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.
Using Python StatsModel ARIMA to Forecast Time Series of Cars in Walmart Parking Lot
Machine learning datasets used in tutorials on MachineLearningMastery.com
Tensorflow implement FSRNet： End-to-End Learning Face Super-Resolution with Facial Priors
Unofficial Tensorflow implementation of the AAAI'18 paper "Spatial Temporal Graph Convolutional Networks for Skeleton-Based Action Recognition"
A Keras implementation of CenterNet with pre-trained model (unofficial)
ICoordinateMapper functions using PyKinectV2 wrapper for python
Human - Robot Collaboration for fabric folding using Kinect2, RoboDK, Reflex 1 gripper and the ATI Force Torque Gamma sensor
Creating real-time dynamic Point Clouds using PyQtGraph, Kinect 2 and the python library PyKinect2.
RainNet: a convolutional neural network for radar-based precipitation nowcasting
