Learn the complete machine learning workflow in Neural Designer, from choosing a model type and preparing data to training, testing, and deployment. Each chapter can be read independently, or followed in order as a structured tutorial.
Starting in the middle? Every chapter includes the context and navigation needed to continue. If this is your first visit, begin with Chapter 1.
Seven chapters
1Model typesChoose the machine learning task and model family that match your problem.2Data setPrepare variables, samples, partitions, and transformations for modeling.3Neural networkDefine the architecture, layers, inputs, outputs, and parameters.4Training strategySelect the loss, optimizer, stopping criteria, and validation controls.5Model selectionIdentify the architecture with the best generalization.6Testing analysisEvaluate performance on data that was not used during training.7Model deploymentGenerate predictions, inspect responses, and export the model.ASelected bibliographyContinue with books and research references used throughout the tutorial.
Follow along in Neural Designer
Download Neural Designer to apply each chapter to your own data as you progress through the tutorial.