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Search Results for: machine learning

The Next Wave of Cognitive Analytics: Graph Aware Machine Learning

In this contributed article, editorial consultant Jelani Harper discusses a number of compelling and timely topics including manifold learning, graph embeddings, and cognitive computing.

Why Even Marketers Should Understand the Value of Machine Learning

In this contributed article, Dr. Peter Day, Chief Technology Officer at Quantcast, discusses how leading marketers are making use of widely available machine learning and AI-powered solutions that can do the hard work of spotting signals in the noise. In doing so, they are elevating the quality of their decision making and driving better results.

Best of arXiv.org for AI, Machine Learning, and Deep Learning – August 2021

In this recurring monthly feature, we will filter all the recent research papers appearing in the arXiv.org preprint server for subjects relating to AI, machine learning and deep learning – from disciplines including statistics, mathematics and computer science – and provide you with a useful “best of” list for the month.

How to Organize Data Labeling for Machine Learning: Practical Approaches

In this contributed article, AI and computer vision enthusiast Melanie Johnson discusses how prior organization of data labeling for a machine learning project is key to success. Organizing data labeling for machine learning is not a one sitting job, yet a single error by a data labeler may cost you a fortune. Now, you probably wonder how do I get high-quality datasets without investing so much time and money?

Best of arXiv.org for AI, Machine Learning, and Deep Learning – July 2021

In this recurring monthly feature, we will filter all the recent research papers appearing in the arXiv.org preprint server for subjects relating to AI, machine learning and deep learning – from disciplines including statistics, mathematics and computer science – and provide you with a useful “best of” list for the month.

Book Review: Mathematics for Machine Learning

“Mathematics for Machine Learning” by Marc Peter Deisenroth, A. Aldo Faisal, and Cheng Soon Ong, published by Cambridge University Press, is an excellent way to learn the math behind the models. This review shall highlight all the ways this book is special among the competition. Of all the books I’ve reviewed thus far, this is my favorite. Read on to learn why.

Best of arXiv.org for AI, Machine Learning, and Deep Learning – June 2021

In this recurring monthly feature, we will filter all the recent research papers appearing in the arXiv.org preprint server for subjects relating to AI, machine learning and deep learning – from disciplines including statistics, mathematics and computer science – and provide you with a useful “best of” list for the month.

Video Highlights: Running Effective Machine Learning Teams

In the video presentation below, Niko Laskaris, Data Scientist and Head of Strategic Projects at MLOps solutions provider Comet, hosts a compelling webinar showing you how to address many issues regarding AI/ML. He also shares case studies, and examines the shortfalls of traditional and agile practices applied to ML teams.

The State of AI and Machine Learning

In the 7th edition of its annual State of AI and Machine Learning report, Appen continues to explore the strategies  employed by companies large and small in successfully deploying AI. The reports surveys business  leaders and technical practitioners ( referred to as technologists) alike to understand  their priorities, their successes, and their bottlenecks when it […]

The State of AI and Machine Learning

In the 7th edition of its annual State of AI report, Appen continues to explore the strategies  employed by companies large and small in successfully deploying AI. The reports surveys business  leaders and technical practitioners ( referred to as technologists) alike to understand  their priorities, their successes, and their bottlenecks when it comes to implementing AI.  Collectively, their answers enabled the company to paint a picture of how the AI industry continues to  evolve in a world that is more virtual, more tech-savvy, and more globalized than ever.