Skip to content

McZyWu/Deep-Representations-and-Learning

 
 

Folders and files

NameName
Last commit message
Last commit date

Latest commit

 

History

78 Commits
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 

Repository files navigation

Deep-Representations-and-Learning

This is a repo for Labs prepared for COMP0188 taught at University Collge London (UCL).

Dataset Download instructions
Dataset is uploaded to moodle please download them to access the files and for the lab exercise please upload it to the google colab on your user name. For lab exercises the dataset in the form of .csv can be copied to a directory csv, and the .jpg files can be copied to images directory in your respective google drive for convenience.

How to structure your project
Now that you’ve had a chance to configure your development environment, take a second now and download the code and datasets associated with the course from the corresponding week uploaded on moodle.

|--- Colab Notebooks
| |--- csv
| |--- images
| |--- npy
| |--- h5
| |--- npz
|Comp0188_Lab2.ipynb

Each week (that includes accompanying code and dataset) has its own directory. Each directory then includes: • The source code for each week.

Advantages of organised code and datasets • Keeps the learning by week using project structure neat and tidy • Allows you to reuse datasets across multiple projects.

About

This is a repo for Labs prepared for COMP0188 taught at UCL.

Resources

Stars

Watchers

Forks

Releases

No releases published

Packages

No packages published

Languages

  • Jupyter Notebook 98.9%
  • Python 1.1%