Students will contribute to the course by:
There are many Python tutorials online for starters. Here we provide you with one tutorial link. Remember that for python 2.7 is used on google cloud platform. However, if you want to run the code on your local windows machine, then it should be python 3. Moreover, for this course we recommend students to use python 3 grammar when finishing all programming assignments.
Students will be running deep learning code on the Google Compute Engine (GCE) virtual machine (VM) instances, with access to GPU resources. This tutorial teaches how to setup a VM instance using the VM image which instructors have created for students.
For those who have a GPU-enabled computer and want to build a deep learning environment on the local machine/laptop, please see this guide on how to setup TensorFlow and PyTorch working environments.
The GCP VM instances which we use are Linux-based. If you haven't used Linux system before, this tutorial offers hands-on instruction to basic Linux commands that you will be using throughout the course.
TensorFlow is an open-source deep learning framework developed by Google. It is essential that you learn to program in TensorFlow for this course.
All of our homework distributions will be based on Bitbucket repositories, which can be manipulated by Git commands. If you don't know what Git is, it is strongly recommended that you read the first 3 chapters of this website.
Solutions to some common problems
ECBM E4040 Neural Networks and Deep Learning, 2017.