ideaplexus,IDEAPLEXUS,github

speech-to-text-wavenet

Speech-to-Text-WaveNet : End-to-end sentence level English speech recognition based on DeepMind's WaveNet and tensorflow

spin

Repository for the paper "Learning to Reconstruct 3D Human Pose and Shape via Model-fitting in the Loop"

spleeter

Deezer source separation library including pretrained models.

spresso-search

Visual metasearch engine built with React, Redux, Express and TypeScript

srgan-treehacks

TreeHacks 2019: Best computer vision prize and IBM’s #1 favorite health hack. Upscaling low resolution videos with a novel super resolution GAN architecture.

srs

SRS is a simple live streaming cluster, a simple joy.

stargan

Official PyTorch Implementation of StarGAN - CVPR 2018

stargan-v2

StarGAN v2 - Official PyTorch Implementation (CVPR 2020)

stereo-magnification

Code accompanying the SIGGRAPH 2018 paper "Stereo Magnification: Learning View Synthesis using Multiplane Images"

sticky-browser

Frameless Browser that can be made sticky, e.g. always on top. So you have picture in picture.

stock-market-analysis

Stock Market Analysis with RNN and Time Series

stock-prediction

Stock price prediction with recurrent neural network. The data is from the Chinese stock.

stock-prediction-1

LSTM RNN for sentiment-based stock prediction

stock-price-prediction-lstm

OHLC Average Prediction of Apple Inc. Using LSTM Recurrent Neural Network

stock-price-predictor

This project seeks to utilize Deep Learning models, Long-Short Term Memory (LSTM) Neural Network algorithm, to predict stock prices.

stock-rnn

Predict stock market prices using RNN model with multilayer LSTM cells + optional multi-stock embeddings.

stockprediction

Plain Stock Close-Price Prediction via Graves LSTM RNNs

In this noteboook I will create a complete process for predicting stock price movements. Follow along and we will achieve some pretty good results. For that purpose we will use a Generative Adversarial Network (GAN) with LSTM, a type of Recurrent Neural Network, as generator, and a Convolutional Neural Network, CNN, as a discriminator. We use LSTM for the obvious reason that we are trying to predict time series data. Why we use GAN and specifically CNN as a discriminator? That is a good question: there are special sections on that later.

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