The board will consist of independent advisors with specific skills and contacts in areas of importance for the ongoing development of the mPower suite of mobility services. AI and ML News: Why SMBs Shouldn't Be Afraid of Artificial Intelligence (AI) Charles Martin is considered a leading figure across multiple technology disciplines, with an established track record in machine learning, deep learning, data science, and AI software development, complemented by extensive domain experience in Natural Language Processing (NLP) for Search Relevance (as well as Text Generation and Quantitative Finance). Highly sought after in the software development field, he has personally developed and implemented machine learning (ML) systems at companies including Roche, France Telecom, GoDaddy, Aardvark (Google), eBay, eHow, Walmart, Barclays/BGI, and Blackrock. Much of his recent project work has been done under his advisory firm, Calculation Consulting, which he founded in 2010 to provide data science, machine learning, and deep learning solutions. He has also served as both a consultant and FTE distinguished engineer at GLG, a prestigious international consulting firm, where he developed AI methods for the search and recommendations platform.

The board will consist of independent advisors with specific skills and contacts in areas of importance for the ongoing development of the mPower suite of mobility services. Charles Martin is considered a leading figure across multiple technology disciplines, with an established track record in machine learning, deep learning, data science, and AI software development, complemented by extensive domain experience in Natural Language Processing (NLP) for Search Relevance (as well as Text Generation and Quantitative Finance). Highly sought after in the software development field, he has personally developed and implemented machine learning (ML) systems at companies including Roche, France Telecom, GoDaddy, Aardvark (Google), eBay, eHow, Walmart, Barclays/BGI, and Blackrock. Much of his recent project work has been done under his advisory firm, Calculation Consulting, which he founded in 2010 to provide data science, machine learning, and deep learning solutions. He has also served as both a consultant and FTE distinguished engineer at GLG, a prestigious international consulting firm, where he developed AI methods for the search and recommendations platform.

The weightwatcher tool can detect the signatures of overtraining in specific layers of a pre/trained Deep Neural Networks. In the Figure above, fig (a) is well trained, whereas fig (b) may be over-trained. That orange spike on the far right is the tell-tale clue; it's what we call a Correlation Trap. Weightwatcher can detect the signatures of overtraining in specific layers of a pre/trained Deep Neural Networks. In this post, we show how to use the weightwatcher tool to do this.

It's easy to see the massive rise in popularity for venture investment, conferences, and business-related queries for "machine learning" since 2012 – but most technology executives often have trouble identifying where their business might actually apply machine learning (ML) to business problems. With new AI buzzwords being created weekly, it can seem difficult to get ahold of what applications are viable, and which are hype, hyperbole or hoax. At Emerj, our market research focuses on cutting through the AI hype, and helping innovation and strategy leaders make a better business case for AI. This includes both our AI Opportunity Landscape research with enterprise clients, and our Emerj Plus best-practices guides for consultants and vendors. In this article, we'll break down categories of business problems that are commonly handled by ML, and we'll also provide actionable advice to begin a ML initiative with the right approach and perspective (even it's the first such project you've undertaken at your company).

In this episode, I am with Dr. Charles Martin from Calculation Consulting a machine learning and data science consulting company based in San Francisco. We speak about the nuts and bolts of deep neural networks and some impressive findings about the way they work.