The best way to learn is to try it out yourself. Here you'll find our tutorials and use cases ready to be used by you. The tutorials will give you an overview of the platform or will highlight a specific feature.
We recommend you start with the Deploy an operational AI model tutorial. It will give you an overview of the platform's key concepts.
Explore how to build an image classification model that can recognize handwritten numbers. Then deploy and test it.
Build and train an AI model with real data — both numbers and images — using the Peltarion Platform to make it reliable for house price prediction.
Build an image classifier of clothing through creating the typical building blocks of a convolution neural network.
Use BERT to build a text classifier to perform sentiment analysis on movie reviews.
Build a multilingual text classifier that can recognize many different topics.
Use a pretrained snippet in a classification model designed to detect different types of car damage.
Use BERT to build a text classifier for book summaries
Build a image classification model to detect faulty parts in a production line.
Build a simple AI model that use the spreadsheets to solve a classification problem
Solve a multi-label classification problem for content-based automatic music tagging.
Do some DNA research. Use multi-label classification to predict the protein expression rate.
How to solve an image segmentation problem.
Once you have a working model, the next important step is making systematic changes to the design to try to improve the performance.
Kaggle without knowing TensorFlow or PyTorch? In this tutorial we explore how it's possible.
A text similarity end-to-end project to figure out which of the old classic writing style you mirror.
Use an autoencoder to reconstruct noisy images.
Build a no-code app around your AI model.
Build a model which uses audio recording to detect abnormal operation in industrial machinery.
