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

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.

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

Foundational concepts in the fields of Machine Learning and Deep Neural Networks. Now we will learn how to use one of the most exciting tools and self-driving car development, deep neural networks. A deep neural network is just a term that describes a big multi-layer neural network.

Cheat Sheets for AI, Neural Networks, Machine Learning, Deep Learning & Big Data

Introduction to Convolutional Neural Networks for Self Driving Cars. Introductory concepts in the field of Image Recognition using Convolutional Neural Networks

Foundational Concepts in the field of Deep Learning and Machine Learning. We’ll focus on TensorFlow because if one becomes a machine learning expert, these are the tools that people in the trade use everyday.

The past few decades have witnessed a massive boom in the penetration as well as the power of computation, and amidst this information.