One specific goal of the open source basketball analytics machine learning project is to provide a mini-map of the players. Basically a top-down view of the court with the different players represented as coloured circles. Eventually we could also draw the players movement on the 2D view to detect patterns of basketball plays. Let's have a closer look how this can be accomplished using Python, OpenCV and machine learning libraries. BTW Suggestions and comments are always very welcome to improve this open source project.
Machine learning is a very interesting field in Computer Science that has ranked really high on my to-learn list for a long while now. With so many updates from RxJava, Testing, Android N, Android Studio and other Android goodies, I haven't been able to dedicate time to learn it. I was very excited to discover that Machine Learning can now implemented by anyone in their Android Apps based on the Mobile Vision APIs from Google without needing to have prior knowledge in the field. All you need is to know is how to use APIs. There are a lot of APIs for Machine Learning on the cloud and mobile, but in this series I'm going to focus only on the Mobile Vision APIs since those are created specifically for Android developers.
FILE - In this Sept. 15, 2015, file photo, Greece's Giannis Antetokounmpo looks to shoot as Spain's Pau Gasol, right, defends during a EuroBasket European Basketball Championship quarterfinal in Lille, northern France. Antetokounmpo will try to help Greece earn a spot in the Olympics when it plays in next week's Olympic Qualifying Tournament in Milan, Italy.
We propose Symplectic Recurrent Neural Networks (SRNNs) as learning algorithms that capture the dynamics of physical systems from observed trajectories. An SRNN models the Hamiltonian function of the system by a neural network and furthermore leverages symplectic integration, multiple-step training and initial state optimization to address the challenging numerical issues associated with Hamiltonian systems. We show SRNNs succeed reliably on complex and noisy Hamiltonian systems. We also show how to augment the SRNN integration scheme in order to handle stiff dynamical systems such as bouncing billiards.