Displaying 1 to 20 from 34 results

fashion-mnist - A MNIST-like fashion product database. Benchmark :point_right:

  •    Python

Fashion-MNIST is a dataset of Zalando's article images—consisting of a training set of 60,000 examples and a test set of 10,000 examples. Each example is a 28x28 grayscale image, associated with a label from 10 classes. We intend Fashion-MNIST to serve as a direct drop-in replacement for the original MNIST dataset for benchmarking machine learning algorithms. It shares the same image size and structure of training and testing splits.

TensorFlow-Tutorials - 텐서플로우를 기초부터 응용까지 단계별로 연습할 수 있는 소스 코드를 제공합니다

  •    Python

텐서플로우를 기초부터 응용까지 단계별로 연습할 수 있는 소스 코드를 제공합니다. 텐서플로우 공식 사이트에서 제공하는 안내서의 대부분의 내용을 다루고 있으며, 공식 사이트에서 제공하는 소스 코드보다는 훨씬 간략하게 작성하였으므로 쉽게 개념을 익힐 수 있을 것 입니다. 또한, 모든 주석은 한글로(!) 되어 있습니다.

regl-cnn - Digit recognition with Convolutional Neural Networks in WebGL

  •    Javascript

GPU accelerated handwritten digit recognition with regl. Note that this network will probably be slower than the corresponding network implemented on the CPU. This is because of the overhead associated with transferring data to and from the GPU. But in the future we will attempt implementing more complex networks in the browser, such as Neural Style, and then we think that we will see a significant speedup compared to the CPU.




mnist-ruby-test - Handwritten digit OCR in Ruby

  •    Ruby

Testing classification of MNIST digits in Ruby. Includes a Sinatra app that uses a trained ruby-fann neural network to predict digits drawn on a element. The neural network was trained on all 60,000 training examples with 1 hidden layer of 300 neurons, and successfully classified ~93% of the test set.

fashion - The Fashion-MNIST dataset and machine learning models.

  •    R

Training AI machine learning models on the Fashion MNIST dataset. Fashion-MNIST is a dataset consisting of 70,000 images (60k training and 10k test) of clothing objects, such as shirts, pants, shoes, and more. Each example is a 28x28 grayscale image, associated with a label from 10 classes. The 10 classes are listed below.


tensorflow-infogan - :dolls: InfoGAN: Interpretable Representation Learning

  •    Python

This repository contains a straightforward implementation of Generative Adversarial Networks trained to fool a discriminator that sees real MNIST images, along with Mutual Information Generative Adversarial Networks (InfoGAN). Note: generator architecture changed w.r.t. the publication, due to the fact that it was producing 32x32 images rather than 64x64 images as desired. Results may be different.

rust-simple-nn - Simple neural network implementation in Rust

  •    Rust

Simple neural network implementation in Rust. NOTE: I wanted to give Rust a try, and decided to try implementing a simple NN framework, but this is not meant to be used in production (it is way too slow for now anyway).

tensorflow-cdcgan - A short Conditional DCGAN tensorflow implementation.

  •    Python

This is a short implementation of a Conditional DCGAN, however if you need a cDCGAN for real-world use cases, please consider using a more serious implementation. Here can be seen a cDCGAN trained on CIFAR-10 using the same networks architectures I used for MNIST, obviously it shows that we need to be careful when designing the architecture. It works better using more filters.

kaggle-for-fun - All my submissions for Kaggle contests that I have been, and going to be participating

  •    Python

All my submissions for Kaggle contests that I have been, and going to be participating. I will probably have everything written in Python (utilizing scikit-learn or similar libraries), but occasionally I might also use R or Haskell if I can.

MNIST-Sequence - A tool to generate image dataset for sequences of handwritten digits using MNIST database

  •    Python

The MNIST database is a large database of handwritten digits that is commonly used for training various image processing systems. The database is also widely used for training and testing in the field of machine learning. The goal of this project is to use the above database of handwritten digit images to generate images representing sequences of handwritten digits. The project also provides a utility to generate and save a set of training/test image dataset of MNIST sequences with labels.

chainer-ADDA - Adversarial Discriminative Domain Adaptation in Chainer

  •    Python

Implementation of Adversarial Discriminative Domain Adaptation in Chainer. Note this code depends on this version of Chainer (or newer). Please check out the source from that link rather than installing via pip.

CapsNet_Tensorflow

  •    Python

This repository contains different tests performed on a capsule network model. Example code to train the capsule_dynamic(CapsNet with dynamic routing) model on mnist dataset.

Kaggle-MNIST - Simple ConvNet to classify digits from the famous MNIST dataset

  •    Python

Simple ConvNet to classify digits from the famous MNIST dataset. This program gets 98.63% on Kaggle's test set. In order to run this program, you need to have Theano, Keras, and Numpy installed as well as the train and test datasets (from Kaggle) in the same folder as the python file.

MNIST-CoreML - Predict handwritten digits with CoreML

  •    Python

This is the MNIST dataset implemented in Apple's new framework CoreML. The MNIST dataset can predict handwritten (drawn) digits from an image and outputs a prediction from 0-9. The model was built with Keras 1.2.2. To test this model you can open the MNISTPrediction.xcodeproj and run it on your device (iOS 11 and Xcode 9 is required). To test further images just add them to the project and replace my testing with yours.

tensorbag - Collection of tensorflow notebooks tutorials for implementing the most important Deep Learning algorithms

  •    Jupyter

Tensorbag is a collection of tensorflow tutorial on different Deep Learning and Machine Learning algorithms. The tutorials are organised as jupyter notebooks and require tensorflow >= 1.5. There is a subset of notebooks identified with the tag [quiz] that directly ask to the reader to complete part of the code. In the same folder there is always a complementary notebook with the complete solution.





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