#Mnist
Showing 55 of 55 repositories tagged #mnist, ranked by stars
A MNIST-like fashion product database. Benchmark :point_down:
텐서플로우를 기초부터 응용까지 단계별로 연습할 수 있는 소스 코드를 제공합니다
GPU Accelerated t-SNE for CUDA with Python bindings
A PyTorch implementation of the NIPS 2017 paper "Dynamic Routing Between Capsules".
[pip install medmnist] 18x Standardized Datasets for 2D and 3D Biomedical Image Classification
Learn deep learning with tensorflow2.0, keras and python through this comprehensive deep learning tutorial series. Learn deep learning from scratch. Deep learning series for beginners. Tensorflow tutorials, tensorflow 2.0 tutorial. deep learning tutorial python.
Experiments for understanding disentanglement in VAE latent representations
A free audio dataset of spoken digits. An audio version of MNIST.
Variational Autoencoder and Conditional Variational Autoencoder on MNIST in PyTorch
Minimalist implementation of VQ-VAE in Pytorch
PyTorch implementation of NIPS 2017 paper Dynamic Routing Between Capsules
Android TensorFlow MachineLearning MNIST Example (Building Model with TensorFlow for Android)
Pytorch、Scikit-learn实现多种分类方法，包括逻辑回归（Logistic Regression）、多层感知机（MLP）、支持向量机（SVM）、K近邻（KNN）、CNN、RNN，极简代码适合新手小白入门，附英文实验报告（ACM模板）
A curated list of dedicated resources and applications
Pretrained GANs + VAEs + classifiers for MNIST/CIFAR in pytorch.
Convolutional Neural Network with CUDA (MNIST 99.23%)
C* (C-Asterisk) is a custom, high-performance programming language. It uses LLVM and native I/O bypasses to run Deep Learning models from scratch 100x faster than Python.
A lightweight deep learning training framework implemented from scratch in C++, featuring a PyTorch-style API.
A header-only neural network library for microcontrollers, with partial bare-metal & native-os support.
:dolls: InfoGAN: Interpretable Representation Learning
conditional variational autoencoder written in Keras [not actively maintained]
Comparison of Generative Models in Tensorflow
A repository for recording the machine learning code
Label Embedding Network
One-Shot Learning with Triplet CNNs in Pytorch
Real-time Number Recognition using Apple's CoreML 2.0 and MNIST -
ADAS is short for Adaptive Step Size, it's an optimizer that unlike other optimizers that just normalize the derivative, it fine-tunes the step size, truly making step size scheduling obsolete, achieving state-of-the-art training performance
Convolutional Neural Network RTL-level Design
Сustom torch style machine learning framework with automatic differentiation implemented on numpy, allows build GANs, VAEs, etc.
Amos optimizer with JEstimator lib.
A simple, easy to use MNIST loader written in Python 3
Generative Adversarial Networks in TensorFlow 2.0
An implementation for mnist center loss training and visualization
Official PyTorch implementation of "Visualizing the Decision-making Process in Deep Neural Decision Forest", CVPR 2019 Workshops on Explainable AI
My solution to TUM's Machine Learning MNIST challenge 2016-2017 [winner]
A fast Tsetlin Machine implementation employing bit-wise operators, with MNIST demo.
Comparing FC VAE / FCN VAE / PCA / UMAP on MNIST / FMNIST
A C++ implementation to create, visualize and train Convolutional Neural Networks
A look at some simple autoencoders for the Cifar10 dataset, including a denoising autoencoder. Python code included.
Google Street View House Number(SVHN) Dataset, and classifying them through CNN
simple implementation of "Improved Variational Inference with Inverse Autoregressive Flow" paper with pytorch
Implementation of key concepts of neuralnetwork via numpy
A Flask web app for handwritten digit recognition using machine learning
Handwritten digit recognizer using a feed-forward neural network and the MNIST dataset of 70,000 human-labeled handwritten digits.
All my submissions for Kaggle contests that I have been, and going to be participating.
Deep Neural Network from scratch in C++ for learning purposes
Basic TensorFlow mechanics, operations, class definitions, and neural networks building. Examples from deeplearning.ai Tensorflow course using Google Colab platform.
Implementation of a neural network from scratch in python.
Convolutional Neural Network in C (for educational purposes)
A high level deep learning library for Convolutional Neural Networks,GANs and more, made from scratch(numpy/cupy implementation).
Neural network library written in C and Javascript
A repository for recording the codes of machine learning algorithms
Recognize handwritten multi-digit numbers using a CRNN model trained with synthetic data.
#WORK IN PROGRESS PyTorch Implementation of Supervised and Deep Q-Learning EWC(Elastic Weight Consolidation), introduced in "Overcoming Catastrophic Forgetting in Neural Networks"
Hi! Thanks for checking out my tutorial where I walk you through the process of coding a convolutional neural network in java from scratch. After building a network for a university assignment, I decided to create a tutorial to (hopefully) help others do the same and improve my own understanding of neural networks.