Activation Function and Algorithm – Back Propagation Network by Deeba Kannan
In this detailed tutorial, I explained the critical role of activation functions in Backpropagation Networks and their impact on the training process. Activation functions are essential for introducing non-linearity into the model, enabling neural networks to solve complex problems that linear models cannot handle.
Topics Covered:
Introduction to activation functions in neural networks
Explanation of commonly used activation functions: Sigmoid, ReLU, Tanh
How activation functions affect the learning process in Backpropagation Networks
Step-by-step breakdown of the Backpropagation algorithm
Forward propagation vs Backpropagation in neural network training
Gradient descent and its role in optimizing the weights
Challenges with vanishing gradients and how activation functions help mitigate them
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