Create a computer vision system using decision tree algorithms to solve a real-world problem : Backpropagation Training

The video tutorial introduces backpropagation, a key training strategy for neural networks. It covers the steps of forward propagation, error calculation, and backpropagation, emphasizing the importance of updating weights to minimize error. The tutorial also discusses the role of learning rate in training speed and accuracy, and highlights the need to avoid local minima for optimal network performance. Practical application in Python and Jupyter is mentioned, with a focus on understanding the intuition behind the process.

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