Machine Learning
Today, Let me share a list of website related to Machine Learning.This list is created by referring to dhilipsiva@gmail.com 's e-mails to BangML meetup group. Acknowledged.
- CycleGAN: Software that generates photos from paintings, turns horses into zebras, performs style transfer, and more (from UC Berkeley): https://github.com/junyanz/CycleGAN
- weld: a runtime and language for accelerating data analytics frameworks: https://github.com/weld-project/weld
- deform-conv: D eformable Convolution in TensorFlow / Keras: https://github.com/felixlaumon/deform-conv
- sact: Spatially Adaptive Computation Time for Residual Networks: https://github.com/mfigurnov/sact
- CLR: Cyclical Learning Rate: https://github.com/bckenstler/CLR
- seq2seq: A general-purpose encoder-decoder framework for Tensorflow: https://github.com/google/seq2seq
- deep-photo-styletransfer: Code and data for paper "Deep Photo Style Transfer": https://github.com/luanfujun/deep-photo-styletransfer
- DiscoGAN-pytorch: PyTorch implementation of "Learning to Discover Cross-Domain Relations with Generative Adversarial Networks": https://github.com/carpedm20/DiscoGAN-pytorch
- evolution-strategies-starter: Starter code for Evolution Strategies: https://github.com/openai/evolution-strategies-starter
- AdaIN-style: Arbitrary Style Transfer in Real-time with Adaptive Instance Normalization: https://github.com/xunhuang1995/AdaIN-style
- TC-Bot: User Simulation for Task-Completion Dialogues: https://github.com/MiuLab/TC-Bot
- e2e-model-learning: Task-based end-to-end model learning: https://github.com/locuslab/e2e-model-learning
- csgm: Code to reproduce results from the paper: "Compressed Sensing using Generative Models": https://github.com/AshishBora/csgm
- rnnprop: RNNprop: https://github.com/vfleaking/rnnprop
- DeepSketchHashing: Deep Sketch Hashing: https://github.com/ymcidence/DeepSketchHashing
- tensorflow/models: Learning to Remember Rare Events: https://github.com/tensorflow/models/tree/master/learning_to_remember_rare_events
- ORB_SLAM2: Real-Time SLAM for Monocular, Stereo and RGB-D Cameras, with Loop Detection and Relocalization Capabilities: https://github.com/raulmur/ORB_SLAM2
- rllab: is a framework for developing and evaluating reinforcement learning algorithms, fully compatible with OpenAI Gym: https://github.com/openai/rllab
- voxnet: 3D/Volumetric Convolutional Neural Networks with Theano+Lasagne: https://github.com/dimatura/voxnet
- dynamics: A Compositional Object-Based Approach to Learning Physical Dynamics: https://github.com/mbchang/dynamics
- CommAI-env - A platform for developing AI systems as described in A Roadmap towards Machine Intelligence: https://github.com/facebookresearch/CommAI-env
- rwa - Machine Learning on Sequential Data Using a Recurrent Weighted Average: https://github.com/jostmey/rwa
- MazeBase - Simple environment for creating very simple 2D games and training neural network models to perform tasks within them: https://github.com/facebook/MazeBase
- ontology - The Audio Set Ontology aims to provide a comprehensive set of categories to describe sound events: https://github.com/audioset/ontology
- mv3d - Multi-view 3D Models from Single Images with a Convolutional Network: https://github.com/mtatarchenko/mv3d
- universal - Universal adversarial perturbations: https://github.com/LTS4/universal
- prophet - Tool for producing high quality forecasts for time series data that has multiple seasonality with linear or non-linear growth: https://github.com/facebookincubator/prophet
- faiss - A library for efficient similarity search and clustering of dense vectors: https://github.com/facebookresearch/faiss
- CausalImpact - An R package for causal inference in time series: https://github.com/google/CausalImpact
- GPflow - Gaussian processes in TensorFlow: https://github.com/GPflow/GPflow/
- optnet - Differentiable Optimization as a Layer in Neural Networks: https://github.com/locuslab/optnet
- RNN-based generative models for speech: https://github.com/sotelo/parrot
- An SSBM player based on Deep Reinforcement Learning: https://github.com/vladfi1/phillip
- Shake-Shake regularization of 3-branch residual networks: https://github.com/xgastaldi/shake-shake
