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deep-summarization

Uses Recurrent Neural Network (LSTM/GRU/basic_RNN units) for summarization of amazon reviews

deep-summarization-toolkit

A toolkit designed to simplify summarization research

deep_learning_book_code

Code for the examples explained in my deep learning ebook in Tamil Language

deep_learning_bootcamp

deep_learning_python_intro

Materials for the course on programming deep neural networks in Python (Russian)

deepgraphlearning

Homepage

deeplearning4nlp-tutorial

Hands-on tutorial on deep learning with a special focus on Natural Language Processing (NLP)

deeplearningbook-notes

Notes on the Deep Learning book from Ian Goodfellow, Yoshua Bengio and Aaron Courville (2016)

deeplearningnlp

Natural Language Processing Crash Course

deeplearningproject

An in-depth machine learning tutorial introducing readers to a whole machine learning pipeline from scratch.

deeplearningtutorials

Deep Learning Tutorial notes and code. See the wiki for more info.

deeplearningzerotoall

TensorFlow Basic Tutorial Labs

deeplearntoolbox

Matlab/Octave toolbox for deep learning. Includes Deep Belief Nets, Stacked Autoencoders, Convolutional Neural Nets, Convolutional Autoencoders and vanilla Neural Nets. Each method has examples to get you started.

demo

Differential Evolution for Multiobjective Optimization and its variants

demofortext2word-freq

A Python demo to show how o preprocess and transform texts into words and corresponding frequencies.

developing-data-migrations-and-integrations-with-salesforce

Source code for 'Developing Data Migrations and Integrations with Salesforce' by David Masri

devops-in-python

Source Code for 'DevOps in Python' by Moshe Zadka

digestant

Modules for effectively digesting data from Twitter and Reddit using ML, NLP and statistics.

dinosaur-land

Implementation of Recurrent Neural Network from scratch to invent new Dinosaurs!

discogan-pytorch

PyTorch implementation of "Learning to Discover Cross-Domain Relations with Generative Adversarial Networks"

discussionsummarization

Discussion Summarization is the process of condensing a text document which is a collection of discussion threads, using CBS (Cluster Based Summarization) approach in order to create a relevant summary which enlists most of the important points of the original thematic discussion, thereby providing the users, both concise and comprehensive piece of information. This outlines all the opinions which are described from multiple perspectives in a single document. This summary is completely unbiased as they present information extracted from multiple sources based on a designed algorithm, without any editorial touch or subjective human intervention. Extractive methods used here, follow the technique of selecting a subset of existing words, phrases, or sentences in the original text to form the summary. An iterative ranking algorithm is followed for clustering. The NLP (Natural Language Processing) is used to process human language data. Precisely, it is applied while working with corpora, categorizing text, analyzing linguistic structure. Thus, the quick summary is aimed at being salient, relevant and non-redundant. The proposed model is validated by testing its ability to generate optimal summary of discussions in Yahoo Answers. Results show that the proposed model is able to generate much relevant summary when compared to present summarization techniques.

dl-course

DL Course Materials

dl-workshop-series

Material used for Deep Learning related workshops for Machine Learning Tokyo (MLT)

dl_tutorial

Tutorials for deep learning

dlbook_exercises

Exercises for the Deep Learning textbook at www.deeplearningbook.org

dlrm

An implementation of a deep learning recommendation model (DLRM)

doc-summary

Extractive Document Summarization

docs-veevavault-connector

Veevavault Connector documentation

docsum

Implementations of various methods for document summarization.

document-classification

📖 Document classification with PyTorch.