Detect Steel Plate Defects with ML

The video tutorial covers the process of detecting faults in steel plates using image analysis. It explains the preparation of data, feature selection, and the use of one-hot encoding for classification. The tutorial demonstrates data visualization, outlier detection, and balancing techniques. It then guides through building and training a neural network with Keras, evaluating its performance, and comparing it with other classifiers using SKLearn.

What challenges are associated with classifying multiple types of defects?

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Describe the process of scaling and splitting the data into training and testing sets.

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What role does the softmax function play in the neural network model for defect classification?

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How does the performance of the Keras TensorFlow neural network compare to other classifiers mentioned?

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