Repo-2017 - Python codes in Machine Learning, NLP, Deep Learning and Reinforcement Learning with Keras and Theano

  •        200

Welcome to my GitHub repo. I am a Data Scientist and I code in R, Python and Wolfram Mathematica. Here you will find some Machine Learning, Deep Learning, Natural Language Processing and Artificial Intelligence models I developed.



Related Projects

Pyod - A Python Toolkit for Scalable Outlier Detection (Anomaly Detection)

  •    Python

Important Notes: PyOD contains some neural network based models, e.g., AutoEncoders, which are implemented in keras. However, PyOD would NOT install keras and tensorflow automatically. This would reduce the risk of damaging your local installations. You are responsible for installing keras and tensorflow if you want to use neural net based models. An instruction is provided here. Anomaly detection resources, e.g., courses, books, papers and videos.

Keras-GAN - Keras implementations of Generative Adversarial Networks.

  •    Python

Collection of Keras implementations of Generative Adversarial Networks (GANs) suggested in research papers. These models are in some cases simplified versions of the ones ultimately described in the papers, but I have chosen to focus on getting the core ideas covered instead of getting every layer configuration right. Contributions and suggestions of GAN varieties to implement are very welcomed. Implementation of Auxiliary Classifier Generative Adversarial Network.

text2vec - Fast vectorization, topic modeling, distances and GloVe word embeddings in R.

  •    R

text2vec is an R package which provides an efficient framework with a concise API for text analysis and natural language processing (NLP). To learn how to use this package, see and the package vignettes. See also the text2vec articles on my blog.

TensorFlow-VAE-GAN-DRAW - A collection of generative methods implemented with TensorFlow (Deep Convolutional Generative Adversarial Networks (DCGAN), Variational Autoencoder (VAE) and DRAW: A Recurrent Neural Network For Image Generation)

  •    Python

TensorFlow implementation of Deep Convolutional Generative Adversarial Networks, Variational Autoencoder (also Deep and Convolutional) and DRAW: A Recurrent Neural Network For Image Generation. Deep Convolutional Generative Adversarial Networks produce decent results after 10 epochs using default parameters.

reactionrnn - Python module + R package to predict the reactions to a given text using a pretrained recurrent neural network

  •    Python

reactionrnn is a Python 2/3 module + R package on top of Keras/TensorFlow which can easily predict the proportionate reactions (love, wow, haha, sad, angry) to a given text using a pretrained recurrent neural network. Unlike traditional sentiment analysis models using tools like word2vec/doc2vec, reactionrnn handles text at the character level, allowing it to incorporate capitalization, grammar, text length, and sarcasm in its predictions.

nlp-architect - NLP Architect by Intel AI Lab: Python library for exploring the state-of-the-art deep learning topologies and techniques for natural language processing and natural language understanding

  •    Python

NLP Architect is an open-source Python library for exploring state-of-the-art deep learning topologies and techniques for natural language processing and natural language understanding. It is intended to be a platform for future research and collaboration. Framework documentation on NLP models, algorithms, and modules, and instructions on how to contribute can be found at our main documentation site.

Deep-Learning-with-Keras - Code repository for Deep Learning with Keras published by Packt

  •    Jupyter

This is the code repository for Deep Learning with Keras, published by Packt. It contains all the supporting project files necessary to work through the book from start to finish. This book starts by introducing you to supervised learning algorithms such as simple linear regression, classical multilayer perceptron, and more sophisticated Deep Convolutional Networks. In addition, you will also understand unsupervised learning algorithms such as Autoencoders, Restricted Boltzmann Machines, and Deep Belief Networks. Recurrent Networks and Long Short Term Memory (LSTM) networks are also explained in detail. You will also explore image processing involving the recognition of handwritten digital images, the classification of images into different categories, and advanced object recognition with related image annotations. An example of the identification of salient points for face detection is also provided.

lectures - Oxford Deep NLP 2017 course


This repository contains the lecture slides and course description for the Deep Natural Language Processing course offered in Hilary Term 2017 at the University of Oxford. This is an applied course focussing on recent advances in analysing and generating speech and text using recurrent neural networks. We introduce the mathematical definitions of the relevant machine learning models and derive their associated optimisation algorithms. The course covers a range of applications of neural networks in NLP including analysing latent dimensions in text, transcribing speech to text, translating between languages, and answering questions. These topics are organised into three high level themes forming a progression from understanding the use of neural networks for sequential language modelling, to understanding their use as conditional language models for transduction tasks, and finally to approaches employing these techniques in combination with other mechanisms for advanced applications. Throughout the course the practical implementation of such models on CPU and GPU hardware is also discussed.

