• Deep Learning on the Edge — First Impressions of the Movidius Neural Compute Stick
  Tips on speeding up the Movidius Neural Compute Stick for faster image recognition
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• Movidius NCS | Neural Compute Stick
  Introduction, SDKs, tutorials, examples for the Intel Movidius Neural Compute Stick
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• Multivariate Time Series Forecasting with LSTMs in Keras
  Neural networks like Long Short-Term Memory (LSTM) recurrent neural networks are able to almost seamlessly model problems with multiple input variables. This is a great benefit in time series forecasting, where classical linear methods can be difficult to adapt to multivariate or multiple input forecasting problems. In this tutorial, you will discover how you can develop an LSTM model for multivariate time series forecasting in the Keras deep learning library. After completing this tutorial, you will know: How to transform a raw dataset into something we can use for time series forecasting. How to prepare data and fit an LSTM for a multivariate time series forecasting problem. How to make a forecast and rescale the result back into the original units.
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• Object Detection with Intel Neural Compute Stick and YOLO
  Objekterkennung mit neuronalen Netzen - codecentric AG Blog
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• OpenCV 4 Node.js
  Asynchronous OpenCV 3.x nodejs bindings with JavaScript and TypeScript API, with examples for: Face Detection, Machine Learning, Deep Neural Nets, Hand Gesture Recognition, Object Tracking, Feature Matching, Image Histogram
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• Stanford University CS231n
  Convolutional Neural Networks for Visual Recognition
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• Time Series Prediction Using LSTM Deep Neural Networks
  This article focuses on using a Deep LSTM Neural Network architecture to provide multidimensional time series forecasting using Keras and Tensorflow - specifically on stock market datasets to provide momentum indicators of stock price. The code for this framework can be found in the following GitHub repo (it assumes python version 3.5.x and the requirement versions in the requirements.txt file. Deviating from these versions might cause errors): https://github.com/jaungiers/LSTM-Neural-Network-for-Time-Series-Prediction The following article sections will briefly touch on LSTM neuron cells, give a toy example of predicting a sine wave then walk through the application to a stochastic time series. The article assumes a basic working knowledge of simple deep n
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