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
Training a Resilient Q-Network against Observational Interference, Causal Inference Q-Networks

Obs-Causal-Q-Network AAAI 2022 - Training a Resilient Q-Network against Observational Interference Preprint | Slides | Colab Demo | Environment Setup

23 Nov 21, 2022
Shōgun

The SHOGUN machine learning toolbox Unified and efficient Machine Learning since 1999. Latest release: Cite Shogun: Develop branch build status: Donat

Shōgun ML 2.9k Jan 04, 2023
Convert human motion from video to .bvh

video_to_bvh Convert human motion from video to .bvh with Google Colab Usage 1. Open video_to_bvh.ipynb in Google Colab Go to https://colab.research.g

Dene 306 Dec 10, 2022
This is the official code for the paper "Ad2Attack: Adaptive Adversarial Attack for Real-Time UAV Tracking".

Ad^2Attack：Adaptive Adversarial Attack on Real-Time UAV Tracking Demo video 📹 Our video on bilibili demonstrates the test results of Ad^2Attack on se

Intelligent Vision for Robotics in Complex Environment 10 Nov 07, 2022
Accurate 3D Face Reconstruction with Weakly-Supervised Learning: From Single Image to Image Set (CVPRW 2019). A PyTorch implementation.

Accurate 3D Face Reconstruction with Weakly-Supervised Learning: From Single Image to Image Set —— PyTorch implementation This is an unofficial offici

Sicheng Xu 833 Dec 28, 2022
InsCLR: Improving Instance Retrieval with Self-Supervision

InsCLR: Improving Instance Retrieval with Self-Supervision This is an official PyTorch implementation of the InsCLR paper. Download Dataset Dataset Im

Zelu Deng 25 Aug 30, 2022
A PyTorch implementation of QANet.

QANet-pytorch NOTICE I'm very busy these months. I'll return to this repo in about 10 days. Introduction An implementation of QANet with PyTorch. Any

H. Z. 343 Nov 03, 2022
Neural Module Network for VQA in Pytorch

Neural Module Network (NMN) for VQA in Pytorch Note: This is NOT an official repository for Neural Module Networks. NMN is a network that is assembled

Harsh Trivedi 111 Nov 24, 2022
CURL: Contrastive Unsupervised Representations for Reinforcement Learning

CURL Rainbow Status: Archive (code is provided as-is, no updates expected) This is an implementation of CURL: Contrastive Unsupervised Representations

Aravind Srinivas 46 Dec 12, 2022
🚀 PyTorch Implementation of "Progressive Distillation for Fast Sampling of Diffusion Models(v-diffusion)"

PyTorch Implementation of "Progressive Distillation for Fast Sampling of Diffusion Models(v-diffusion)" Unofficial PyTorch Implementation of Progressi

Vitaliy Hramchenko 58 Dec 19, 2022
DeepGNN is a framework for training machine learning models on large scale graph data.

DeepGNN Overview DeepGNN is a framework for training machine learning models on large scale graph data. DeepGNN contains all the necessary features in

Microsoft 45 Jan 01, 2023
Markov Attention Models

Introduction This repo contains code for reproducing the results in the paper Graphical Models with Attention for Context-Specific Independence and an

Vicarious 0 Dec 09, 2021
Solving SMPL/MANO parameters from keypoint coordinates.

Minimal-IK A simple and naive inverse kinematics solver for MANO hand model, SMPL body model, and SMPL-H body+hand model. Briefly, given joint coordin

Yuxiao Zhou 305 Dec 30, 2022
TeST: Temporal-Stable Thresholding for Semi-supervised Learning

TeST: Temporal-Stable Thresholding for Semi-supervised Learning TeST Illustration Semi-supervised learning (SSL) offers an effective method for large-

Xiong Weiyu 1 Jul 14, 2022
PyTorch code for ICPR 2020 paper Future Urban Scene Generation Through Vehicle Synthesis

Future urban scene generation through vehicle synthesis This repository contains Pytorch code for the ICPR2020 paper "Future Urban Scene Generation Th

Alessandro Simoni 4 Oct 11, 2021
A Python parser that takes the content of a text file and then reads it into variables.

Text-File-Parser A Python parser that takes the content of a text file and then reads into variables. Input.text File 1. What is your ***? 1. 18 -

Kelvin 0 Jul 26, 2021
This is the code repository for the paper A hierarchical semantic segmentation framework for computer-vision-based bridge column damage detection

Bridge-damage-segmentation This is the code repository for the paper A hierarchical semantic segmentation framework for computer-vision-based bridge c

Jingxiao Liu 5 Dec 07, 2022
Pytorch Implementation of Continual Learning With Filter Atom Swapping (ICLR'22 Spolight) Paper

Continual Learning With Filter Atom Swapping Pytorch Implementation of Continual Learning With Filter Atom Swapping (ICLR'22 Spolight) Paper If find t

11 Aug 29, 2022
existing and custom freqtrade strategies supporting the new hyperstrategy format.

freqtrade-strategies Description Existing and self-developed strategies, rewritten to support the new HyperStrategy format from the freqtrade-develop

39 Aug 20, 2021
Deep Ensembling with No Overhead for either Training or Testing: The All-Round Blessings of Dynamic Sparsity

[ICLR 2022] Deep Ensembling with No Overhead for either Training or Testing: The All-Round Blessings of Dynamic Sparsity by Shiwei Liu, Tianlong Chen, Zahra Atashgahi, Xiaohan Chen, Ghada Sokar, Elen

VITA 18 Dec 31, 2022