Contractive_Autoencoder_in_Pytorch - Pytorch implementation of contractive autoencoder on MNIST dataset

  •        49

Pytorch implementation of contractive autoencoder on MNIST dataset. Rifai, Salah, et al. “Contractive auto-encoders: Explicit invariance during feature extraction.” Proceedings of the 28th international conference on machine learning (ICML-11). 2011.

https://github.com/avijit9/Contractive_Autoencoder_in_Pytorch

Tags
Implementation
License
Platform

   




Related Projects

grokking-pytorch - The Hitchiker's Guide to PyTorch

  •    

PyTorch is a flexible deep learning framework that allows automatic differentiation through dynamic neural networks (i.e., networks that utilise dynamic control flow like if statements and while loops). It supports GPU acceleration, distributed training, various optimisations, and plenty more neat features. These are some notes on how I think about using PyTorch, and don't encompass all parts of the library or every best practice, but may be helpful to others. Neural networks are a subclass of computation graphs. Computation graphs receive input data, and data is routed to and possibly transformed by nodes which perform processing on the data. In deep learning, the neurons (nodes) in neural networks typically transform data with parameters and differentiable functions, such that the parameters can be optimised to minimise a loss via gradient descent. More broadly, the functions can be stochastic, and the structure of the graph can be dynamic. So while neural networks may be a good fit for dataflow programming, PyTorch's API has instead centred around imperative programming, which is a more common way for thinking about programs. This makes it easier to read code and reason about complex programs, without necessarily sacrificing much performance; PyTorch is actually pretty fast, with plenty of optimisations that you can safely forget about as an end user (but you can dig in if you really want to).

PyTorch - Tensors and Dynamic neural networks in Python with strong GPU acceleration

  •    Python

PyTorch is a deep learning framework that puts Python first. It is a python package that provides Tensor computation (like numpy) with strong GPU acceleration, Deep Neural Networks built on a tape-based autograd system. You can reuse your favorite python packages such as numpy, scipy and Cython to extend PyTorch when needed.

deep-learning-book - Repository for "Introduction to Artificial Neural Networks and Deep Learning: A Practical Guide with Applications in Python"

  •    Jupyter

Repository for the book Introduction to Artificial Neural Networks and Deep Learning: A Practical Guide with Applications in Python. Deep learning is not just the talk of the town among tech folks. Deep learning allows us to tackle complex problems, training artificial neural networks to recognize complex patterns for image and speech recognition. In this book, we'll continue where we left off in Python Machine Learning and implement deep learning algorithms in PyTorch.

deep-reinforcement-learning - Repo for the Deep Reinforcement Learning Nanodegree program

  •    Jupyter

This repository contains material related to Udacity's Deep Reinforcement Learning Nanodegree program. The tutorials lead you through implementing various algorithms in reinforcement learning. All of the code is in PyTorch (v0.4) and Python 3.

pytorch_geometric - Geometric Deep Learning Extension Library for PyTorch

  •    Python

PyTorch Geometric is a geometric deep learning extension library for PyTorch. It consists of various methods for deep learning on graphs and other irregular structures, also known as geometric deep learning, from a variety of published papers. In addition, it consists of an easy-to-use mini-batch loader, a large number of common benchmark datasets (based on simple interfaces to create your own), and helpful transforms, both for learning on arbitrary graphs as well as on 3D meshes or point clouds.


pytorch-tutorial - PyTorch Tutorial for Deep Learning Researchers

  •    Python

This repository provides tutorial code for deep learning researchers to learn PyTorch. In the tutorial, most of the models were implemented with less than 30 lines of code. Before starting this tutorial, it is recommended to finish Official Pytorch Tutorial.

Deep-Learning-Boot-Camp - A community run, 5-day PyTorch Deep Learning Bootcamp

  •    Jupyter

Tel-Aviv Deep Learning Bootcamp is an intensive (and free!) 5-day program intended to teach you all about deep learning. It is nonprofit focused on advancing data science education and fostering entrepreneurship. The Bootcamp is a prominent venue for graduate students, researchers, and data science professionals. It offers a chance to study the essential and innovative aspects of deep learning. Participation is via a donation to the A.L.S ASSOCIATION for promoting research of the Amyotrophic Lateral Sclerosis (ALS) disease.

ignite - High-level library to help with training neural networks in PyTorch

  •    Python

Ignite is a high-level library to help with training neural networks in PyTorch. As you can see, the code is more concise and readable with ignite. Furthermore, adding additional metrics, or things like early stopping is a breeze in ignite, but can start to rapidly increase the complexity of your code when "rolling your own" training loop.

