Deep Learning vs Reinforcement Learning

Short excerpt, worth reading the whole thing.  But instructive as it is:

Artificial Intelligence: What's The Difference Between Deep Learning And Reinforcement Learning?
 By Bernard Marr in Forbes

" ... What is deep learning?

" Deep learning is essentially an autonomous, self-teaching system in which you use existing data to train algorithms to find patterns and then use that to make predictions about new data. For example, you might train a deep learning algorithm to recognize cats on a photograph. You would do that by feeding it millions of images that either contains cats or not. The program will then establish patterns by classifying and clustering the image data (e.g. edges, shapes, colors, distances between the shapes, etc.). Those patterns will then inform a predictive model that is able to look at a new set of images and predict whether they contain cats or not, based on the model it has created using the training data.

Deep learning algorithms do this via various layers of artificial neural networks which mimic the network of neurons in our brain. This allows the algorithm to perform various cycles to narrow down patterns and improve the predictions with each cycle.

A great example of deep learning in practice is Apple’s Face ID. When setting up your phone you train the algorithm by scanning your face. Each time you log on using e.g. Face ID, the TrueDepth camera captures thousands of data points which create a depth map of your face and the phone’s inbuilt neural engine will perform the analysis to predict whether it is you or not.

What is reinforcement learning?

Reinforcement learning is an autonomous, self-teaching system that essentially learns by trial and error. It performs actions with the aim of maximizing rewards, or in other words, it is learning by doing in order to achieve the best outcomes. This is similar to how we learn things like riding a bike where in the beginning we fall off a lot and make too heavy and often erratic moves, but over time we use the feedback of what worked and what didn’t to fine-tune our actions and learn how to ride a bike. The same is true when computers use reinforcement learning, they try different actions, learn from the feedback whether that action delivered a better result, and then reinforce the actions that worked, i.e. reworking and modifying its algorithms autonomously over many iterations until it makes decisions that deliver the best result.

A good example of using reinforcement learning is a robot learning how to walk. The robot first tries a large step forward and falls. The outcome of a fall with that big step is a data point the reinforcement learning system responds to. Since the feedback was negative, a fall, the system adjusts the action to try a smaller step. The robot is able to move forward. This is an example of reinforcement learning in action.  ... "