Deep Learning - Artificial Neural Networks with Tensorflow - How Does a Model "Learn"?

The video tutorial explains how models learn using linear regression, focusing on the line of best fit, scatter plots, and error functions. It introduces mean squared error and cost minimization, using calculus to find gradients and solve for parameters. The tutorial covers automatic differentiation with TensorFlow and the basics of gradient descent, including iterative training and epochs. It concludes with a discussion on learning rates and hyperparameters, emphasizing their importance in model training.

What is gradient descent and how does it work in training models?

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How can the learning rate affect the training of a model?

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What is the difference between parameters and hyperparameters in model training?

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Discuss the implications of choosing a learning rate that is too high or too low.

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