deep learning

Image Recognition and Transfer Learning

Image Recognition and Transfer Learning

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.

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.

Image Recognition: Retinopathy

Image Recognition: Retinopathy

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.