Facial-Similarity-with-Siamese-Networks-in-Pytorch - Implementing Siamese networks with a contrastive loss for similarity learning

  •    Jupyter

The goal is to teach a siamese network to be able to distinguish pairs of images. This project uses pytorch. Any dataset can be used. Each class must be in its own folder. This is the same structure that PyTorch's own image folder dataset uses.

PyTorch-Tutorial - Build your neural network easy and fast

  •    Jupyter

In these tutorials for pyTorch, we will build our first Neural Network and try to build some advanced Neural Network architectures developed recent years. Thanks for liufuyang's notebook files which is a great contribution to this tutorial.

pytorch-cnn-finetune - Fine-tune pretrained Convolutional Neural Networks with PyTorch

  •    Python

VGG and AlexNet models use fully-connected layers, so you have to additionally pass the input size of images when constructing a new model. This information is needed to determine the input size of fully-connected layers. See examples/cifar10.py file (requires PyTorch 0.4).

onnx - Open Neural Network Exchange

  •    PureBasic

Open Neural Network Exchange (ONNX) is the first step toward an open ecosystem that empowers AI developers to choose the right tools as their project evolves. ONNX provides an open source format for AI models. It defines an extensible computation graph model, as well as definitions of built-in operators and standard data types. Initially we focus on the capabilities needed for inferencing (evaluation). Caffe2, PyTorch, Microsoft Cognitive Toolkit, Apache MXNet and other tools are developing ONNX support. Enabling interoperability between different frameworks and streamlining the path from research to production will increase the speed of innovation in the AI community. We are an early stage and we invite the community to submit feedback and help us further evolve ONNX.

ngraph - nGraph is an open source C++ library, compiler and runtime for Deep Learning frameworks

  •    C++

Welcome to the open-source repository for the Intel® nGraph™ Library. Our code base provides a Compiler and runtime suite of tools (APIs) designed to give developers maximum flexibility for their software design, allowing them to create or customize a scalable solution using any framework while also avoiding device-level hardware lock-in that is so common with many AI vendors. A neural network model compiled with nGraph can run on any of our currently-supported backends, and it will be able to run on any backends we support in the future with minimal disruption to your model. With nGraph, you can co-evolve your software and hardware's capabilities to stay at the forefront of your industry. The nGraph Compiler is Intel's graph compiler for Artificial Neural Networks. Documentation in this repo describes how you can program any framework to run training and inference computations on a variety of Backends including Intel® Architecture Processors (CPUs), Intel® Nervana™ Neural Network Processors (NNPs), cuDNN-compatible graphics cards (GPUs), custom VPUs like Movidius, and many others. The default CPU Backend also provides an interactive Interpreter mode that can be used to zero in on a DL model and create custom nGraph optimizations that can be used to further accelerate training or inference, in whatever scenario you need.

OpenNMT-py - Open Source Neural Machine Translation in PyTorch

  •    Python

This is a Pytorch port of OpenNMT, an open-source (MIT) neural machine translation system. It is designed to be research friendly to try out new ideas in translation, summary, image-to-text, morphology, and many other domains. Codebase is relatively stable, but PyTorch is still evolving. We currently only support PyTorch 0.4 and recommend forking if you need to have stable code.

PyTorch-NLP - Supporting Rapid Prototyping with a Toolkit (incl. Datasets and Neural Network Layers)

  •    Python

PyTorch-NLP, or torchnlp for short, is a library of neural network layers, text processing modules and datasets designed to accelerate Natural Language Processing (NLP) research. Join our community, add datasets and neural network layers! Chat with us on Gitter and join the Google Group, we're eager to collaborate with you.

NCRFpp - NCRF++, an Open-source Neural Sequence Labeling Toolkit

  •    Python

Sequence labeling models are quite popular in many NLP tasks, such as Named Entity Recognition (NER), part-of-speech (POS) tagging and word segmentation. State-of-the-art sequence labeling models mostly utilize the CRF structure with input word features. LSTM (or bidirectional LSTM) is a popular deep learning based feature extractor in sequence labeling task. And CNN can also be used due to faster computation. Besides, features within word are also useful to represent word, which can be captured by character LSTM or character CNN structure or human-defined neural features. NCRF++ is a PyTorch based framework with flexiable choices of input features and output structures. The design of neural sequence labeling models with NCRF++ is fully configurable through a configuration file, which does not require any code work. NCRF++ is a neural version of CRF++, which is a famous statistical CRF framework.