Skip to content

A survey class of neural network implementation and applications. Topics include: optimization - stochastic gradient descent, adaptive and 2nd order methods, normalization; convolutional neural networks - image processing, classification, detection, segmentation; recurrent neural networks - semantic understanding, translation, question-answering…

Notifications You must be signed in to change notification settings

Anthony2018/CSE599

Repository files navigation

CSE 490/599 G1 Introduction to Deep Learning

What the heck is this codebase?

During this class you'll be building out your own neural network framework. We'll take care of some of the more boring parts like loading data, stringing things together, etc. so you can focus on the important parts. We will be implementing everything using Numpy and Numba in a Conda environment. If you are not familiar with them, take a few minutes to learn the ins and outs. PyTorch uses a Numpy-like interface, so it will be good to know for the other parts of the homework as well.

Setup Conda and the codebase

First install Miniconda (https://docs.conda.io/en/latest/miniconda.html). Then run the following commands.

conda create -n dl-class python=3.6.9
git clone https://gitlab.com/danielgordon10/dl-class-2019a.git
cd dl-class-2019a
conda deactivate
conda env update -n dl-class -f environment.yml
conda activate dl-class

About

A survey class of neural network implementation and applications. Topics include: optimization - stochastic gradient descent, adaptive and 2nd order methods, normalization; convolutional neural networks - image processing, classification, detection, segmentation; recurrent neural networks - semantic understanding, translation, question-answering…

Resources

Stars

Watchers

Forks

Releases

No releases published

Packages

No packages published