DNN.io: A Web-based Tool for Comprehensive Deep Neural Networks Analytics  (V.1.0 @ 2023)
Principal Investigator (PI) & Developer: Mehdi Ashayeri, Ph.D.
In today's era of big data, machine learning, especially deep learning, is rapidly becoming the go-to approach for predictive modeling. However, the complexity of setting up and training these models can be daunting, especially for those new to the field. DNN.io emerges as a cutting-edge solution for this challenge: a web-based interface that demystifies the process of data modeling through deep neural networks (DNNs).

1. Data Interaction:Users can swiftly upload their datasets and get started right away. DNN.io provides a user-friendly environment to select dependent and independent variables, preparing the data for modeling.
2. Customizable Neural Network Architecture: Modeling isn't a one-size-fits-all operation. DNN.io offers flexible options for users to determine the architecture of the neural network. One can select the number of hidden layers, designate the number of neurons per hidden layer, and even adjust the neural network architecture graphically – a feature that brings a tangible, visual element to abstract computational processes.
3. Advanced Training Controls: DNN.io doesn’t just stop at model setup. The platform provides a suite of tools to optimize the training process. Users can:   - Adjust the number of folds for cross-validation to ensure model robustness.   - Choose among different optimization algorithms to improve the efficiency and accuracy of the training.   - Designate the proportion of data to be used for training and testing, allowing for rigorous model evaluation.
4. Interactive Training Visualization:Model training becomes more interpretable with DNN.io's visual plotting of the training process. Users can get insights into model convergence, optimization, and potential overfitting in real-time.
5. Diagnostic and Interpretation Tools: Understanding a model goes beyond its accuracy score. DNN.io offers a plethora of diagnostic plots, aiding users in model interpretation and validation:   - Sensitivity Analysis: Assess the importance of individual variables in predicting the outcome.   - Variable Importance Plot (Olden method): Get a ranked visual of feature significance.   - Partial Dependence Profiles: Visualize the relation between one to three explanatory variables and the predicted outcome, a critical tool to unravel complex interactions.