Tag: Deep Learning

Feed: Featured Blog Posts - Data Science Central. Author: Rahul Agarwal. Kaggle is an excellent place for learning. And I learned a lot of things from the recently concluded competition on Quora Insincere questions classification in which I got a rank of 182/4037. In this post, I will try to provide a summary of the things I tried. I will also try to summarize the ideas which I missed but were a part of other winning solutions. As a side note: if you want to know more about NLP, I would like to recommend this awesome course on Natural Language ... Read More

Feed: Featured Blog Posts - Data Science Central. Author: Andrea Manero-Bastin. Quora contribution written by Chomba Bupe. I am actually not even aware of any machine learning (ML) problem that is considered to have been solved recently or in the past. This tells you a lot about how hard things really are in ML. Of course, if you read media outlets, it may seem like researchers are sweeping the floor clean with deep learning (DL), solving ML problems one after the other leaving no stones unturned. In reality, they are not, researchers actually attack relatively simpler problems in the hope ... Read More

Feed: All - O'Reilly Media. Author: Olga Troyanskaya. This is a keynote highlight from the O'Reilly Artificial Intelligence Conference in New York 2019. Watch the full version of this keynote on the O'Reilly online learning platform. You can also see other highlights from the event. Article image: DNA (source: Pixabay). Olga Troyanskaya is a professor at the Lewis-Sigler Institute for Integrative Genomics and the Department of Computer Science at Princeton University and deputy director for genomics at the Flatiron Institute of the Simons Foundation. Her group is focused on developing machine learning methods to address cutting edge problems in genomics ... Read More

Feed: All - O'Reilly Media. Author: Ruchir Puri. This is a keynote from the O'Reilly Artificial Intelligence Conference in New York 2019. See other highlights from the event. This keynote was sponsored by IBM Watson. Article image: Clockwork (source: Pixabay). Ruchir Puri is an IBM Fellow and the Chief Architect of IBM Watson. Dr. Puri led Deep Learning and Machine Learning Platform Initiative at IBM Research and also led IBM's efforts in software-hardware acceleration for cognitive and analytic workloads and drove strategy for differentiated cognitive computing infrastructure. He is a Fellow of the IEEE, an ACM Distinguished Speaker, an IEEE ... Read More

Feed: All - O'Reilly Media. Author: Carlos Humberto Morales. This is a keynote highlight from the O'Reilly Artificial Intelligence Conference in New York 2019. Watch the full version of this keynote on the O'Reilly online learning platform. You can also see other highlights from the event. Article image: Blocks (source: Pixabay). Carlos Humberto Morales is the senior director of deep learning systems at Intel, where he obsesses over making AI development easier and more available. Previously, Carlos was Nervana’s platform architect and an architect for Cisco Systems, where he focused on topics such as cross-platform infrastructure security and fog computing ... Read More

Feed: SAS Blogs. Author: Sundeep Tengur. The financial services industry is under continued pressure to create more convenient ways of paying for goods and services. This trend goes beyond common hype. It’s a cultural shift from long-standing payment methods traditionally defined by industry heavyweights to embracing market demands and delivering customer-centricity. With digitisation, new market entrants and the creation of new channels, such as PSD2, there is increased competition to acquire new market share, retain existing business and, most importantly, to remain relevant. The volume of transactions is incidentally growing at an exponential rate worldwide, fuelled by digitisation, new fintech ... Read More

Feed: R-bloggers. Author: Open Data Science ODSC Team. R programmers are not necessary data scientists, but rather software engineers. We have an entirely new multitrack focus area that helps engineers learn AI skills – AI for Engineers. This focus area is designed specifically to help programmers get familiar with AI-driven software that utilizes deep learning and machine learning models to enable conversational AI, autonomous machines, machine vision, and other AI technologies that require serious engineering. For example, some use R in reinforcement learning – a popular topic and arguably one of the best techniques for sequential decision making and control ... Read More

Feed: Featured Blog Posts - Data Science Central. Author: Vincent Granville. This resource is part of a series on specific topics related to data science: regression, clustering, neural networks, deep learning, decision trees, ensembles, correlation, Python, R, Tensorflow, SVM, data reduction, feature selection, experimental design, cross-validation, model fitting, and many more. To keep receiving these articles, sign up on DSC.  34 Great Articles and Tutorials on Clustering How to Automatically Determine the Number of Clusters in your Data  Scale-Invariant Clustering and Regression  Steps to calculate centroids in cluster using K-means clustering algorithm  Classification and Regression Trees  K-nn Clustering Explained in ... Read More

Feed: Featured Blog Posts - Data Science Central. Author: Michał Frącek. In February 2019 Polish government added an amendment to a banking law that gives a customer a right to receive an explanation in case of a negative credit decision. It’s one of the direct consequences of implementing GDPR in EU. This means that a bank needs to be able to explain why the loan wasn’t granted if the decision process was automatic. In October 2018 world headlines reported about Amazon AI recruiting tool that favored men. Amazon’s model was trained on biased data that were skewed towards male candidates. It has ... Read More

Feed: Featured Blog Posts - Data Science Central. Author: Bill Schmarzo. Describing the Data Science analytics development process has always been a struggle for me.  The corporate world is full of linear processes.  Major monolithic enterprise applications – ERP, MRP, SFA, CRM systems – have been architected to reduce operational complexity via a series of interlocking, heavily-engineered (and re-engineered) linear processes (though I suspect that anyone who has survived an SAP ERP implementation would take issue with my “reduce operational complexity” statement). The data science development process could not be more different.  The Data Science development process is marked by ... Read More