Aim of this Repository: From Perceptron model to Deep Neural Network (from scratch) in Python.
** Currently working on a basic Neural network model.
Reference for the Perceptron model: https://www.youtube.com/watch?v=ntKn5TPHHAk&list=PLRqwX-V7Uu6aCibgK1PTWWu9by6XFdCfh&index=3&t=217s
fastquant ๐ค Bringing backtesting to the mainstream fastquant allows you to easily backtest investment strategies with as few as 3 lines of python cod
Introduction This repository is for X-Linear Attention Networks for Image Captioning (CVPR 2020). The original paper can be found here. Please cite wi
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MagFace MagFace: A Universal Representation for Face Recognition and Quality Assessment in IEEE Conference on Computer Vision and Pattern Recognition
Contrastive Explanations for Model Interpretability This is the repository for the paper "Contrastive Explanations for Model Interpretability", about
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Ranking Models in Unlabeled New Environments Prerequisites This code uses the following libraries Python 3.7 NumPy PyTorch 1.7.0 + torchivision 0.8.1
You Only Group Once: Efficient Point-Cloud Processing with Token Representation and Relation Inference Module By Chenfeng Xu, Bohan Zhai, Bichen Wu, T
Deformable Neural Radiance Fields This is the code for Deformable Neural Radiance Fields, a.k.a. Nerfies. Project Page Paper Video This codebase conta
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Barlow Twins and HSIC Unofficial Pytorch implementation for Barlow Twins and HSIC_SSL on small datasets (CIFAR10, STL10, and Tiny ImageNet). Correspon
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์์จ ์ฃผํ์ฐจ์ ์์ ๊ธฐ๋ฐ ์ฐจ๊ฐ๊ฑฐ๋ฆฌ ์ ์ง ๊ฐ๋ฐ Table of Contents ํ๋ก์ ํธ ์๊ฐ ์ฃผ์ ๊ธฐ๋ฅ ์์คํ ๊ตฌ์กฐ ๋๋ ํ ๋ฆฌ ๊ตฌ์กฐ ๊ฒฐ๊ณผ ์คํ ๋ฐฉ๋ฒ ์ฐธ์กฐ ํ์ ํ๋ก์ ํธ ์๊ฐ YOLO-v5 ๊ธฐ๋ฐ์ผ๋ก ๋จ์ ์นด๋ฉ๋ผ์ ์์์ ํ์ฉํด ์ฐจ๊ฐ ๊ฑฐ๋ฆฌ๋ฅผ ์ผ์ ํ๊ฒ ์ ์งํ๋ฉฐ ์ฃผํํ๋ Adap
ONNX-GLPDepth - Python scripts for performing monocular depth estimation using the GLPDepth model in ONNX
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d3rlpy: An offline deep reinforcement learning library d3rlpy is an offline deep reinforcement learning library for practitioners and researchers. imp
AugMax: Adversarial Composition of Random Augmentations for Robust Training Haotao Wang, Chaowei Xiao, Jean Kossaifi, Zhiding Yu, Anima Anandkumar, an
Kaggle-titanic This is a tutorial in an IPython Notebook for the Kaggle competition, Titanic Machine Learning From Disaster. The goal of this reposito
MicroNet: Improving Image Recognition with Extremely Low FLOPs (ICCV 2021) A pytorch implementation of MicroNet. If you use this code in your research
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