Predicting Stock Volume with LSTM

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.

Understanding Speech Recognition: An Introduction

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.

Image Recognition: Getting Value from Visual Data

Image Recognition: Getting Value from Visual Data

Machine vision is one of the pillars of machine learning. Its stature will grow in importance as more accurate algorithms are explored, but has already been used by Amazon for online shopping, Google for image search and Facebook for its facial recognition. Here we give a brief overview of convolutional neural networks, one of the best deep learning algorithms for image recognition. 

Extracting Value from Customer Data: Customer Lifetime Value

Extracting Value from Customer Data: Customer Lifetime Value

Customer lifetime value goes a step further than churn rate prediction. It uses retention (i.e. the inverse of churn) and a “discount” factor, which accounts for the decreasing value of future money to predict the expected monetary value that a customer will generate over the entire period of the relationship with your company.

Archaeological Site Prediction Using Machine Learning

Archaeological Site Prediction Using Machine Learning

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.

Nuts and Bolts of Portfolio Prediction: Reinforcement Learning

Nuts and Bolts of Portfolio Prediction: Reinforcement Learning

We introduce the concept of Reinforcement Learning and discuss how such an algorithm can be used to solve portfolio optimizations that extract the most value from raw market data. Over time, such algorithms learn the strategies to deploy that will generate the maximum possible value and consequently the maximum possible profit.