Text data requires special preparation before you can start using it for predictive modeling. The text must be parsed to remove words, called tokenization. Then the words need to be encoded as integers or floating point values for use as input to a machine…

You need datasets to practice on when getting started with deep learning for natural language processing tasks. It is better to use small datasets that you can download quickly and do not take too long to fit models. Further, it is also helpful to use standard…

The promise of deep learning in the field of natural language processing is the better performance by models that may require more data but less linguistic expertise to train and operate. There is a lot of hype and large claims around deep learning methods…

Multi-task learning is becoming increasingly popular in NLP but it is still not understood very well which tasks are useful. As inspiration, this post gives an overview of the most common auxiliary tasks used for multi-task learning for NLP.

This post gives an overview of highlights of the Conference on Empirical Methods in Natural Language Processing (EMNLP) 2017 in Copenhagen.

Face detection using deep learning. Contribute to natanielruiz/dockerface development by creating an account on GitHub.

Semantic Segmentation using Fully Convolutional Neural Network. - upul/Semantic_Segmentation

Last year, when I was on a mission to "fix GANs" I had a tendency to focus only
on what the loss function is, and completely disregard the issue of how do we
actually find a minimum. Here is the paper that has finally challenged that
attitude:

* Mescheder…

A detailed analysis

Caveat: Some prior knowledge of CNNs is assumed for this post

Tools and recipes to train deep learning models and build services for NLP tasks such as text classification, semantic search ranking and recall fetching, cross-lingual information retrieval, and q...

Tensorflow implementation of paper "unsupervised image to image translation networks" - GitHub - leehomyc/Img2Img-Translation-Networks: Tensorflow implementation of paper &quo...

Привет, Хабр! Мы продолжаем нашу традицию и снова выпускаем ежемесячный набор рецензий на научные статьи от членов сообщества Open Data Science из канала #article_essense. Хотите получать их раньше...

This entry is a non-exhaustive introduction on how to create interactive content directly from your Jupyter notebook.

Using D3, this post visually explores activation functions, a fundamental component of neural networks.

tensorlayer - TensorLayer: Deep Learning and Reinforcement Learning Library for Researcher and Engineer.