BigML’s Summer 2017 Release is here! Join us on Thursday October 5, 2017, at 10:00 AM PDT (Portland, Oregon. GMT -07:00) / 07:00 PM CEST (Valencia, Spain. GMT +02:00) for a FREE live webinar to discover the latest update of the BigML platform. We will be presenting Deepnets, a highly effective supervised learning method that solves classification and regression problems in a way that can match or exceed human performance, especially in domains where effective feature engineering is difficult.
Deepnets, the new resource that we bring to the BigML Dashboard, API and WhizzML, are an optimized version of Deep Neural Networks, the machine-learned models loosely inspired by the neural circuitry of the human brain. Deepnets are state-of-the-art in many important supervised learning applications. To avoid the difficult and time-consuming work of hand-tuning the algorithm, BigML’s unique implementation of Deep Neural Networks offers first-class support for automatic network topology search and parameter optimization. BigML makes it easier for you by searching over all possible networks for your dataset and returning the best network found to solve your problem.
As any other supervised learning model, you need to evaluate the performance of your Deepnets to get an estimate of how good your model will be at making predictions for new data. To do this, prior to training your model, you will need to split your dataset into two different subsets (one for training and the other one for testing). When your Deepnets model is trained, you can use your pre-built test dataset to evaluate its performance and easily interpret the results with BigML’s evaluation comparison tool.
One of the main goals of any BigML resource is making predictions, and Deepnets are no exception. As Deepnets have more than one layer of nodes between the input and the output layers, the output is the network’s prediction: an array of per-class probabilities for classification problems, or a single, real value for regression problems. Moreover, BigML provides a prediction explanation whereby you can request a list of human-readable rules that explain why the network predicted a particular class or value.
Want to know more about Deepnets?
If you have any questions or you’d like to learn more about how Deepnets work, please visit the dedicated release page. It includes a series of six blog posts about Deepnets, the BigML Dashboard and API documentation, the webinar slideshow as well as the full webinar recording.