Rule-based vision systems and deep learning vision software definely are two different approaches to solving problems in the field of artificial intelligence and machine learning.
Rule-based vision systems operate using explicit, predefined rules and logic. These rules are typically created by human experts and are based on if-then-else statements or logical conditions.
Rule-based systems do not involve learning from data. Instead, they rely on the knowledge encoded in the rules to make decisions or perform tasks. These systems are static and do not adapt unless the rules are manually updated.
Deep learning software employs neural networks, which are composed of interconnected nodes (neurons) organized in layers. These networks are inspired by the structure of the human brain and can automatically extract features and patterns from data.
Deep learning models learn from large datasets through a process called training. During training, the model adjusts the weights and parameters of its neurons to minimize the error between predicted and actual outcomes. This process allows them to generalize from the training data and make predictions or perform tasks on new, unseen data.
Deep learning models can learn complex patterns, making them suitable for tasks like image recognition, natural language processing, and speech recognition.
Need an advice for product inspection? PM.
#cognex #codetrace #vision
Machine Learning? Deep Learning? Edge Learning? What do all these terms mean and how are they used in the context of machine vision and factory automation?
Watch as Reto Wyss, VP of AI Technology, walks us through these concepts and explains the power behind example-based artificial intelligence.
Watch the full video here: https://loom.ly/4Oq_kyQ
#ai #deeplearning #edgelearning #machinelearning
To learn more, visit https://loom.ly/yIImjqk
#edgelearning #AI #factoryautomation
