In a bid to strengthen its fintech portfolio, digital payments platform MobiKwik on Monday announced it has made a strategic investment of Rs 2 crore in Pune-based data science company Pivotchain Solutions. Pivotchain is a predictive analytics company with expertise in Machine Learning and Artificial Intelligence, and has built exclusive AI and deep learning models for MobiKwik. The Gurgaon-based payments firm said these models will be instrumental for MobiKwik as it rolls out numerous lending products to address the credit requirements of its user base. "MobiKwik is transforming from a leading digital payments player to India's largest digital financial services platform. Delivering high quality fintech products will require immense focus on data, and an in-depth understanding of the user requirements, across categories. Pivotchain is doing incredible work in alternate data scoring, predictive modeling and risk management and this investment will give us an edge over competition."

Artificial Intelligence (AI) School features several sets of great contents ranging from Machine Learning, Azure Cognitive Services, Deep Learning, and other Azure Artificial Intelligence stacks. At AI School, you can easily access a wide range of content type to learn end to end AI based solution. There are several paths, level of courses and AI Services defined in the AI School. Choose them based on your learning path and keep your skills up to date. If you are a developer interested in learning AI or a data scientist, AI School is a great place to getting started and deep dive into Microsoft AI Platform and deep learning.

Alex Krizhevsky didn't get into the AI business to change the course of history. Krizhevsky, born in Ukraine but raised in Canada, was just looking to delay getting a coding job when he reached out to Geoff Hinton about doing a computer-science PhD program in AI at the University of Toronto. The fateful moment was when, as a graduate student, Krizhevsky and a fellow student named Ilya Sutskever, decided to enter the ImageNet competition, a test for AI consisting of a huge database of online images. The competition, open to anyone in the world, was to evaluate algorithms designed for large-scale object detection and image classification. The point wasn't just to crown a winner, but to test a hypothesis: with the right algorithm, the massive amount data in the ImageNet database could be the key to unlocking AI's potential.

In order to make educated decisions in this fast-moving field, all managers should have a basic understanding of AI. Here are four key facts that will give you an edge. AI systems learn from the data and feedback that they receive in response to their earlier decisions. Their predictions and actions are only as good as the data they have been trained on. This characteristic makes AI systems very different from traditional deduction- based programming. A traditional program processes data but does not learn from it.

Applications span diverse markets such as autonomous driving, medical diagnostics, home appliances, industrial automation, adaptive websites, financial analytics and network infrastructure. These applications, especially when implemented on the edge, demand high performance and, low latency to respond successfully to real-time changes in conditions. They also require low power consumption, rendering energy-intensive cloud-based solutions unusable. A further requirement is for these embedded systems to always be on and ready to respond even in the absence of a network connection to the cloud. This combination of factors calls for a change in the way that hardware is designed.

Ltd has invested Rs 2 crore ($294,000) in Pune-based data science company Pivotchain Solutions Technologies Pvt. Ltd. MobiKwik said in a statement that the artificial intelligence and deep learning models built by Pivotchain will be instrumental for its lending products. Pivotchain is a predictive analytics company with expertise in machine learning and artificial intelligence. The statement didn't specify the size of the stake MobiKwik bought in Pivotchain or the valuation. MobiKwik is preparing to roll out digital lending solutions in partnership with banks and non-banking financial companies (NBFCs).

Above two are intro course to deep learning. This is a Youtube channel which contains courses by Prof. Andrew Ng on various topics in deep learning. This a 14 week course, taught by Jeremy Howard. It cover most of the topics in deep learning. This course is a gentle introduction to Reinforcement Learning. It walks you through most of the topics in Reinforcement Learning in high level. This course is not taught at a basic level, so you need to be familiar with basic concepts and perhaps a little more. For more stories follow AiMantra.

Researchers from the University of Wyoming have shown how images, collected by motion-sensor cameras, can be rapidly interpreted by deep neural networks. This has led to a system that can automate animal identification with an accuracy of 99.3 percent of images. When compared to humans (as part of a citizen science project) the artificial intelligence was more accurate (the human volunteers came in with a 96.6 percent accuracy rate). This type of machine learning could be used to assist with nature conservation. Achieving this level of accuracy was not straightforward.

A human heart is an astounding machine that is designed to continually function for up to a century without failure. One of the key ways to measure how well your heart is functioning is to compute its ejection fraction: after your heart relaxes at its diastole to fully fill with blood, what percentage does it pump out upon contracting to its systole? The first step of getting at this metric relies on segmenting (delineating the area of) the ventricles from cardiac images. During my time at the Insight AI Program in NYC, I decided to tackle the right ventricle segmentation challenge from the calls for research hosted by the AI Open Network. I managed to achieve state of the art results with over an order of magnitude less parameters; below is a brief account of how.

When we don't have p_data but a finite training set, we have a ML problem. The latter can be converted back to an optimization problem by replacing p_data with the empirical distribution with p _data obtained from the training set, thereby reducing the empirical risk. Although this might look relatively similar to optimization, there are two main problems. Firstly, ERM is prone to overfitting with the possibility of the dataset being learned by high capacity models (models with the ability to learn extremely complex functions). Secondly, ERM might not be feasible.