Occasionally a novel neural network architecture comes along that enables a truly unique way of solving specific deep learning problems. This has certainly been the case with Generative Adversarial Networks (GANs), originally proposed by Ian Goodfellow et al. in a 2014 paper that has been cited more than…Continue
Added by OGE MARQUES on August 13, 2021 at 7:00am — No Comments
Most of what goes by the name of Artificial Intelligence (AI) today is actually based on training and deploying Deep Learning (DL) models. Despite their impressive achievements in fields as diverse as image classification, language translation, complex games (such as Go and chess), speech recognition, and self-driving vehicles, DL models are…Continue
Added by OGE MARQUES on August 5, 2021 at 4:00am — No Comments
The development of machine learning and deep learning solutions typically follows a workflow that starts from the problem definition and goes through the crucial steps of collecting and exploring useful data, training and evaluating candidate models, deploying a solution, and finally documenting and maintaining the system once it is running in the wild (Figure 1). Despite its predictable structure, some…Continue
Added by OGE MARQUES on July 1, 2021 at 6:00am — No Comments
Algorithms have become smarter. They’re learning what websites you frequent, why you typed a certain query and what other search suggestions you’re likely to click. Behind the online results you’re seeing is a highly advanced form of AI: deep…Continue
Added by Zhai CreativoMedia on May 11, 2021 at 9:00pm — No Comments
Bigger is not always better for machine learning. Yet, deep learning models and the datasets on which they’re trained keep expanding, as researchers race to outdo one another while chasing state-of-the-art benchmarks. However groundbreaking they are, the consequences of bigger models are severe for both budgets and the environment alike. For…Continue
Added by Sasa Zelenovic on April 23, 2021 at 8:30am — No Comments
Summary: The annual Burtch Works salary survey tells us a lot about which industries are using the most data scientists and the difference between higher and lower skilled data scientists. Salary increases show us whether demand is increasing, and finally we take a shot at determining which skills are most in demand.
Added by William Vorhies on July 1, 2019 at 8:00am — No Comments
Integrating Pivot Billions with Keras Deep Learning to enhance currency trading models with AI to achieve over 30% net profit in less than 7 months.
Deep Learning has revolutionized the fields of image…
Added by Benjamin Waxer on February 8, 2019 at 5:29am — No Comments
Added by Kyrylo Kolodiazhnyi on January 21, 2019 at 7:44am — No Comments
While a reliable method to accurately identify suicidal patients is missing from medical literature,…Continue
Added by Deena Zaidi on August 15, 2018 at 6:42pm — No Comments
Added by Yoel Zeldes on August 15, 2018 at 10:30am — No Comments
As deep neural networks (DNN) become more powerful, their complexity…Continue
Added by Yoel Zeldes on August 11, 2018 at 9:00am — No Comments
Summary: Before starting to develop an AI strategy, make sure your team understands the limits of what is reasonable today, as well as incremental improvements that might be overlooked. Focus should be on your LOB leaders who understand the business. Make sure they are also able to recognize AI opportunities.
Added by William Vorhies on May 8, 2018 at 9:30am — No Comments
Summary: Deep Learning, based on deep neural nets is launching a thousand ventures but leaving tens of thousands behind. Transfer Learning (TL), a method of reusing previously trained deep neural nets promises to make these applications available to everyone, even those with very little labeled data.
Added by William Vorhies on April 17, 2018 at 12:25pm — No Comments
I’d like to introduce a series of blog posts and their corresponding Python Notebooks gathering notes on the Deep Learning Book from Ian Goodfellow, Yoshua Bengio, and Aaron Courville (2016). The aim of these notebooks is to help beginners/advanced beginners to grasp linear algebra concepts underlying deep learning and machine learning. Acquiring these skills can boost your ability to understand and apply various data science algorithms. In…Continue
Summary: GDPR carries many new data and privacy requirements including a “right to explanation”. On the surface this appears to be similar to US rules for regulated industries. We examine why this is actually a penalty and not a benefit for the individual and offer some insight into the actual wording of the GDPR regulation which also offers some relief.
Summary: Some observations about new major trends and directions in data science drawn from the Strata Data conference in San Jose last week.
Summary: Here are our 6 predictions for data science, machine learning, and AI for 2018. Some are fast track and potentially disruptive, some take the hype off over blown claims and set realistic expectations for the coming year.
Summary: As a profession we do a pretty poor job of agreeing on good naming conventions for really important parts of our professional lives. “Machine Learning” is just the most recent case in point. It’s had a perfectly good definition for a very long time, but now the deep learning folks are trying to hijack the term. Come on folks. Let’s make up our minds.
As a profession we do a pretty poor job of agreeing on good naming conventions…Continue
Summary: The addition of AI capabilities to our personal devices, applications, and even self-driving cars has caused us to take a much deeper look at what we call ‘User Experience’ (Ux). A more analytical framework identified as Cognitive Ergonomics is becoming an important field for data scientists to understand and implement.
Added by William Vorhies on October 31, 2017 at 9:51am — No Comments
Artificial intelligence or AI for short is the field of making computer think like humans by creating an artificial brain. Whatever the human can do intelligently is required to be moved into machines. The machine will just do what the human tells it and no more. For example, the human can sort numbers in an intelligent manner and so machines should be intelligent by sorting numbers like humans. To do this, there are a number of algorithms like bubble sort that allows the machine to think…Continue