Support for Theano library was discontinued in 2017. Theano and many other libraries like Torch are getting sidelined with the new frameworks. The most suitable framework that is supposed to stand the test of time is Tensorflow with the heavy backing of Google. I don't feel the same confidence with Caffe2 because Facebook is not known for a community-friendly approach to open-source software. Another thing, that is definitely going to survive in Keras.

The baby boomers to generation z popularly known as Post-Millennials are all living in an impressionable moment of history now, where technologies like machine learning, deep learning and reinforcement learning are witnessing an unparalleled revolution of all time.

In India, we celebrated the festival of color "Holi" last week. We celebrate the end of the winter with a splash of color because that's what the spring will bring us in a few days. When I was young, the celebrations were sparse. It was the decade of frugal parenting. We waited for festivals so eagerly because it meant parent approved outing and fun.

Neural network architecture design is one of the key hyperparameters in solving problems using deep learning and computer vision. Various neural networks are compared on two key factors i.e. accuracy and computational requirement. In general, as we aim to design more accurate neural networks, the computational requirement increases.

What does a nuclear power plant disaster have to do with machine learning? The safety plan for Fukushima Daiichi nuclear power plant was designed using the historical data for past 400 years. The structural engineers designed the plant to withstand an earthquake of 8.6 intensity on Richter scale and a tsunami as high as 5.7 meters. These threshold numbers were decided using predictive modeling. So, they had the data for earthquakes(intensity and annual frequency) in last 400 years and they were looking for a model that can help predict the earthquakes in future.

What if you could use Artificial Intelligence to enhance your photos like those seen on TV? Image super-resolution is the technology which allows you to increase the resolution of your images using deep learning so as to zoom into your images. Image super-resolution is a software technique which will let us enhance the image spatial resolution with the existing hardware. Low Resolution(LR) Image: Pixel density within an image is small, hence it offers few details. High Resolution(HR) Image: Pixel density within an image is large, hence it offers a lot of details. A technique which is used to reconstruct a high-resolution image from one or many low-resolution images by restoring the high-frequency details is called as "Super-Resolution".

Opencv object detectors which are built using Haar feature-based cascade classifiers is at least a decade old. OpenCV framework provides a default pre-built haar and lbp based cascade classifiers for face and eye detection which are very good quality detectors. However, I had never measured the accuracy of these face and eye detectors. I recently discovered that pre-built haar/lbp cascades have a relatively higher false positive rates which might make them unsuitable for many use-cases. It's possible to build an eye detector with very high accuracy and low false positive rates for many cases with OpenCV.

Object detection using Haar feature-based cascade classifiers is at least a decade old. OpenCV framework provides a default pre-built haar and lbp based cascade classifiers for face and eye detection which are very good quality detectors. However, I had never measured the accuracy of these face and eye detectors. I recently discovered that pre-built haar/lbp cascades have a relatively higher false positive rates which might make them unsuitable for many use-cases. It's possible to build an eye detector with very high accuracy and low false positive rates for many cases with OpenCV.