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In order for machine learning systems to work they need to be trained on data. But as the old saying goes “garbage in is garbage out” so you need to make sure you have a dataset of prepared data that is cleaned so you can incrementally train a machine learning model to perform a particular task. And then you need to have a way to measure to make sure the machine learning model is actually learning from that data. In this episode of the AI Today podcast hosts Kathleen Walch and Ron Schmelzer define the terms Training Data, Epoch, Batch, and Learning Curve, explain how these terms relates to AI and why it’s important to know about them.
Show Notes:
- FREE Intro to CPMAI mini course
- CPMAI Training and Certification
- AI Glossary
- Glossary Series: Artificial Intelligence
- AI Glossary Series – Machine Learning, Algorithm, Model
- Glossary Series: (Artificial) Neural Networks, Node (Neuron), Layer
- Glossary Series: Bias, Weight, Activation Function, Convergence, ReLU
- Glossary Series: Perceptron
- Glossary Series: Hidden Layer, Deep Learning
- Glossary Series: Backpropagation, Learning Rate, Optimizer
