Everyone loves Keras and here’s a great article on why that is.

Today, the Deep Learning ecosystem is much more mature, so thankfully one can get by with learning fewer frameworks. While many excellent frameworks have been released over these intervening years, and are being used in specialized niches, the major ones are Keras, Tensorflow, and Pytorch. Pytorch became popular because of its eager execution model, which Tensorflow did not allow, and which Keras hid behind its cleverly-designed API. Keras has since been subsumed into Tensorflow as tf.keras, but the original Keras lives on as well, with an additional CNTK (from Microsoft) backend. For its part, Tensorflow, in its 2.x incarnation, has embraced Pytorch’s eager execution model, and made tf.keras its default API.

Developing a new drug is an expensive and time consuming process.

Here’s an interesting blog post about how deep learning can speed up the process and lower the costs.

The cost of developing a new drug and bringing it to market is anywhere between $1.3 billion USD to nearly $2.9 billion USD depending on who you ask. Many of the low-hanging fruit are already picked, and the changing health landscape of modern society compounds the challenge. Our aging society is now more heavily affected by heart disease, dementia, and cancer than our progenitors were.

Here’s an interesting article in Forbes on how computer vision is being applied in the real world.

Even though early experiments in computer vision started in the 1950s and it was first put to use commercially to distinguish between typed and handwritten text by the 1970s, today the applications for computer vision have grown exponentially. By 2022, the computer vision and hardware market is expected to […]