Machine Learning with DeepRacer

In my previous article, we talked about DeepRacer and how we could all get into creating our own model. Here we will dive much deeper into how is DeepRacer learning from his actions, but also the different types of Machine Learning Algorithm available today.

Machine Learning

Machine Learning (ML) is a broad term to talk about self-learning algorithms. It is used in multiple domains: engineering, retails, finance, commerce and more.

There are 3 families of learning. Supervised Learning is used to predict or classify labeled data. Unsupervised Learning is mainly used for clustering unlabelled data ( finding correlations ). And Reinforcement learning is used when an agent takes actions within an environment. For example an autonomous car within the city or a rescue-drone…

We are going to explain mostly what Reinforcement Learning (RL) is, but we first need to explain what are Supervised (SL) and Unsupervised Learning (UL) to understand best RL.

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An introduction to AWS DeepRacer

Here is a quick and easy to read article about DeepRacer from Amazon WebServices, a car learning to race with Reinforcement Learning. This article will be followed by one article diving deeper into Reinforcement Learning, and a second concluding on the training our DeepRacer did and the results to the AWS DeepRacer real-life track.

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AWS DeepRacer is a 1/18th scale autonomous racing car. Launched at AWS re:Invent 2018, it is a new opening toward Machine Learning and especially Reinforcement Learning. It is driven by multiple AWS software such as Amazon SageMaker, Amazon RoboMaker for the main section.

The car is composed of two main parts: the chassis/servos and the computer. Here we are going to center our attention to creating our first model.

Let’s start !


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