Neural Designer implements the most innovative machine learning algorithms in the marketplace.
In this post, we describe some of the features that make Neural Designer unique.
Explainable AI
One of the main difficulties of machine learning models is that it is difficult to understand what they do. Moreover, their predictions are not easy to interpret.
At Neural Designer, we have worked hard to solve these difficulties. Some of the most remarkable results in Explainable AI are the following:
- Algorithm description: The platform provides detailed descriptions of all configuration options and task results, ensuring you have a clear understanding of which algorithms to use and the outputs they produce.
- Model input importance: Identify the variables with the most significant impact on the neural network.
- Sample input importance: Identify the variables that most impact a given prediction.
- Model exploration: Visualize how the outputs of your model vary as a function of the inputs.
Feature selection
Many machine learning applications, from customer targeting to medical diagnosis, arise from complex relationships between features (also called input variables or characteristics).
Feature selection techniques help identify and remove unneeded, irrelevant, and redundant features. Indeed, those variables do not contribute to or reduce the predictive model’s accuracy.
Neural Designer contains the most innovative algorithms to find the optimal architecture for your model:
- Growing inputs: The growing inputs method starts by calculating the correlation of each input with each output variable.
This algorithm begins with the most correlated input and keeps adding well-correlated variables until the model’s accuracy stops increasing. - Genetic algorithm: A stochastic method based on the mechanics of natural genetics and biological evolution. The method evolves populations of models with different input configurations until it finds the most accurate model. Read more.
Computational performance
Neural Designer is the data science and machine learning platform with the highest computational performance in the market.
As a result, you won’t need powerful computers to build your models, and you will maximize your productivity.
These are the main competitive advantages related to computational performance:
- Data capacity: The platform is written entirely in C++, which is known for its memory management capabilities. This allows you to load datasets that are several times larger. Read more.
- Training speed: Neural Designer has been optimized to improve calculation efficiency significantly. It also implements parallelization on CPUs and GPU acceleration. As a consequence, you will train your models much faster. Read more.
- Model accuracy: Neural Designer includes optimization algorithms that use second derivatives to accelerate convergence and improve accuracy, such as the quasi-Newton method and the Levenberg-Marquardt algorithm. That means models with greater accuracy. Read more.
- Energy efficiency: Using fewer computational resources results in energy savings. Read more.
Machine Learning Operations (MLOps)
MLOps is the operationalization of machine learning models.
With Neural Designer, you can export your models in different programming languages (C, Python, PHP, JavaScript) without requiring any external library, and deploy them into your company’s system in the simplest possible way.
Conclusions
Neural Designer is the most advanced data science and machine learning platform on the market.
Neural Designer offers unique features that let you build the most powerful models from your data and deploy them.
