Fundamentals of Neural Networks - Backward Propagation

The video tutorial covers the basics of neural networks, focusing on the flow of information from input to output layers. It introduces backward propagation, explaining the gradient descent algorithm used for optimization. The tutorial discusses the loss function, particularly mean square error, and draws an analogy to ordinary least squares (OLS) in linear regression. It details the steps of gradient descent, emphasizing the importance of the learning rate (ETA) and the challenges of exploding and vanishing gradients. The tutorial aims to provide a foundational understanding of these concepts for effective neural network training.

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