== Progress ==
== Intro Introduction to Neural Networks ==Artificial intelligence Before we can dive into PyTorch, we need to introduce the general concepts behind Neural Networks and AI programing. You have probably heard of AI and machine learning, and have seen some examples of what they can do, but I wonder if you know the difference between AI, Machine Learning, and Deep Learning? The confusion usually comes from thinking of them as separate ideas, but it is an umbrella term with many levels. more helpful to think of them as belonging to a hierarchy, where Machine learning Learning is a subset of AI that focuses on these self-teaching algorithms. , and Deep learning Learning is a further smaller subset within the subset of machine learning where multi-layered artificial neural networks are employed to allow for more versatile, independent learning [1]. Machine learning algorithms that do not utilize deep learning would be less versatile in what they could learn and would need more hand-holding by programmersLearning. A This handy infographic is shown belowgoes into more detail:
[[File=== Machine Learning vs. Deep Learning ===Even with the above infographic, there is still probably some confusion with what distinguishes Deep Learning from Machine Learning. If you are confused, it is an important reminder that everything it says about Machine Learning also applies to Deep Learning, “enable machines to improve with experience.” The main difference is that conventional Machine Learning algorithms require manual intervention in the area of feature extraction, while Deep Learning algorithms do it themselves. See the infographic below:Example What this means is that Deep Learning algorithms have the added advantage that they can be setup randomly, with random weights and biases.jpg]]And as along as we tell it what we want the output to be, let’s say a car, it will find out the best way to distinguish whether any given input is in indeed a car. In other words, it can teach itself. On the other hand, with traditional machine learning, the programmer would have to tell the algorithm what “features” to look for when determining if something was a car. Deep Learning algorithms can do what they do because they use Neural Networks. There are various kinds of Deep Learning Neural Networks, such as Artificial Neural Networks (ANN), Convolutional Neural Networks (CNN), and Recurrent Neural Networks (RNN). For our project, we are interested in an algorithm that can recognize numbers from pixel images. To do this we create a standard ANN, and then convert it into a more efficient CNN. === ANN ===This section covers the background knowledge you’ll need to understand what our ANN code is doing. This type of neural network is probably the simplest and easiest to understand. The name Neural Network comes from the fact that their architecture is vaguely modelled off neurons in the brain. They can be activated like a neuron and they often linked with other neurons. The analogy is considered misleading, so I’ll stop it there. It’s better think of these artificial neurons as bits that can hold a value from zero to one, instead of only zero or one. For our purposes we want classify an image. So, the neural network starts by associating each pixel with a value, like previously mentioned, from zero to one. One represents a fully colored in pixel, while zero represents an empty pixel. See below:
