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

Related tags

Deep LearningCS5242_2021
Overview

CS5242_2021

Neural Networks and Deep Learning, NUS CS5242, 2021



Cloud Machine #1 : Google Colab (Free GPU)



Cloud Machine #2 : Binder (No GPU)



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







Owner
Xavier Bresson
Xavier Bresson
GitHub Repository
This is the dataset and code release of the OpenRooms Dataset.

This is the dataset and code release of the OpenRooms Dataset.

Visual Intelligence Lab of UCSD 95 Jan 08, 2023
A framework for the elicitation, specification, formalization and understanding of requirements.

A framework for the elicitation, specification, formalization and understanding of requirements.

NASA - Software V&V 161 Jan 03, 2023
PyTorch implementation of some learning rate schedulers for deep learning researcher.

pytorch-lr-scheduler PyTorch implementation of some learning rate schedulers for deep learning researcher. Usage WarmupReduceLROnPlateauScheduler Visu

Soohwan Kim 59 Dec 08, 2022
A PyTorch Toolbox for Face Recognition

FaceX-Zoo FaceX-Zoo is a PyTorch toolbox for face recognition. It provides a training module with various supervisory heads and backbones towards stat

JDAI-CV 1.6k Jan 06, 2023
Born-Infeld (BI) for AI: Energy-Conserving Descent (ECD) for Optimization

Born-Infeld (BI) for AI: Energy-Conserving Descent (ECD) for Optimization This repository contains the code for the BBI optimizer, introduced in the p

G. Bruno De Luca 5 Sep 06, 2022
[ECE NTUA] 👁 Computer Vision - Lab Projects & Theoretical Problem Sets (2020-2021)

Computer Vision - NTUA (2020-2021) This repository hosts the lab projects and theoretical problem sets of the Computer Vision course held by ECE NTUA

Dimitris Dimos 6 Jul 21, 2022
[ICLR 2021] Is Attention Better Than Matrix Decomposition?

Enjoy-Hamburger 🍔 Official implementation of Hamburger, Is Attention Better Than Matrix Decomposition? (ICLR 2021) Under construction. Introduction T

Gsunshine 271 Dec 29, 2022
Framework for Spectral Clustering on the Sparse Coefficients of Learned Dictionaries

Dictionary Learning for Clustering on Hyperspectral Images Overview Framework for Spectral Clustering on the Sparse Coefficients of Learned Dictionari

Joshua Bruton 6 Oct 25, 2022
A Fast Monotone Rotating Shallow Water model

pyRSW A Fast Monotone Rotating Shallow Water model How fast? As fast as a sustained 2 Gflop/s per core on a 2.5 GHz cpu (or 2048 Gflop/s with 1024 cor

Guillaume Roullet 13 Sep 28, 2022
Group R-CNN for Point-based Weakly Semi-supervised Object Detection (CVPR2022)

Group R-CNN for Point-based Weakly Semi-supervised Object Detection (CVPR2022) By Shilong Zhang*, Zhuoran Yu*, Liyang Liu*, Xinjiang Wang, Aojun Zhou,

Shilong Zhang 129 Dec 24, 2022
MicroNet: Improving Image Recognition with Extremely Low FLOPs (ICCV 2021)

MicroNet: Improving Image Recognition with Extremely Low FLOPs (ICCV 2021) A pytorch implementation of MicroNet. If you use this code in your research

Yunsheng Li 293 Dec 28, 2022
95.47% on CIFAR10 with PyTorch

Train CIFAR10 with PyTorch I'm playing with PyTorch on the CIFAR10 dataset. Prerequisites Python 3.6+ PyTorch 1.0+ Training # Start training with: py

5k Dec 30, 2022
Translation-equivariant Image Quantizer for Bi-directional Image-Text Generation

Translation-equivariant Image Quantizer for Bi-directional Image-Text Generation Woncheol Shin1, Gyubok Lee1, Jiyoung Lee1, Joonseok Lee2,3, Edward Ch

Woncheol Shin 7 Sep 26, 2022
Implementation of ProteinBERT in Pytorch

ProteinBERT - Pytorch (wip) Implementation of ProteinBERT in Pytorch. Original Repository Install $ pip install protein-bert-pytorch Usage import torc

Phil Wang 92 Dec 25, 2022
Lung Pattern Classification for Interstitial Lung Diseases Using a Deep Convolutional Neural Network

ild-cnn This is supplementary material for the manuscript: "Lung Pattern Classification for Interstitial Lung Diseases Using a Deep Convolutional Neur

22 Nov 05, 2022
On-device wake word detection powered by deep learning.

Porcupine Made in Vancouver, Canada by Picovoice Porcupine is a highly-accurate and lightweight wake word engine. It enables building always-listening

Picovoice 2.8k Dec 29, 2022
A nutritional label for food for thought.

Lexiscore As a first effort in tackling the theme of information overload in content consumption, I've been working on the lexiscore: a nutritional la

Paul Bricman 34 Nov 08, 2022
McGill Physics Hackathon 2021: Reaction-Diffusion Models for the Generation of Biological Patterns

DiffuseAnimals: Reaction-Diffusion Models for the Generation of Biological Patterns Introduction Reaction-diffusion equations can be utilized in order

Austin Szuminsky 2 Mar 07, 2022
A highly efficient and modular implementation of Gaussian Processes in PyTorch

GPyTorch GPyTorch is a Gaussian process library implemented using PyTorch. GPyTorch is designed for creating scalable, flexible, and modular Gaussian

3k Jan 02, 2023
implementation of paper - You Only Learn One Representation: Unified Network for Multiple Tasks

YOLOR implementation of paper - You Only Learn One Representation: Unified Network for Multiple Tasks To reproduce the results in the paper, please us

Kin-Yiu, Wong 1.8k Jan 04, 2023