CS5242_2021

Neural Networks and Deep Learning, NUS CS5242, 2021

Cloud Machine #1 : Google Colab (Free GPU)

Follow this Notebook installation :
https://colab.research.google.com/github/xbresson/CS5242_2021/blob/master/codes/installation/installation.ipynb
Open your Google Drive :
https://www.google.com/drive
Open in Google Drive Folder 'CS5242_2021' and go to Folder 'CS5242_2021/codes/'
Select the notebook 'file.ipynb' and open it with Google Colab using Control Click + Open With Colaboratory

Cloud Machine #2 : Binder (No GPU)

Simply click here

Local Installation for OSX & Linux

Open a Terminal and type

   # Conda installation
   curl https://repo.continuum.io/miniconda/Miniconda3-latest-Linux-x86_64.sh -o miniconda.sh -J -L -k # Linux
   curl https://repo.continuum.io/miniconda/Miniconda3-latest-MacOSX-x86_64.sh -o miniconda.sh -J -L -k # OSX
   chmod +x miniconda.sh
   ./miniconda.sh
   source ~/.bashrc

   # Clone GitHub repo
   git clone https://github.com/xbresson/CS5242_2021.git
   cd CS5242_2021

   # Install python libraries
   conda env create -f environment.yml
   source activate deeplearn_course

   # Run the notebooks
   jupyter notebook

Local Installation for Windows

   # Install Anaconda 
   https://repo.anaconda.com/miniconda/Miniconda3-latest-Windows-x86_64.exe

   # Open an Anaconda Terminal 
   Go to Application => Anaconda3 => Anaconda Prompt 

   # Install git : Type in terminal
   conda install git 

   # Clone GitHub repo
   git clone https://github.com/xbresson/CS5242_2021.git
   cd CS5242_2021

   # Install python libraries
   conda env create -f environment_windows.yml
   conda activate deeplearn_course

   # Run the notebooks
   jupyter notebook

CS5242_2021 - Neural Networks and Deep Learning, NUS CS5242, 2021

Related tags

Overview

CS5242_2021

Cloud Machine #1 : Google Colab (Free GPU)

Cloud Machine #2 : Binder (No GPU)

Local Installation for OSX & Linux

Local Installation for Windows

Owner

Xavier Bresson

Implementation of parameterized soft-exponential activation function.

[NeurIPS'20] Multiscale Deep Equilibrium Models

AdvStyle - Official PyTorch Implementation

Code for Paper Predicting Osteoarthritis Progression via Unsupervised Adversarial Representation Learning

PyTorch implementation of Train Short, Test Long: Attention with Linear Biases Enables Input Length Extrapolation.

A Pytorch reproduction of Range Loss, which is proposed in paper 《Range Loss for Deep Face Recognition with Long-Tailed Training Data》

Code for the paper One Thing One Click: A Self-Training Approach for Weakly Supervised 3D Semantic Segmentation, CVPR 2021.

An implementation of quantum convolutional neural network with MindQuantum. Huawei, classifying MNIST dataset

Keras Image Embeddings using Contrastive Loss

ScaleNet: A Shallow Architecture for Scale Estimation

MIMIC Code Repository: Code shared by the research community for the MIMIC-III database

Code release for NeurIPS 2020 paper "Co-Tuning for Transfer Learning"

Implementation of CrossViT: Cross-Attention Multi-Scale Vision Transformer for Image Classification

Code for "NeuralRecon: Real-Time Coherent 3D Reconstruction from Monocular Video", CVPR 2021 oral

I have created this Virtual Paint Program, in this you can paint(draw) on your screen using hand gestures, created in Python-3 using OpenCV and Mediapipe library. Gestures :- Index Finger for drawing and Index+Middle Finger for changing position and objects.

2021 CCF BDCI 全国信息检索挑战杯（CCIR-Cup）智能人机交互自然语言理解赛道第二名参赛解决方案

Faster RCNN with PyTorch

Datasets and source code for our paper Webly Supervised Fine-Grained Recognition: Benchmark Datasets and An Approach

Chainer implementation of recent GAN variants

LV-BERT: Exploiting Layer Variety for BERT (Findings of ACL 2021)