sdhnshu / Pytorch Model Zoo
Licence: mit
A collection of deep learning models implemented in PyTorch
Stars: ✭ 125
Projects that are alternatives of or similar to Pytorch Model Zoo
Mit Deep Learning
Tutorials, assignments, and competitions for MIT Deep Learning related courses.
Stars: ✭ 8,912 (+7029.6%)
Mutual labels: jupyter-notebook, neural-networks
Lis Ynp
🔮 Life is short, you need PyTorch.
Stars: ✭ 92 (-26.4%)
Mutual labels: jupyter-notebook, models
Math And Ml Notes
Books, papers and links to latest research in ML/AI
Stars: ✭ 76 (-39.2%)
Mutual labels: jupyter-notebook, neural-networks
Sigmoidal ai
Tutoriais de Python, Data Science, Machine Learning e Deep Learning - Sigmoidal
Stars: ✭ 103 (-17.6%)
Mutual labels: jupyter-notebook, neural-networks
Convisualize nb
Visualisations for Convolutional Neural Networks in Pytorch
Stars: ✭ 57 (-54.4%)
Mutual labels: jupyter-notebook, neural-networks
Knet.jl
Koç University deep learning framework.
Stars: ✭ 1,260 (+908%)
Mutual labels: jupyter-notebook, neural-networks
Yann
This toolbox is support material for the book on CNN (http://www.convolution.network).
Stars: ✭ 41 (-67.2%)
Mutual labels: jupyter-notebook, neural-networks
Codesearchnet
Datasets, tools, and benchmarks for representation learning of code.
Stars: ✭ 1,378 (+1002.4%)
Mutual labels: jupyter-notebook, neural-networks
Mish
Official Repsoitory for "Mish: A Self Regularized Non-Monotonic Neural Activation Function" [BMVC 2020]
Stars: ✭ 1,072 (+757.6%)
Mutual labels: jupyter-notebook, neural-networks
A Nice Mc
Code for "A-NICE-MC: Adversarial Training for MCMC"
Stars: ✭ 115 (-8%)
Mutual labels: jupyter-notebook, neural-networks
Mckinsey Smartcities Traffic Prediction
Adventure into using multi attention recurrent neural networks for time-series (city traffic) for the 2017-11-18 McKinsey IronMan (24h non-stop) prediction challenge
Stars: ✭ 49 (-60.8%)
Mutual labels: jupyter-notebook, neural-networks
Lovaszsoftmax
Code for the Lovász-Softmax loss (CVPR 2018)
Stars: ✭ 1,148 (+818.4%)
Mutual labels: jupyter-notebook, neural-networks
Machine Learning From Scratch
Succinct Machine Learning algorithm implementations from scratch in Python, solving real-world problems (Notebooks and Book). Examples of Logistic Regression, Linear Regression, Decision Trees, K-means clustering, Sentiment Analysis, Recommender Systems, Neural Networks and Reinforcement Learning.
Stars: ✭ 42 (-66.4%)
Mutual labels: jupyter-notebook, neural-networks
Neural Networks
brief introduction to Python for neural networks
Stars: ✭ 82 (-34.4%)
Mutual labels: jupyter-notebook, neural-networks
Teacher Student Training
This repository stores the files used for my summer internship's work on "teacher-student learning", an experimental method for training deep neural networks using a trained teacher model.
Stars: ✭ 34 (-72.8%)
Mutual labels: jupyter-notebook, neural-networks
Neural Tangents
Fast and Easy Infinite Neural Networks in Python
Stars: ✭ 1,357 (+985.6%)
Mutual labels: jupyter-notebook, neural-networks
Pytorchnlpbook
Code and data accompanying Natural Language Processing with PyTorch published by O'Reilly Media https://nlproc.info
Stars: ✭ 1,390 (+1012%)
Mutual labels: jupyter-notebook, neural-networks
Deep learning models in PyTorch
NOTE: This project is not actively maintained anymore. Feel free to send a PR or fork it.
This repository contains deep learning models built in PyTorch. Intended for learning PyTorch, this repo is made understandable for someone with basic python and deep learning knowledge. All kinds of important deep learning models are implemented here. Links to the papers are also given.
The structure for each project is consistent:
- model.py - Contains the model of the neural network.
- train.py - Preprocessing the data and training the network.
- test.py - Infering from the trained network.
- any other supporting files.
Common folders:
- Trained folder - Trained models can will be saved here.
- Datasets folder - Datasets downloaded will be stored here.
Best practices like PEP8, dropout, batch normalization, suitable optimizers are used.
IMP: For training on the CPU remove '.cuda()' wherever you find it.
Models
1. CNNs
- Image Classification: Microsoft-ResNet 2015
- Spatial Transformation Network STN 2016
- You Only Look Once YOLO 2016
- Super resolution IEEE 2015
- Artistic Style Transfer Gatys 2015
- Deep Photo Style Transfer 2017
- Neural Doodle 2016
- Image Colorization UC Berkeley 2016
2. RNNs
- Speech Recognition: Deep Speech 2 2015 (If possible with attention) (Maybe Deep Voice )
- Generating sequences: LSTM 2013
- Sequence to sequence with attention: Text Summarizer
- Neural Machine Translation: Google 2016
- Conversational Model: Google 2015
- Skip Thoughts sentence to vector
3. GANs
- Deep Convolutional GAN: DCGAN 2015
- Text to image synthesis 2016
4. Reinforcement
- Deep Q Learning Deep Mind 2015
- Asynchronous advantage actor-critic A3C
5. Others
- Image Captioning with attention Bengio 2016
- Hybrid Computing with a NN and external memory Nature 2016
- Network in Network 2014
References:
- https://github.com/songrotek/Deep-Learning-Papers-Reading-Roadmap
- https://github.com/terryum/awesome-deep-learning-papers
- https://github.com/yunjey/pytorch-tutorial
- https://github.com/pytorch/examples
- https://github.com/bharathgs/Awesome-pytorch-list
- https://github.com/ritchieng/the-incredible-pytorch
Note that the project description data, including the texts, logos, images, and/or trademarks,
for each open source project belongs to its rightful owner.
If you wish to add or remove any projects, please contact us at [email protected].