Achieving net zero carbon emissions by 2050 requires the integration of increasing amounts of wind power into power grids. This energy source poses a challenge to system operators due to its variability and uncertainty. Therefore, accurate forecasting of wind power is critical for grid operation and system balancing. This paper presents an innovative approach to short-term (1 to 6 hour horizon) wind power forecasting at a national level. The method leverages Automated Deep Learning combined with Numerical Weather Predictions wind speed maps to accurately forecast wind power.

Deep learning, the branch of AI that uses artificial neural networks to build prediction and pattern matching models from large datasets relevant to a particular application, is having a sizable impact on both consumer and enterprise software. Whether for enabling home appliances to understand and respond to vocal commands or identifying hidden patterns endemic to all malware, deep learning algorithms allow machines to mimic and even improve upon human cognition in ways that are impossible with imperative or declarative programming. Unfortunately, developing deep learning software isn't easy since the models are customized for a particular use. Indeed, developing models is more like making a custom-fitted suit, not off-the-rack clothing in standard sizes. Deep learning encompasses a large category of software, not a general-purpose solution, and describes a broad range of algorithms and network types, each better suited to particular types of problems and data than others.

Summary: As the Automated Machine Learning (AML) movement got underway a few years back there was an early branch between proprietary platforms and open source platforms. Since they continue to require fluency in Python or R we label them "professional". As the Automated Machine Learning (AML) movement got underway a few years back there was an early branch between proprietary platforms and open source platforms. Today, the primary difference between these is that the proprietary entries are largely code-free so that citizen data scientists / business analysts can use them in addition to data scientists. The open source versions are still reliant on your ability to code, or at least to copy code.

Summary: Remember when we used to say data is the new oil. Now Training Data is the new oil. Training data is proving to be the single greatest impediment to the wide adoption and creation of deep learning models. We'll discuss current best practice but more importantly new breakthroughs into fully automated image labeling that are proving to be superior even to hand labeling. More and more data scientists are skilled in the deep learning arts of CNNs and RNNs and that's a good thing.

There are several things holding back our use of deep learning methods and chief among them is that they are complicated and hard. Now there are three platforms that offer Automated Deep Learning (ADL) so simple that almost anyone can do it. There are several things holding back our use of deep learning methods and chief among them is that they are complicated and hard. A small percentage of our data science community has chosen the path of learning these new techniques, but it's a major departure both in problem type and technique from the predictive and prescriptive modeling that makes up 90% of what we get paid to do. Artificial intelligence, at least in the true sense of image, video, text, and speech recognition and processing is on everyone's lips but it's still hard to find a data scientist qualified to execute your project.

To test the deep learning model, the team used retrospective data sets from 175 patient who underwent fat-suppressed T2-weighted fast spin-echo MRI. The reference standard for training the CNN classification was based on prior musculoskeletal radiology interpretation of the articular surfaces of the femur and tibia.