When you use the Automatic Network Search option to find the optimal parametrization of your deepnets, the final deepnet is usually composed of multiple networks with different configurations (read the section 4.4.2 Automatic Parameter Optimization of the deepnet document). These configuration parameters were hidden to the user until now. To address the requests from our most technical customers and provide a higher level of interpretability, BigML displays the configuration for each of the networks composing a deepnet created with this automatic option.
Please read the section 22.214.171.124 Summary of the deepnet documentation to learn more
BigML is proud to announce Deepnets, 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 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. Thus, non-experts can train deep learning models with results matching that of top-level data scientists.