Code for the AI lab course 2021/2022 of the University of Verona

Related tags

Deep LearningAI-Lab
Overview

AI-Lab

Code for the AI lab course 2021/2022 of the University of Verona

Set-Up the environment for the curse

  1. Download Anaconda for your System.

  2. Install Anaconda

    • On Linux/Mac
      • Use sh Anacaonda{version}.sh to install.
      • Add it to the PATH during installation if you’re ok with it:
        • First export PATH=~/anaconda3/bin:$PATH
        • Then source ~/.bashrc
      • sudo apt-get install git (may be required).
    • On Windows
      • Double click the installer to launch.
      • NB: during the installation, ensure to install "Anaconda Prompt" and use it for the other steps.
  3. Set-Up conda environment:

Using the Notebook

To start the environment and work on your assignments, navigate to the downloaded folder root (AI-Lab) and run:

source ~/anaconda3/etc/profile.d/conda.sh (may be required on linux).
conda activate ai-lab
jupyter notebook

The last command will open your browser for you to start working. To open the tutorial navigate with your browser to the current lesson notebook (Lesson_*/lesson_*_problem.ipynb).

Authors

Acknowledgments

Environments are based on OpenAI Gym: https://github.com/openai/gym

Owner
Davide Corsi
Davide Corsi
This repository contains the database and code used in the paper Embedding Arithmetic for Text-driven Image Transformation

This repository contains the database and code used in the paper Embedding Arithmetic for Text-driven Image Transformation (Guillaume Couairon, Holger

Meta Research 31 Oct 17, 2022
Complex Answer Generation For Conversational Search Systems.

Complex Answer Generation For Conversational Search Systems. Code for Does Structure Matter? Leveraging Data-to-Text Generation for Answering Complex

Hanane Djeddal 0 Dec 06, 2021
Official PyTorch code for Mutual Affine Network for Spatially Variant Kernel Estimation in Blind Image Super-Resolution (MANet, ICCV2021)

Mutual Affine Network for Spatially Variant Kernel Estimation in Blind Image Super-Resolution (MANet, ICCV2021) This repository is the official PyTorc

Jingyun Liang 139 Dec 29, 2022
ML-Decoder: Scalable and Versatile Classification Head

ML-Decoder: Scalable and Versatile Classification Head Paper Official PyTorch Implementation Tal Ridnik, Gilad Sharir, Avi Ben-Cohen, Emanuel Ben-Baru

189 Jan 04, 2023
An energy estimator for eyeriss-like DNN hardware accelerator

Energy-Estimator-for-Eyeriss-like-Architecture- An energy estimator for eyeriss-like DNN hardware accelerator This is an energy estimator for eyeriss-

HEXIN BAO 2 Mar 26, 2022
Simple Text-Generator with OpenAI gpt-2 Pytorch Implementation

GPT2-Pytorch with Text-Generator Better Language Models and Their Implications Our model, called GPT-2 (a successor to GPT), was trained simply to pre

Tae-Hwan Jung 775 Jan 08, 2023
Writeups for the challenges from DownUnderCTF 2021

cloud Challenge Author Difficulty Release Round Bad Bucket Blue Alder easy round 1 Not as Bad Bucket Blue Alder easy round 1 Lost n Found Blue Alder m

DownUnderCTF 161 Dec 31, 2022
Mercury: easily convert Python notebook to web app and share with others

Mercury Share your Python notebooks with others Easily convert your Python notebooks into interactive web apps by adding parameters in YAML. Simply ad

MLJAR 2.2k Dec 27, 2022
Build Low Code Automated Tensorflow, What-IF explainable models in just 3 lines of code.

Build Low Code Automated Tensorflow explainable models in just 3 lines of code.

Hasan Rafiq 170 Dec 26, 2022
Unified tracking framework with a single appearance model

Paper: Do different tracking tasks require different appearance model? [ArXiv] (comming soon) [Project Page] (comming soon) UniTrack is a simple and U

ZhongdaoWang 300 Dec 24, 2022
DSL for matching Python ASTs

py-ast-rule-engine This library provides a DSL (domain-specific language) to match a pattern inside a Python AST (abstract syntax tree). The library i

1 Dec 18, 2021
Differentiable Annealed Importance Sampling (DAIS)

Differentiable Annealed Importance Sampling (DAIS) This repository contains the code to reproduce the DAIS results from the paper Differentiable Annea

Guodong Zhang 6 Dec 26, 2021
A semismooth Newton method for elliptic PDE-constrained optimization

sNewton4PDEOpt The Python module implements a semismooth Newton method for solving finite-element discretizations of the strongly convex, linear ellip

2 Dec 08, 2022
Sign Language is detected in realtime using video sequences. Our approach involves MediaPipe Holistic for keypoints extraction and LSTM Model for prediction.

RealTime Sign Language Detection using Action Recognition Approach Real-Time Sign Language is commonly predicted using models whose architecture consi

Rishikesh S 15 Aug 20, 2022
Matthew Colbrook 1 Apr 08, 2022
Code for "Neural 3D Scene Reconstruction with the Manhattan-world Assumption" CVPR 2022 Oral

News 05/10/2022 To make the comparison on ScanNet easier, we provide all quantitative and qualitative results of baselines here, including COLMAP, COL

ZJU3DV 365 Dec 30, 2022
The code for our paper Semi-Supervised Learning with Multi-Head Co-Training

Semi-Supervised Learning with Multi-Head Co-Training (PyTorch) Abstract Co-training, extended from self-training, is one of the frameworks for semi-su

cmc 6 Dec 04, 2022
[ICCV21] Official implementation of the "Social NCE: Contrastive Learning of Socially-aware Motion Representations" in PyTorch.

Social-NCE + CrowdNav Website | Paper | Video | Social NCE + Trajectron | Social NCE + STGCNN This is an official implementation for Social NCE: Contr

VITA lab at EPFL 125 Dec 23, 2022
Highly comparative time-series analysis

〰️ hctsa 〰️ : highly comparative time-series analysis hctsa is a software package for running highly comparative time-series analysis using Matlab (fu

Ben Fulcher 569 Dec 21, 2022
Tiny Kinetics-400 for test

Kinetics-400迷你数据集 English | 简体中文 该数据集旨在解决的问题:参照Kinetics-400数据格式,训练基于自己数据的视频理解模型。 数据集介绍 Kinetics-400是视频领域benchmark常用数据集,详细介绍可以参考其官方网站Kinetics。整个数据集包含40

38 Jan 06, 2023