DPS921/PyTorch: Convolutional Neural Networks

Convolutional Neural Networks Using Pytorch

The basic idea was to create a convolutional neural network using the python machine learning Framework PyTorch. The actual code will

be written in Jupyter Lab both for demonstration and implementation purposes. Furthermore, using the the torchvision dataset, the goal

was to show the training of the neural network, and show the classification of several images which have a single digit from 0 - 9.

A successful execution will show the correct determination of what number resides in that specific image. As part our of research,

We will explain in detail how an actual convolution neural network works at a fundamental level. Will we will both take a graphical

and mathematical approach to explaining the different parts of the neural network and how it comes together as whole. Furthermore,

we will briefly explain how it relates to parallel computing and how parallel computing plays a significant role in driving the

implementation of the neural network.

Group Members

1. Shervin Tafreshipour

2. Parsa Jalilifar

3. Novell Rasam

Intro to Neural Networks

Artificial intelligence is an umbrella term and in fact, has many levels of implementation. Machine learning is a subset of AI that focuses on these self-teaching algorithms. Deep learning is a further 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 programmers. A handy infographic is shown below:

Update 1: Friday, Nov 6, 2020 - Created a basic CNN in jupyterlab using the pytorch Framework.