This blog focuses on investigating collaborative filtering; one of the common approaches used to generate movie recommendations.
For human beings, interpreting what we see is so easy that we are hardly conscious of it; However, for computers, these tasks are very difficult problems to solve.
In this blog, we will briefly introduce image recognition with transfer learning. At its most fundamental, an image recognition algorithm takes images and outputs a label describing the image.
We will classify images from the Caltech 101 dataset with the Open Source Computer Vision (OpenCV) library.
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 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.
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.
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.
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.
Document classification is currently one of the most important branches of Natural Language Processing (NLP). The general idea is to automatically classify documents into categories using machine learning algorithms.
The applications are almost endless, we can classify: patient records, movie reviews, webpages, emails (spam vs not spam), and in fact anything text based.
Dynamic Time Warping (DTW) is an intelligent, dynamically adjusted metric that allows more flexibility when used in combination with any distance dependent algorithm. This flexibility allows for better classification results in many different time-series analyses.
DTW allows us to retain the temporal dynamics by directly modeling the time-series. As its names suggests, the usual Euclidean distance in problems is replaced with a dynamically adjusted metric.
Almost every major company now has vested interest in image recognition algorithms: Tesla for autonomous driving, Amazon for product and price comparison, Google for its image search, and of course Facebook for facial recognition.
The potential for this type of technology is limitless and has already been used in fields as disparate as sport science to astronomy. Here we discuss one recent and important example in the medical field: diabetic retinopathy.
Diagnosis software, based on image recognition, allows quick and cheap diagnosis, saving human hours as well as human lives.
The goal for this project was to determine from raw neural activity what the volunteer was thinking at any given time. This form of analysis have many direct applications, for example: prosthesis moving, computer games which interface with brain activity, and even piloting vehicles without need for physical interactions.