How it works
Last updated
Last updated
Visualyze Robot AI uses AutoML and Neural Architecture Search(NAS) to train custom machine learning models on image, text, and video data without writing any code.
For many machine learning projects, choosing a model is one of the complex processes that requires expertise, time, and money. A significant part of creating a deep learning model is trying out different architectures. This requires specialized skills and is challenging in general; It’s a lot of trial and error and the experimentation itself is time-consuming and expensive.
NAS automates the task of finding the suitable neural network architecture by searching for the best neural network architecture for the given data. It allows us to discover architectures far more complicated than what humans may think to try, and these architectures can be optimized for particular goals. NAS has been used to design networks that are on par or outperform hand-designed architectures.[1][2]
AutoML abstracts away all of the complex parts of deep learning - model selection and hyperparameter optimization.
Visualyze RobotAI uses a collection of NAS presets to find the best model for the given problem. The automated architecture search substantially speeds up the development of new deep learning models as developers do not need to painstakingly evaluate different architectures.
[1]Zoph, Barret; Le, Quoc V. (2016-11-04). "Neural Architecture Search with Reinforcement Learning". arXiv:1611.01578 [cs.LG]
[2]Zoph, Barret; Vasudevan, Vijay; Shlens, Jonathon; Le, Quoc V. (2017-07-21). "Learning Transferable Architectures for Scalable Image Recognition". arXiv:1707.07012 [cs.CV].
