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This repository is an easy-to-run t-SNE visualization tool for your dataset of choice. It currently supports 2D and 3D plots as well as an optional original image overlay on top of the 2D points.

https://github.com/kevinzakka/tsne-vizTags | tsne-algorithm python3 fashion-mnist plotting |

Implementation | Python |

License | Public |

Platform | Windows Linux |

This is a multicore modification of Barnes-Hut t-SNE by L. Van der Maaten with python and Torch CFFI-based wrappers. This code also works faster than sklearn.TSNE on 1 core. Barnes-Hut t-SNE is done in two steps.

barnes-hut-tsne multicore py-bh-tsne tsneFashion-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.

mnist deep-learning benchmark machine-learning dataset computer-vision fashion fashion-mnist gan zalando convolutional-neural-networksJSAT is a library for quickly getting started with Machine Learning problems. It is developed in my free time, and made available for use under the GPL 3. Part of the library is for self education, as such - all code is self contained. JSAT has no external dependencies, and is pure Java. I also aim to make the library suitably fast for small to medium size problems. As such, much of the code supports parallel execution.If you want to use the bleeding edge, but don't want to bother building yourself, I recomend you look at jitpack.io. It can build a POM repo for you for any specific commit version. Click on "Commits" in the link and then click "get it" for the commit version you want.

machine-learning machine-learning-library machine-learning-algorithms svm tsne jsatGAN Playground lets you play around with Generative Adversarial Networks right in your browser. Currently, it contains three built-in datasets: MNIST, Fashion MNIST, and CIFAR-10. GAN Playground provides you the ability to set your models' hyperparameters and build up your discriminator and generator layer-by-layer. You can observe the network learn in real time as the generator produces more and more realistic images, or more likely, gets stuck in failure modes such as mode collapse.

generative-adversarial-network deep-learning machine-learning neural-network ganPyTorch deep learning project made easy. The code in this repo is an MNIST example of the template. Try python3 train.py -c config.json to run code.

Converts the MNIST database of handwritten digits to .bmp images The MNIST database you can find under http://yann.lecun.com/exdb/mnist/

All pull requests are welcome, make sure to follow the contribution guidelines when you submit pull request.

tensorflow tensorflow-tutorials mnist-classification mnist machine-learning android tensorflow-models machine-learning-android tensorflow-android tensorflow-model mnist-model deep-learning deep-neural-networks deeplearning deep-learning-tutorialto verify that everything is functioning properly on your machine. Please refer to the MNIST tutorial on how to prepare the MNIST dataset for the following example. The complete code for this example is located at examples/mnist/mnist.jl. See below for detailed documentation of other tutorials and user guide.

BreakoutDetection is an open-source R package that makes breakout detection simple and fast. The BreakoutDetection package can be used in wide variety of contexts. For example, detecting breakout in user engagement post an A/B test, detecting behavioral change, or for problems in econometrics, financial engineering, political and social sciences.The underlying algorithm – referred to as E-Divisive with Medians (EDM) – employs energy statistics to detect divergence in mean. Note that EDM can also be used detect change in distribution in a given time series. EDM uses robust statistical metrics, viz., median, and estimates the statistical significance of a breakout through a permutation test.

Visualize GitHub's most popular repos. http://www.donnemartin.com/viz

github visualization programmingWisp : Wisp Is Scala Plotting is a console-centric plotting library for scala. It focuses on existing web-based plotting libraries, and strives to bring the power and flexibility of web-based plotting tools to the scala console, while preserving an at-your-finger-tips feel readily found in matlab, R, and many other languages. Wisp is open source, and we hope to get involvement from the community. We'd love to get some pull requests. Also, even if you don't have a fix, feel free to report bugs or just request new features through the github issue tracker.

gnuplot is a command-driven interactive function plotting program. It can be used to plot functions and data points in both two- and three-dimensional plots in many different formats. It is designed primarily for the visual display of scientific data.

graphics chart visualization plotting-engineExample scripts for a deep, feed-forward neural network have been written from scratch. No machine learning packages are used, providing an example of how to implement the underlying algorithms of an artificial neural network. The code is written in the Julia, a programming language with a syntax similar to Matlab. The neural network is trained on the MNIST dataset of handwritten digits. On the test dataset, the neural network correctly classifies 98.42 % of the handwritten digits. The results are pretty good for a fully connected neural network that does not contain a priori knowledge about the geometric invariances of the dataset like a Convolutional Neural Network would.

Simple Tensorflow implementation of "Densenet" using Cifar10, MNIST

densenet tensorflow densenet-tensorflowSome examples require MNIST dataset for training and testing. Don't worry, this dataset will automatically be downloaded when running examples (with input_data.py). MNIST is a database of handwritten digits, for a quick description of that dataset, you can check this notebook.

recurrent-neural-networks convolutional-neural-networks deep-learning-tutorial tensorflow tensorlayer keras deep-reinforcement-learning tensorflow-tutorials deep-learning machine-learning notebook autoencoder multi-layer-perceptron reinforcement-learning tflearn neural-networks neural-network neural-machine-translation nlp cnnMNIST 예제를 CNN 모델로 학습하는 코드를 조금 보강하고 정리해서 TensorFlow-MNIST 저장소에 올려두었습니다. summary 를 저장해서 TensorBoard 를 사용할 수 있게 하였으며, 모델을 생성하는 부분과 Trainer, Tester 를 분리하여 학습한 모델을 저장 후 따로 사용할 수 있도록 해 두었으니 참고 해 주세요.

K-means implementation is based on "Yinyang K-Means: A Drop-In Replacement of the Classic K-Means with Consistent Speedup". While it introduces some overhead and many conditional clauses which are bad for CUDA, it still shows 1.6-2x speedup against the Lloyd algorithm. K-nearest neighbors employ the same triangle inequality idea and require precalculated centroids and cluster assignments, similar to the flattened ball tree. Technically, this project is a shared library which exports two functions defined in kmcuda.h: kmeans_cuda and knn_cuda. It has built-in Python3 and R native extension support, so you can from libKMCUDA import kmeans_cuda or dyn.load("libKMCUDA.so").

cuda kmeans yinyang knn-search machine-learning afk-mc2Python 中文数据结构和算法教程

python3 algorithm datastructuresThis repository contains implementations of basic machine learning algorithms in plain Python (Python Version 3.6+). All algorithms are implemented from scratch without using additional machine learning libraries. The intention of these notebooks is to provide a basic understanding of the algorithms and their underlying structure, not to provide the most efficient implementations. After several requests I started preparing notebooks on how to preprocess datasets for machine learning. Within the next months I will add one notebook for each kind of dataset (text, images, ...). As before, the intention of these notebooks is to provide a basic understanding of the preprocessing steps, not to provide the most efficient implementations.

machine-learning logistic-regression ipynb machine-learning-algorithms linear-regression perceptron python-implementations kmeans algorithm python3 neural-network k-nearest-neighbours k-nearest-neighbor k-nn neural-networksBinarytree is a Python library which provides a simple API to generate, visualize, inspect and manipulate binary trees. It allows you to skip the tedious work of setting up test data, and dive straight into practising your algorithms. Heaps and BSTs (binary search trees) are also supported. You may need to use sudo depending on your environment.

python3 python2 python-3 python-2 python-library binary-trees binary-tree interview-practice interview learning practise python-3-5 algorithm data-structures data-structure heap heaps bst binary-search-tree
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