Anomaly detection is a common problem that can be solved using machine learning techniques. Simple density based algorithms provide a good baseline for such projects, and can be used to solve a variety of problems from defect detection in manufacturing to network attacks in IT.
Recently, there has been great success in time series analyses by applying dynamic time warping (DTW). Indeed, when combined with simple algorithms such as k-Nearest Neighbours, DTW has reproduced accuracies of state-of-the-art algorithms, such as deep neural nets etc.
We will discuss here a supervised classification example of wearable devices and a possible use-case for DTW.
In this blog, we will cover a broad spectrum of topics in Finance that relate to predicting trends and outliers on both the small and large scales. We will cover
- Commercial Growth Predictions by Monitoring Parking Lots
- Stock Predictions with Weather Forecasts
- Anomaly Detection use to find Fraud
- Market Forecasts with Sentiment Analysis
Much of the hype surrounding neural networks is about image-based applications. However, Recurrent Neural Networks (RNNs) have been successfully used in recent years to predict future events in time series as well. RNNs have contributed to breakthroughs in a wide variety of fields centered around predicting sequences of events. In this piece, however, we'll demonstrate how one type of RNN, the Long Short-Term Memory (LSTM) network, can be used to predict even financial time series data—perhaps the most chaotic and difficult of all time series.
This blog focuses on developing an algorithm to understand spoken Arabic digits. Such algorithms are the first step in developing computers that can understand languages with applications ranging from text-to-speech, voice-recognition, and translation, to modern AI assistants that are widely becoming available.
Machine Learning is still in its infancy in archaeology, but the approach holds enormous promise. Just as in finance and disaster mitigation, the use of satellite imagery has become a vital resource for archaeology. As more archaeologists around the world automate the process of identifying archaeological sites with satellite images, sites will be found at a faster rate than ever before.