Inception-v4 - Inception-v4, Inception - Resnet-v1 and v2 Architectures in Keras

  •    Python

Implementations of the Inception-v4, Inception - Resnet-v1 and v2 Architectures in Keras using the Functional API. The paper on these architectures is available at "Inception-v4, Inception-ResNet and the Impact of Residual Connections on Learning". The models are plotted and shown in the architecture sub folder. Due to lack of suitable training data (ILSVR 2015 dataset) and limited GPU processing power, the weights are not provided.

one-pixel-attack-keras - Keras reimplementation of "One pixel attack for fooling deep neural networks" using differential evolution on Cifar10 and ImageNet

  •    Jupyter

How simple is it to cause a deep neural network to misclassify an image if an attacker is only allowed to modify the color of one pixel and only see the prediction probability? Turns out it is very simple. In many cases, an attacker can even cause the network to return any answer they want. The following project is a Keras reimplementation and tutorial of "One pixel attack for fooling deep neural networks".

keras-molecules - Autoencoder network for learning a continuous representation of molecular structures

  •    Python

We report a method to convert discrete representations of molecules to and from a multidimensional continuous representation. This generative model allows efficient search and optimization through open-ended spaces of chemical compounds. We train deep neural networks on hundreds of thousands of existing chemical structures to construct two coupled functions: an encoder and a decoder. The encoder converts the discrete representation of a molecule into a real-valued continuous vector, and the decoder converts these continuous vectors back to the discrete representation from this latent space.

t81_558_deep_learning - Washington University (in St

  •    Jupyter

Deep learning is a group of exciting new technologies for neural networks. Through a combination of advanced training techniques and neural network architectural components, it is now possible to create neural networks of much greater complexity. Deep learning allows a neural network to learn hierarchies of information in a way that is like the function of the human brain. This course will introduce the student to computer vision with Convolution Neural Networks (CNN), time series analysis with Long Short-Term Memory (LSTM), classic neural network structures and application to computer security. High Performance Computing (HPC) aspects will demonstrate how deep learning can be leveraged both on graphical processing units (GPUs), as well as grids. Focus is primarily upon the application of deep learning to problems, with some introduction mathematical foundations. Students will use the Python programming language to implement deep learning using Google TensorFlow and Keras. It is not necessary to know Python prior to this course; however, familiarity of at least one programming language is assumed. This course will be delivered in a hybrid format that includes both classroom and online instruction. This syllabus presents the expected class schedule, due dates, and reading assignments. Download current syllabus.

Autoencoders - Torch implementations of various types of autoencoders

  •    Lua

Different models can be chosen using th main.lua -model <modelName>. The denoising criterion can be used to replace the standard (autoencoder) reconstruction criterion by using the denoising flag. For example, a denoising AAE (DAAE) [10] can be set up using th main.lua -model AAE -denoising. The corruption process is additive Gaussian noise *~ N(0, 0.5)*.

tensorlayer-tricks - How to use TensorLayer


While research in Deep Learning continues to improve the world, we use a bunch of tricks to implement algorithms with TensorLayer day to day. Here are a summary of the tricks to use TensorLayer. If you find a trick that is particularly useful in practice, please open a Pull Request to add it to the document. If we find it to be reasonable and verified, we will merge it in.

Super-Resolution-using-Generative-Adversarial-Networks - An implementation of SRGAN model in Keras

  •    Python

This is an implementation of the SRGAN model proposed in the paper Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network in Keras. Note that this project is a work in progress. The SRGAN model is built in stages within Initially, only the SR-ResNet model is created, to which the VGG network is appended to create the pre-training model. The VGG weights are freezed as we will not update these weights.

seq2seq - Sequence to Sequence Learning with Keras

  •    Python

Hi! You have just found Seq2Seq. Seq2Seq is a sequence to sequence learning add-on for the python deep learning library Keras. Using Seq2Seq, you can build and train sequence-to-sequence neural network models in Keras. Such models are useful for machine translation, chatbots (see [4]), parsers, or whatever that comes to your mind. That's it! You have successfully compiled a minimal Seq2Seq model! Next, let's build a 6 layer deep Seq2Seq model (3 layers for encoding, 3 layers for decoding).

cppn-gan-vae-tensorflow - Train CPPNs as a Generative Model, using Generative Adversarial Networks and Variational Autoencoder techniques to produce high resolution images

  •    Python

Train Compositional Pattern Producing Network as a Generative Model, using Generative Adversarial Networks and Variational Autoencoder techniques to produce high resolution images. Run python from the command line to train from scratch and experiment with different settings.

practical-1 - Oxford Deep NLP 2017 course - Practical 1: word2vec

  •    Jupyter

For this practical, you'll be provided with a partially-complete IPython notebook, an interactive web-based Python computing environment that allows us to mix text, code, and interactive plots. We will be training word2vec models on TED Talk and Wikipedia data, using the word2vec implementation included in the Python package gensim. After training the models, we will analyze and visualize the learned embeddings.

keras-contrib - Keras community contributions

  •    Python

This library is the official extension repository for the python deep learning library Keras. It contains additional layers, activations, loss functions, optimizers, etc. which are not yet available within Keras itself. All of these additional modules can be used in conjunction with core Keras models and modules. As the community contributions in Keras-Contrib are tested, used, validated, and their utility proven, they may be integrated into the Keras core repository. In the interest of keeping Keras succinct, clean, and powerfully simple, only the most useful contributions make it into Keras. This contribution repository is both the proving ground for new functionality, and the archive for functionality that (while useful) may not fit well into the Keras paradigm.

We have large collection of open source products. Follow the tags from Tag Cloud >>

Open source products are scattered around the web. Please provide information about the open source projects you own / you use. Add Projects.