All Projects → VICO-UoE → mlpractical

VICO-UoE / mlpractical

Licence: other
Machine Learning Practical Course Code Repository

Programming Languages

Jupyter Notebook
11667 projects
python
139335 projects - #7 most used programming language
shell
77523 projects

Projects that are alternatives of or similar to mlpractical

Dandelion
A light weight deep learning framework, on top of Theano, offering better balance between flexibility and abstraction
Stars: ✭ 15 (-42.31%)
Mutual labels:  deep-learning-library
The Incredible Pytorch
The Incredible PyTorch: a curated list of tutorials, papers, projects, communities and more relating to PyTorch.
Stars: ✭ 8,584 (+32915.38%)
Mutual labels:  deep-learning-library
Lasagne
Lightweight library to build and train neural networks in Theano
Stars: ✭ 3,800 (+14515.38%)
Mutual labels:  deep-learning-library
InsNet
InsNet Runs Instance-dependent Neural Networks with Padding-free Dynamic Batching.
Stars: ✭ 58 (+123.08%)
Mutual labels:  deep-learning-library
nuts-ml
Flow-based data pre-processing for deep learning
Stars: ✭ 32 (+23.08%)
Mutual labels:  deep-learning-library

Machine Learning Practical

This repository contains the code for the University of Edinburgh School of Informatics course Machine Learning Practical.

This assignment-based course is focused on the implementation and evaluation of machine learning systems. Students who do this course will have experience in the design, implementation, training, and evaluation of machine learning systems.

The code in this repository is split into:

  • a Python package mlp, a NumPy based neural network package designed specifically for the course that students will implement parts of and extend during the course labs and assignments,
  • a series of Jupyter notebooks in the notebooks directory containing explanatory material and coding exercises to be completed during the course labs.

Remote working

If you are working remotely, follow this guide.

Getting set up

Detailed instructions for setting up a development environment for the course are given in this file. Students doing the course will spend part of the first lab getting their own environment set up.

Note that the project description data, including the texts, logos, images, and/or trademarks, for each open source project belongs to its rightful owner. If you wish to add or remove any projects, please contact us at [email protected].