Neural network library for digit classification powered by cuda
The library was built to work with MNIST dataset. python-mnist data parser was used to load the dataset.
The library uses Numba which supports CUDA-enabled GPUs with compute capability 2.0 or above with an up-to-date Nvidia driver. See the list of CUDA-enabled GPU cards
Trained weights are located in weights directory. Call load_weights method to use them.
Cement-Strength-Prediction Problem Statement To build a regression model to predict the concrete compressive strength based on the different features
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Tokyo2020-Pictogram-using-MediaPipe MediaPipeで姿勢推定を行い、Tokyo2020オリンピック風のピクトグラムを表示するデモです。 Tokyo2020Pictgram02.mp4 Requirement mediapipe 0.8.6 or later O
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Trading Using Q-Learning In this project, I will present an adaptive learning model to trade a single stock under the reinforcement learning framework
MagInkCal This repo contains the code needed to drive an E-Ink Magic Calendar that uses a battery powered (PiSugar2) Raspberry Pi Zero WH to retrieve
This repository provides an end-to-end library for automatic character rigging and blend shapes generation as well as a visualization tool. It is based on our work Learning Skeletal Articulations wit
SimplEx - Explaining Latent Representations with a Corpus of Examples Code Author: Jonathan Crabbé (
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cloob-training Pretrained models There are two pretrained CLOOB models in this repo at the moment, a 16 epoch and a 32 epoch ViT-B/16 checkpoint train
Grounding Representation Similarity with Statistical Testing This repo contains code to replicate the results in our paper, which evaluates representa
Video Frame Interpolation without Temporal Priors (NeurIPS2020) [Paper] [video] How to run Prerequisites NVIDIA GPU + CUDA 9.0 + CuDNN 7.6.5 Pytorch 1
PyMAF This repository contains the code for the following paper: 3D Human Pose and Shape Regression with Pyramidal Mesh Alignment Feedback Loop Hongwe
Image Denoising with Control over Deep Network Hallucination Paper and arXiv preprint -- Our frequency-domain insights derive from SFM and the concept
SGCN ⠀ A PyTorch implementation of Signed Graph Convolutional Network (ICDM 2018). Abstract Due to the fact much of today's data can be represented as
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