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android-chat-ui

A messages UI library for Android

angular-journal-app

angular-tour-of-heroes

Angular Tutorial App

baking-app

Android application that shows recipes and tutorial videos for various sumptuous meals

bigdata18

Transfer learning for time series classification

blueprint

A full-of-features, easy-to-customize, free and open source, light-weight, Android dashboard library for icon packs.

built-it-bigger

Repository for first project of Term 2: Udacity Android Nanodegree

cloud-app

Andela - Google Cloud Pase II

cnnoptimization

Using Particle Swarm Optimization (PSO) to Optimize a CNN (Convulsional Neural Network) - using an simple dataset (not using an image dataset)

conv_arithmetic

A technical report on convolution arithmetic in the context of deep learning

introtodeeplearning

Lab Materials for MIT 6.S191: Introduction to Deep Learning

journal-app

journal-rest

REST API for the Angular Journal App

keras-attention-mechanism

Attention mechanism Implementation for Keras.

liferay-official-plugins

lstm-stock-market-behaviour-prediction

A Simple Stacked LSTM Model for Stock Market Behaviour Prediction

moviesapp

Android application that displays movie data to users

moviesapp-2

Stage two of the Android Nanodegree Movie App

openshift

Openshift apps

polar-dashboard

Sick of the same-old?

pydata-book

Materials and IPython notebooks for "Python for Data Analysis" by Wes McKinney, published by O'Reilly Media

sandwich-club

Android application that parses JSON data to give details about various sandwiches

stock-price-prediction-using-gan

In this project, we will compare two algorithms for stock prediction. First, we will utilize the Long Short Term Memory(LSTM) network to do the Stock Market Prediction. LSTM is a powerful method that is capable of learning order dependence in sequence prediction problems. Furthermore, we will utilize Generative Adversarial Network(GAN) to make the prediction. LSTM will be used as a generator, and CNN as a discriminator. In addition, Natural Language Processing(NLP) will also be used in this project to analyze the influence of News on stock prices.

stockpredictionai

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.

timegan

Codebase for Time-series Generative Adversarial Networks (TimeGAN) - NeurIPS 2019

weatherapp

Program that generates weather information for various locations around the world

xyzreader

Android application for the Udacity-Android Nanodegree Program