- Benchmarking State-of-the-Art Deep Learning Software Tools: https://github.com/hclhkbu/dlbench
- Public repo for helpful scripts when using the YouTube Bounding Boxes dataset: https://github.com/mbuckler/youtube-bb
- AirSim - Open source simulator based on Unreal Engine for autonomous vehicles from Microsoft AI & Research: https://github.com/Microsoft/AirSim
- dlib - A toolkit for making real world machine learning and data analysis applications in C++: https://github.com/davisking/dlib/
- Cyclades - This repository contains code for Cyclades, a general framework for parallelizing stochastic optimization algorithms in a shared memory setting: https://github.com/amplab/cyclades
- Deep learning with dynamic computation graphs in TensorFlow: https://github.com/tensorflow/fold
- Starter code for working with the YouTube-8M dataset: https://github.com/google/youtube-8m
- Vid2speech: Speech Reconstruction from Silent Video: https://github.com/arielephrat/vid2speech
- PatchBatch - a Batch Augmented Loss for Optical Flow: https://github.com/DediGadot/PatchBatch
- Wasserstein GAN: https://github.com/martinarjovsky/WassersteinGAN
- Keras Implementation of Google's Inception-V4 Architecture: https://github.com/kentsommer/keras-inceptionV4
- Implementation of Meta-RL A3C algorithm: https://github.com/awjuliani/Meta-RL
- DeepPose implementation on TensorFlow: https://github.com/asanakoy/deeppose_tf
- Exact Soft Confidence-Weighted Learning: https://github.com/IshitaTakeshi/SoftConfidenceWeighted.jl
- DyNet: The Dynamic Neural Network Toolkit: https://github.com/clab/dynet
- ENet Training: https://github.com/e-lab/ENet-training
- Generic Foreground Segmentation in Images: https://github.com/suyogduttjain/pixelobjectness
- Plexus - Interactive Emotion Visualization based on Social Media: https://github.com/xavierwu2016/plexus
- Code for Residual and Plain Convolutional Neural Networks for 3D Brain MRI Classification paper: https://github.com/neuro-ml/resnet_cnn_mri_adni
- Image Compression with Neural Networks: https://github.com/tensorflow/models/tree/master/compression
- A library for probabilistic modeling, inference, and criticism. Deep generative models, variational inference: https://github.com/blei-lab/edward
- KOC University deep learning framework: https://github.com/denizyuret/Knet.jl
- Domain Transfer Network. Tensorflow implementation of unsupervised cross-domain image generation: https://github.com/yunjey/dtn-tensorflow
- Improving Convolutional Networks via Attention Transfer: https://github.com/szagoruyko/attention-transfer
- Open-Source Neural Machine Translation in Torch: https://github.com/OpenNMT/OpenNMT
- Texture Networks: Feed-forward Synthesis of Textures and Stylized Images: https://github.com/DmitryUlyanov/texture_nets
- The Predictron: End-To-End Learning and Planning: https://github.com/zhongwen/predictron
- Deluge Networks: https://github.com/xternalz/DelugeNets
- Learning from Simulated and Unsupervised Images through Adversarial Training (TensorFlow): https://github.com/carpedm20/simulated-unsupervised-tensorflow
- FastMask: Segment Multi-scale Object Candidates in One Shot: https://github.com/voidrank/FastMask
- Visual7W visual question answering models: https://github.com/yukezhu/visual7w-qa-models
- Language Modeling with Gated Convolutional Networks: https://github.com/DingKe/nn_playground/tree/master/gcnn
- Semantic JPEG image compression using deep convolutional neural network (CNN): https://github.com/iamaaditya/image-compression-cnn
- TextBoxes: A Fast Text Detector with a Single Deep Neural Network: https://github.com/MhLiao/TextBoxes
- Implementation of video captioning from the paper "Temporal Tessellation for Video Annotation and Summarization": https://github.com/dot27/temporal-tessellation
- TFLearn - Deep learning library featuring a higher-level API for TensorFlow: http://tflearn.org
5 comments:
http://www.practicalcryptography.com/miscellaneous/machine-learning/
https://github.com/terryum/awesome-deep-learning-papers
Open Data for Deep Learning & Machine Learning : https://deeplearning4j.org/opendata
https://www.reddit.com/r/MachineLearning/
https://www.topbots.com/most-important-ai-research-papers-2018/
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