6370 Open source projects that are alternatives of or similar to Pratik Derin Ogrenme Uygulamalari

Google Street View House Number(SVHN) Dataset, and classifying them through CNN

PyTorch tutorials A to Z

Try out deep learning models online on Google Colab

Convolutional Neural Network for German Traffic Sign Recognition Benchmark

🏥 Visualizing Convolutional Networks for MRI-based Diagnosis of Alzheimer’s Disease

Implementation of ews weight constraint mentioned in recent Deep Mind paper: http://www.pnas.org/content/early/2017/03/13/1611835114.full.pdf

Text classification models. Used a submodule for other projects.

Visualisations for Convolutional Neural Networks in Pytorch

Datasets, tools, and benchmarks for representation learning of code.

The deeplearning algorithms implemented by tensorflow

PyTorch implementation of SIFT descriptor

Exercise notebooks for CVND.

Code for Tensorflow Machine Learning Cookbook

QA - Answer Selection (Rank candidate answers for a given question)

Implementation of Neural Image Captioning model using Keras with Theano backend

This toolbox is support material for the book on CNN (http://www.convolution.network).

Face identification with cnn+triplet-loss written by Keras.

A small, interpretable codebase containing the re-implementation of a few "deep" NLP models in PyTorch. Colab notebooks to run with GPUs. Models: word2vec, CNNs, transformer, gpt.

All about Neural Networks!

A tutorial on how to implement models for part-of-speech tagging using PyTorch and TorchText.

PyTorch tutorial for learners

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）

[5 FPS - 150 FPS] Learning Deep Features for One-Class Classification (AnomalyDetection). Corresponds RaspberryPi3. Convert to Tensorflow, ONNX, Caffe, PyTorch. Implementation by Python + OpenVINO/Tensorflow Lite.

A PyTorch implementation of "DGC-Net: Dense Geometric Correspondence Network"

텐서플로우를 기초부터 응용까지 단계별로 연습할 수 있는 소스 코드를 제공합니다

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

Early stopping for PyTorch

Tutorial for video classification/ action recognition using 3D CNN/ CNN+RNN on UCF101

Some interesting TensorFlow tutorials for beginners.

深度学习入门教程, 优秀文章, Deep Learning Tutorial

Solution to Kaggle - Web Traffic Time Series Forecasting

Service Classification based on Service Description

A deep learning based gaze estimation framework implemented with PyTorch

Music recommender using deep learning with Keras and TensorFlow

keras를 이용한 딥러닝 기초 학습

Diseases Detection from NIH Chest X-ray data

The implementation of Relativistic average GAN with Keras

Embedded Graph Convolutional Neural Networks (EGCNN) in TensorFlow

Keras implementation of "One pixel attack for fooling deep neural networks" using differential evolution on Cifar10 and ImageNet

Deep Learning From Scratch

Predict Cryptocurrency Price with Deep Learning

CNN for intent classification task in a Chatbot

Tutorial for End-to-end Sequence Labeling via Bi-directional LSTM-CNNs-CRF

A re-implementation of mtcnn. Joint training, tutorial and deployment together.

Convolutional Neural Network for Multi-label Multi-instance Relation Extraction in Tensorflow

A End to End CNN Model which predicts the steering wheel angle based on the video/image

DLTK Model Zoo

Object classification with CIFAR-10 using transfer learning

Try to use tf.estimator and tf.data together to train a cnn model.

Using Keras and cats to visualize layers from CNNs

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.

코딩셰프의 3분 딥러닝, 케라스맛

CNN image classifier implemented in Keras Notebook 🖼️.

D2-Net: A Trainable CNN for Joint Description and Detection of Local Features

food image to recipe with deep convolutional neural networks.

Real-time Number Recognition using Apple's CoreML 2.0 and MNIST -

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

lidc nodule detection with CNN and LSTM network

Deep learning codes and projects using Python