The Shogun API cookbook — Shogun-cookbook 5.0 documentation. GitHub - kpzhang93/MTCNN_face_detection_alignment: Joint Face Detection and Alignment using Multi-task Cascaded Convolutional Neural Networks. Automatic transliteration with LSTM · YerevaNN. By Tigran Galstyan, Hrayr Harutyunyan and Hrant Khachatrian.
Many languages have their own non-Latin alphabets but the web is full of content in those languages written in Latin letters, which makes it inaccessible to various NLP tools (e.g. automatic translation). Transliteration is the process of converting the romanized text back to the original writing system. In theory every language has a strict set of romanization rules, but in practice people do not follow the rules and most of the romanized content is hard to transliterate using rule based algorithms.
We believe this problem is solvable using the state of the art NLP tools, and we demonstrate a high quality solution for Armenian based on recurrent neural networks. The Neural Network Zoo - The Asimov Institute. With new neural network architectures popping up every now and then, it’s hard to keep track of them all.
Knowing all the abbreviations being thrown around (DCIGN, BiLSTM, DCGAN, anyone?) Can be a bit overwhelming at first. So I decided to compose a cheat sheet containing many of those architectures. Most of these are neural networks, some are completely different beasts. Colorful Image Colorization. How to interpret the results Welcome!
Computer vision algorithms often work well on some images, but fail on others. Ours is like this too. We believe our work is a significant step forward in solving the colorization problem. However, there are still many hard cases, and this is by no means a solved problem. GitHub - Smorodov/Paddle: PArallel Distributed Deep LEarning.
Convolutional networks.