Article by Shawn Tan as featured in Global Banking & Finance Review We all know that historically the VC industry has been based on the belief that if one portfolio company goes bust, it doesn’t really matter. The bigger bet is that another portfolio company’s breakout success will override the
Skymind has announced its first cohort of investments from its $800 million AI fund. Skymind has invested in six startups that are leveraging AI to improve the performance of different industries and sectors across the globe. Skymind is an open-source enterprise deep-learning software company and AI ecosystem builder. Over 1000 companies are reportedly using
ISKANDAR PUTERI: Skymind, a developer of artificial intelligence (AI) technology, plans to bring its global experience and change the landscape of cities in Malaysia by developing Skymind Innovation City here. According to the company, the Skymind Innovation City's primary focus is to boost the economic and living standards of a city using AI innovation
Artificial Intelligence is one of the most popular fintech sectors at the moment. With a plethora of potential that even banks and financial institutions are tapping into, AI solutions are dominating the technology world. Shawn Tan is CEO of Skymind, an open-source enterprise deep-learning software firm and an AI ecosystem builder that enables companies and organisations
As AI technology spreads across the globe, new locations are arising as potential hotbeds for the growth and development of AI technology. One of these areas is Southeast Asia, where AI solutions are expected to lead to increased productivity and a further expansion of AI markets. To learn more, we talked to Adam
Shawn Tan (right in pic) and cofounder and COO of Skymind Holdings Berhad, Dr Goh Shu Wei “We want Malaysia to innovate,” says Shawn Tan, “(and) not to just buy software”. This is the sort of lament that is often heard. But when it comes from the cofounder and CEO of Skymind,
Aimed at integrating models with Java applications, Deeplearning4j offers a stack of components for building JVM-based applications that incorporate AI
Content Why Java? Deep Learning for the JVM — Eclipse Deeplearning4J (DL4J) Set up an environment for the first time with DL4J Run DL4J Examples in 3 steps Why Java? Below are three of the fundamental reasons to use Java programming language for Machine Learning operations. 1. Write Once, Run Anywhere Java programming
Document classification is one of the common use cases in the domain of Natural Language Processing (NLP) and well applied in many applications. This example demonstrates document classification with the use case of spam mail filtering. The results show that by using Deep Learning, we can strategically filter out most of the spam emails
In this post, we will build an autoencoder in Deeplearning4j to detect credit card fraud transactions. We will learn how to deal with an unbalanced dataset which is very common in anomaly detection applications. Background Anomaly is a broad term denoting something that deviates from what is standard or expected. Often, identifying an anomaly
