"Today we’re excited to share DeepMind’s first significant milestone in demonstrating how artificial intelligence research can drive and accelerate new scientific discoveries. With a strongly interdisciplinary approach to our work, DeepMind has brought together experts from the fields of structural biology, physics, and machine learning to apply cutting-edge techniques to predict the 3D structure of a protein based solely on its genetic sequence." source: https://deepmind.com/blog/alphafold/

Over the past five decades, scientists have been able to determine shapes of proteins in labs using experimental techniques like cryo-electron microscopy, nuclear magnetic resonance or X-ray crystallography, but each method depends on a lot of trial and error, which can take years and cost tens of thousands of dollars per structure. This is why biologists are turning to AI methods as an alternative to this long and laborious process for difficult proteins.

Recently released by Deepmind, Alpha fold, beat top pharmaceutical companies with 100K+ employees like Pfizer, Novartis, etc. at predicting protein structures in the CASP13 challenge. It outperformed all the other competitors and emerged first with a huge difference of correctly predicting 25 proteins correctly whereas the second place winner only predicted 9 of them correctly and that too with only 29K of the 129K present data about different proteins

This research is the greatest breakthrough in this field which will be able to predict how proteins fold for the formation of different types of proteins for different functions. This is important because this could lead to a better understanding and possibly a cure for diseases like Alzheimer's, mad cow's disease etc. because these diseases are believed to be caused due to malfunction in the folding of the proteins in the body.

The architecture for the network was simple, on a high level it constituted of residual convolutional neural network and gradient descent to optimize full protein features in the end.

The audience from this talk will be able to learn about how to reproduce the architecture of the Alpha Fold and also some basics about how different proteins strands affect the body and function of the proteins. This talk will be mostly on the technical side of the Alpha Fold.

In This Talk, We will be discussing different problems and The different focus areas of Deep Learning. This Session will focus on intermediate learners looking to learn deeper in Deep Learning. We, Will, Be taking the different Tasks and seeing which deep Neural Network Architecture can solve this problem and also learn about the different neural network architectures for the same task.

From a long time, we have thought of how we could harness the amazing gift of vision because we could achieve greatness to new heights and open up endless possibilities like cars that can drive themselves. Along the path of harnessing this power, we have found numerous algorithms. In this talk, we will cover and see all the latest trends in this field, the architectures of each algorithm and evolution of different algorithms of image recognition task. we will cover it all from The dinosaur age of Image recognition to the cyborg age of object segmentation and further, CNNs to R-CNNs to Mask-RCNN. A close analysis performance-wise of these models