Introduction to Neural Networks For Self Driving Cars

Introduction to Neural Networks For Self Driving Cars

Introduction to Neural Networks For Self Driving Cars. Foundational concepts in the fields of Machine Learning, Deep Neural Networks and Self Driving Cars

One-Hot Encoding

So, as we’ve seen so far, all our algorithms are numerical. This means we need to input numbers, such as a score in a test or the grades, but the input data will not always look like numbers.

Let’s say the module receives as an input the fact that you got a gift or didn’t get a gift. How do we turn that into numbers? Well, that’s easy. If you’ve got a gift, we’ll just say that the input variable is 1. And, if you didn’t get a gift, we’ll just say that the input variable is 0. But, what if we have more classes as before or, let’s say, our classes are Duck, Beaver and Walrus?

What variable do we input in the algorithm?

Maybe, we can input a 0 or 1 and a 2, but that would not work because it would assume dependencies between the classes that we can’t have. So, this is what we do. We will come up with one variable for each of the classes. That’s one variable for Duck, one for Beaver and one for Walrus. Now, if the input is a duck then the variable for duck is 1 and the variables for beaver and walrus are 0. Similarly for the beaver and the walrus. We may have more columns of data but at least there are no unnecessary dependencies. This process is called The One-Hot Encoding and it will be used a lot for processing data.

Maximum Likelihood

So we’re still in our search for an algorithm that will help us pick the best model that separates our data well. Well, since we’re dealing with probabilities then just use them in our favor. Let’s say I’m a student and I have two models. One that tells me that my probability of getting accepted is 80% and one that tells me the probability is** 55%.** Which model looks more accurate?

self-driving-cars deep-learning machine-learning data-science neural-networks

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