type of algorithm or model that you will find while solving CV tasks. For example, there are ...


2

DP, Monte Carlo, and TD are methods of estimating returns. Policy gradient describes methods of learning a policy. So policy gradients serve a different purpose than the other things you mentioned. For clarity, you can use Monte Carlo or TD methods to estimate returns to construct the loss that you get your policy gradient from.


1

Computer vision is a wide field, and besides the fact that deep learning dominates, there are still many, many other algorithms that see widespread use in both academia and industry. For tasks such as image classification / object recognition, the typical paradigm is some CNN architecture such as a ResNet or VGG. There has been lots of works to extend and ...


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