The architecture of Neuton is not based on any existing solutions. The Neuton engine was designed, developed and implemented exclusively by Bell Integrator scientists and engineers.
Neuton requires fewer training samples than traditional neural networks or classical non-neural algorithms (xgboost, RF, etc.).
A Neuton trained model is 10-100 times smaller in size than that of other trained neural networks and traditional non-neural algorithms.
The resulting models are faster than those built by competitors.
Our benchmark results show that in the vast majority of cases, Neuton achieves a higher level of accuracy than algorithms traditionally used for regression and classification problems.
Neuton automatically resolves the issue of retraining (No overfitting).
Neuton is designed and optimized to be utilized across multiple user demographics, even those who do not have special skills in machine learning.
Once your resulting model is trained, customers are allowed to validate the results via our workplace. The resulting number of neurons and coefficients in the trained model is evident of how compact Neuton models are. In addition, one can also compare the size in Kb of Neuton Models versus resulting models from its' competitors.
If in a small dataset the simulated pattern is fully reflected, Neuton will definitely be able to build an optimal model that can be successfully extrapolated to a large sample, even while for most other algorithms this will not be enough. They tend to work successfully only on large datasets. However, if there is no regularity in data than no algorithm, including Neuton, can succeed in building a model.
Yes. The resulting models are very small in size. They are at least 10-100 times smaller than traditional models, easily making Neuton the best option for implementation in small microcontrollers.
Neuton works equally effectively with both very small and (very) large data sets, and everything in-between.
The trained model, unloaded from the platform, contains all information about coefficients, weights and other indicators, obtained during the training. With all trained models we offer scripts for calculating predictions in python, which can be easily combined with other algorithms
Neuton is an automated ML solution, which excludes coding in Jupyter notebooks or any other coding infrastructure, and the results are quite disruptive. Neuton automatically selects hyperparameters for training the model.
Neuton is an AutoML solution that does not require human participation in the model building process. Neuton model construction allows for automatically growing a neural network to the optimal size in the learning process to achieve maximum accuracy. Therefore, the adjustment of neurons and layer numbers is not required. However user may limit the maximum number of neurons.
Neuton employs a linear workflow approach allowing automation of the after-training process of models easily.
Train and validation sets can be either uploaded by user separately, or the only train set can be uploaded to be automatically split by the platform into train and validation sets (80/20). However, test set must be uploaded to the platform separately.
Yes, our Enterprise solution works on Customer's premises.
Yes. We support a wide range of data sources and connectors (e.g. SQL, Spark, Hadoop)
Yes, Neuton can be deployed in a public cloud, private cloud or the enterprise.
In regard to the control options:
Yes, you can stop the training if you are comfortable with the displayed validation metrics;
You can choose the metric you want to maximize/minimize;
You can control the model size by limiting the number of neurons/coefficients;
You can limit the training time from 1 to 1440 minutes.
Yes, you can get predictions on a whole CSV file and download the CSV file to disk, selecting what columns from the test set will be downloaded with the prediction, for example an ID.
There is no row number limit in the CSV files.
The current Neuton release supports perceptron and solves the problems of classification and regression. CNN / RNN support is planned for working with images / sound / text in the following versions.
Neuton supports CNN and RNN and is scheduled for an upcoming release.
Yes, Bell Integrator has worldwide offices and can provide support through various means of communication.
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