We are happy to share that Deepnets are fully implemented on our platform and available from the BigML Dashboard, API, as well as from WhizzML for its automation.
BigML Deepnets are the world’s first deep learning algorithm capable of Automatic Network Detection and Structure Search, which automatically optimize model performance and minimize the need for expensive specialists to drive business value. Following our mission to make Machine Learning beautifully simple for everyone, BigML now offers the very first service that enables non-experts to use deep learning with results matching that of top-level data scientists. BigML’s extensive benchmark conducted on 50+ datasets has shown Deepnets, an optimized version of Deep Neural Networks brought to the BigML platform, to outperform other algorithms offered by popular Machine Learning libraries. With nothing to install, nothing to configure, and no need to specify a neural network structure, anyone can use BigML’s Deepnets to transform raw data into valuable business insights.
Special thanks to all webinar attendees who joined the BigML Team yesterday during the official launch. For those who missed the live webinar, you can watch the full recording on the BigML YouTube channel.
As explained in the video, one of the main complexities of deep learning is that a Machine Learning expert is required to find the best network structure for each problem. This can often be a tedious trial-and-error process that can take from days to weeks. To combat these challenges and make deep learning accessible for everyone, BigML now enables practitioners to find the best network for their data without having to write custom code or hand-tune parameters. We make this possible with two unique parameter optimization options: Automatic Network Search and Structure Suggestion.
BigML’s Automatic Network Search conducts an intelligent guided search over the space of possible networks to find suitable configurations for your dataset. The final Deepnet will use the top networks found in this search to make predictions. This capability yields a better model, however, it takes longer since the algorithm conducts an extensive search for the best solution. It’s ideal for use cases that justify the incremental wait for optimal Deepnet performance. On the other hand, BigML’s Structure Suggestion only takes nominally longer than training a single network. This option is capable of swiftly recommending a neural network structure that is optimized to work well with your particular dataset.
For further learning on Deepnets, please visit our dedicated summer 2017 release page, where you will find:
- The slides used during the webinar.
- The detailed documentation to learn how to create and evaluate your Deepnets, and interpret the results before making predictions from the BigML Dashboard and the BigML API.
- The series of six blog posts that gradually explain Deepnets.
Thanks for your positive comments after the webinar. And remember that you can always reach out to us at email@example.com for any suggestions or questions.