Activation Function _ day 11

Activation Functions in Neural Networks Activation Functions in Neural Networks: Why They Matter ? Activation functions are pivotal in neural networks, transforming the input of each neuron to its output signal, thus determining the neuron’s activation level. This process allows neural networks to handle tasks such as image recognition and language processing effectively. The Role of Different Activation Functions Neural networks employ distinct activation functions in their inner and outer layers, customized to the specific requirements of the network: Inner Layers: Functions like ReLU (Rectified Linear Unit) introduce necessary non-linearity, allowing the network to learn complex patterns in the data. Without these functions, neural networks would not be able to model anything beyond simple linear relationships. Outer Layers: Depending on the task, different functions are used. For example, a softmax function is used for multiclass classification to convert the logits to probabilities that sum to one, which are essential for classification tasks. Practical Application Understanding the distinction and application of different activation functions is crucial for designing networks that perform efficiently across various tasks. Neural Network Configuration Example Building a Neural Network for Image Classification This example demonstrates setting up a neural network in Python using TensorFlow/Keras, designed to classify…