ericzengyi

AWS-S3-Upload-Image-PhotoLibrary-or-Camera

Swift example - upload from camera or photo library

science_rcn

Reference implementation of a two-level RCN model

AWS_iOS

AWS integration for iOS

coders-strike-back-referee

Brutaltester compatible referee for coders strike back

cg-brutaltester

A local arena for codingame multiplayer puzzles

gym

A toolkit for developing and comparing reinforcement learning algorithms.

Dancing-Robot

A HoloLens application allowing collaborative viewing and manipulation of the 3D model of an ABB welding robot.

MMWormhole

Message passing between iOS apps and extensions.

cheep-sync

CheepSync is an open source time synchronization service for BLE advertisers in ADV_NONCONN_IND mode

ecg

Cardiologist-level arrhythmia detection and classification in ambulatory electrocardiograms using a deep neural network

flint

A Time Series Library for Apache Spark

bpemb

Pre-trained subword embeddings in 275 languages, based on Byte-Pair Encoding (BPE)

hackerrank-ml

My answers to the machine learning HackerRank challenges

STOCK-PRICE-PREDICTION-FOR-NSE-USING-DEEP-LEARNING-MODELS

Financial time series analysis and prediction have become an important area of re- search in today's world. Designing and pricing securities, construction of portfolios and other risk management strategies depends on the prediction of financial time se- ries. A financial time series often involve large dataset with complex interaction among themselves. A proper analysis of this data will give the investor better gains, but the existing methodologies focus on linear models (AR, MA, ARMA, ARIMA) and non- linear models (ARCH, GARCH, TAR). These models are not capable of identifying the complex interactions and latent dynamics existing within the data. Applying Deep learning methods to these types of data will give more accurate results than the existing methods. Deep learning architectures can identify the hidden patterns in the data and is also capable of exploiting the interactions existing within the data, which is, at least not possible by the existing financial models. The proposed work uses four different deep learning architectures (RNN, LSTM, CNN, and MLP) for predicting the minute wise stock price for NSE listed companies and compares the performance of the mod- els. The proposed method uses a sliding window based approach for predicting future values on a short-term basis. The performance of the models was quantified using error percentage.

Stock-Price-Prediction

Stock price prediction using LSTM and 1D Convoltional Layer implemented in keras with TF backend on daily closing price of S&P 500 data from Jan 2000 to Aug 2016

Deep-Convolution-Stock-Technical-Analysis

Uses Deep Convolutional Neural Networks (CNNs) to model the stock market using technical analysis. Predicts the future trend of stock selections.

LSTM-Neural-Network-for-Time-Series-Prediction

LSTM built using Keras Python package to predict time series steps and sequences. Includes sin wave and stock market data

gru_stocks_new_

lstm-crypto-predictor

Predicting cryptocurrency price using RNN-LSTM networks

CS291K

🎭 Sentiment Analysis of Twitter data using combined CNN and LSTM Neural Network models

Delving-deep-into-GANs

Generative Adversarial Networks (GANs) resources sorted by citations

Insults

Code for the Kaggle insult competition

detect_insults

tensorflow-XNN

4th Place Solution for Mercari Price Suggestion Competition on Kaggle using DeepFM variant.

wiki-detox

See https://meta.wikimedia.org/wiki/Research:Modeling_Talk_Page_Abuse

codecombat

Game for learning how to code.

twitter-hatespeech

Deep Learning models to detect hate speech in tweets

stock-rnn

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

MarketVectors

Implementations for my blog post [here](https://medium.com/@TalPerry/deep-learning-the-stock-market-df853d139e02#.flflpo3xf)

Stock-Market-Prediction-using-Natural-Language-Processing