145 Open source projects that are alternatives of or similar to gradient-boosted-decision-tree

Show how to perform fast retraining with LightGBM in different business cases

Tensorflow 2.0 implementation of Adversarial Autoencoders

Tensorflow Implementation of the paper [Neural Discrete Representation Learning](https://arxiv.org/abs/1711.00937) (VQ-VAE).

MNIST classification using Convolutional NeuralNetwork. Various techniques such as data augmentation, dropout, batchnormalization, etc are implemented.

A curated list of dedicated resources and applications

Recognize your own handwritten digits with Tensorflow, embedded in a PyQT5 GUI. The Neural Network was trained on MNIST.

Lingvo

Experiments on cluttered mnist dataset with Tensorflow.

Implementation of a digit recognition using my Neural Network with the MNIST data set.

Keras implementations of Generative Adversarial Networks. GANs, DCGAN, CGAN, CCGAN, WGAN and LSGAN models with MNIST and CIFAR-10 datasets.

CNN classification model built in Keras used for Digit Recognizer task on Kaggle (https://www.kaggle.com/c/digit-recognizer)

Faster, better, smarter ecological niche modeling and species distribution modeling

Variational Autoencoder and Conditional Variational Autoencoder on MNIST in PyTorch

PyTorch implementation of Conditional Deep Convolutional Generative Adversarial Networks (cDCGAN)

A scratch implementation of Convolutional Neural Network in Python using only numpy and validated over CIFAR-10 & MNIST Dataset

Tensorflow implementation of adversarial auto-encoder for MNIST

Generative Adversarial Network for MNIST with tensorflow

A fast Tsetlin Machine implementation employing bit-wise operators, with MNIST demo.

텐서플로우를 기초부터 응용까지 단계별로 연습할 수 있는 소스 코드를 제공합니다

Keras MNIST for Handwriting Detection

Tensorflow implementation of conditional variational auto-encoder for MNIST

Pytorch reimplementation for "Gradient Surgery for Multi-Task Learning"

A 100%-Julia implementation of Gradient-Boosting Regression Tree algorithms

A PyTorch implementation of the NIPS 2017 paper "Dynamic Routing Between Capsules".

TI-pooling: transformation-invariant pooling for feature learning in Convolutional Neural Networks

Generative Probabilistic Novelty Detection with Adversarial Autoencoders

🌳 Stacked Gradient Boosting Machines

Predict handwritten digits with CoreML

Multi-Classification on dataset of MNIST

A small convolution neural network deep learning framework implemented in c++.

The simplest machine learning library for launching UIs, running evaluations, and comparing model performance.

Simple Implementation of many GAN models with PyTorch.

A realtime digit OCR on the browser using Machine Learning

Minimalist implementation of VQ-VAE in Pytorch

Tensorflow-example：使用MNIST训练模型，并识别手写数字图片

Çeşitli kütüphaneler kullanılarak Türkçe kod açıklamalarıyla TEMEL SEVİYEDE pratik derin öğrenme uygulamaları.

A Pytorch implementation of "Deep Convolutional Generative Adversarial Networks"

MNIST with TensorFlow Lite on Android

Generative Adversarial Networks in TensorFlow 2.0

Collection of generative models in Pytorch version.

[ICML 2019, 20 min long talk] Robust Decision Trees Against Adversarial Examples

Non-negative Positive-Unlabeled (nnPU) and unbiased Positive-Unlabeled (uPU) learning reproductive code on MNIST and CIFAR10

This program shows how Bounding-Box-Regression works in a visual form. Intersection over Union ( IOU ), Non Maximum Suppression ( NMS ), Object detection, 边框回归，边框回归可视化，交并比，非极大值抑制，目标检测。

Tensorflow implementation of Generative Adversarial Networks (GAN) and Deep Convolutional Generative Adversarial Netwokrs for MNIST dataset.

MNIST classification using Multi-Layer Perceptron (MLP) with 2 hidden layers. Some weight-initializers and batch-normalization are implemented.

🎎 InfoGAN: Interpretable Representation Learning

Interactive exploration of MNIST variational autoencoder latent space with React and tensorflow.js.

This is a sample project demonstrating the use of Keras (Tensorflow) for the training of a MNIST model for handwriting recognition using CoreML on iOS 11 for inference.

Boosted trees in Julia

The Pytorch Implementation of L-Softmax

Playing with MNIST. Machine Learning. Generative Models.

Tensorflow implementation of conditional Generative Adversarial Networks (cGAN) and conditional Deep Convolutional Adversarial Networks (cDCGAN) for MANIST dataset.

One-Shot Learning with Triplet CNNs in Pytorch

In-Browser Digit recognition with Tensorflow.js and React using Mnist dataset

Handwritten digit recognizer using a feed-forward neural network and the MNIST dataset of 70,000 human-labeled handwritten digits.

Comparing FC VAE / FCN VAE / PCA / UMAP on MNIST / FMNIST

Implementation of LeNet5 without any auto-differentiate tools or deep learning frameworks. Accuracy of 98.6% is achieved on MNIST dataset.

CNN digit recognizer implemented in Keras Notebook, Kaggle/MNIST (0.995).

手写数字识别。利用Tensorflow和MNIST实现的一个简单的手写数字识别程序。