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
Resco: A simple python package that report the effect of deep residual learning

resco Description resco is a simple python package that report the effect of dee

Pierre-Arthur Claudé 1 Jun 28, 2022
A 3D sparse LBM solver implemented using Taichi

taichi_LBM3D Background Taichi_LBM3D is a 3D lattice Boltzmann solver with Multi-Relaxation-Time collision scheme and sparse storage structure impleme

Jianhui Yang 121 Jan 06, 2023
Riemann Noise Injection With PyTorch

Riemann Noise Injection - PyTorch A module for modeling GAN noise injection based on Riemann geometry, as described in Ruili Feng, Deli Zhao, and Zhen

2 May 27, 2022
A light weight data augmentation tool for training CNNs and Viola Jones detectors

hey-daug A light weight data augmentation tool for training CNNs and Viola Jones detectors (Haar Cascades). This tool inflates your data by up to six

Jaiyam Sharma 2 Nov 23, 2019
HINet: Half Instance Normalization Network for Image Restoration

HINet: Half Instance Normalization Network for Image Restoration Liangyu Chen, Xin Lu, Jie Zhang, Xiaojie Chu, Chengpeng Chen Paper: https://arxiv.org

303 Dec 31, 2022
Meta-Learning Sparse Implicit Neural Representations (NeurIPS 2021)

Meta-SparseINR Official PyTorch implementation of "Meta-learning Sparse Implicit Neural Representations" (NeurIPS 2021) by Jaeho Lee*, Jihoon Tack*, N

Jaeho Lee 41 Nov 10, 2022
The implementation of PEMP in paper "Prior-Enhanced Few-Shot Segmentation with Meta-Prototypes"

Prior-Enhanced network with Meta-Prototypes (PEMP) This is the PyTorch implementation of PEMP. Overview of PEMP Meta-Prototypes & Adaptive Prototypes

Jianwei ZHANG 8 Oct 14, 2021
Visualizing lattice vibration information from phonon dispersion to atoms (For GPUMD)

Phonon-Vibration-Viewer (For GPUMD) Visualizing lattice vibration information from phonon dispersion for primitive atoms. In this tutorial, we will in

Liangting 6 Dec 10, 2022
Official implementation of Densely connected normalizing flows

Densely connected normalizing flows This repository is the official implementation of NeurIPS 2021 paper Densely connected normalizing flows. Poster a

Matej Grcić 31 Dec 12, 2022
VQMIVC - Vector Quantization and Mutual Information-Based Unsupervised Speech Representation Disentanglement for One-shot Voice Conversion

VQMIVC: Vector Quantization and Mutual Information-Based Unsupervised Speech Representation Disentanglement for One-shot Voice Conversion (Interspeech

Disong Wang 262 Dec 31, 2022
Reproducing Results from A Hybrid Approach to Targeting Social Assistance

title author date output Reproducing Results from A Hybrid Approach to Targeting Social Assistance Lendie Follett and Heath Henderson 12/28/2021 html_

Lendie Follett 0 Jan 06, 2022
HEAM: High-Efficiency Approximate Multiplier Optimization for Deep Neural Networks

Approximate Multiplier by HEAM What's HEAM? HEAM is a general optimization method to generate high-efficiency approximate multipliers for specific app

4 Sep 11, 2022
Code release for "Conditional Adversarial Domain Adaptation" (NIPS 2018)

CDAN Code release for "Conditional Adversarial Domain Adaptation" (NIPS 2018) New version: https://github.com/thuml/Transfer-Learning-Library Dataset

THUML @ Tsinghua University 363 Dec 20, 2022
CTRL-C: Camera calibration TRansformer with Line-Classification

CTRL-C: Camera calibration TRansformer with Line-Classification This repository contains the official code and pretrained models for CTRL-C (Camera ca

57 Nov 14, 2022
ONNX-PackNet-SfM: Python scripts for performing monocular depth estimation using the PackNet-SfM model in ONNX

Python scripts for performing monocular depth estimation using the PackNet-SfM model in ONNX

Ibai Gorordo 14 Dec 09, 2022
Predicting a person's gender based on their weight and height

Logistic Regression Advanced Case Study Gender Classification: Predicting a person's gender based on their weight and height 1. Introduction We turn o

1 Feb 01, 2022
🛰️ Awesome Satellite Imagery Datasets

Awesome Satellite Imagery Datasets List of aerial and satellite imagery datasets with annotations for computer vision and deep learning. Newest datase

Christoph Rieke 3k Jan 03, 2023
CLIP: Connecting Text and Image (Learning Transferable Visual Models From Natural Language Supervision)

CLIP (Contrastive Language–Image Pre-training) Experiments (Evaluation) Model Dataset Acc (%) ViT-B/32 (Paper) CIFAR100 65.1 ViT-B/32 (Our) CIFAR100 6

Myeongjun Kim 52 Jan 07, 2023
Kindle is an easy model build package for PyTorch.

Kindle is an easy model build package for PyTorch. Building a deep learning model became so simple that almost all model can be made by copy and paste from other existing model codes. So why code? wh

Jongkuk Lim 77 Nov 11, 2022
Near-Optimal Sparse Allreduce for Distributed Deep Learning (published in PPoPP'22)

Near-Optimal Sparse Allreduce for Distributed Deep Learning (published in PPoPP'22) Ok-Topk is a scheme for distributed training with sparse gradients

Shigang Li 9 Oct 29, 2022