Summary of "Activation Functions | Deep Learning Tutorial 8 (Tensorflow Tutorial, Keras & Python)"

Main Ideas and Concepts

Purpose of Activation Functions
Activation functions are crucial for determining whether a neuron in a neural network is firing (activating) or not. They help in transforming the output of neurons to a specific range, which is essential for making classification decisions.
Sigmoid Function
The Sigmoid Function compresses outputs to a range between 0 and 1, making it suitable for binary classification problems. In the context of an insurance dataset, it helps predict whether a person will buy insurance based on features like age and income.
Hidden Layers and Non-linearity
If activation functions are removed from Hidden Layers, the neural network effectively becomes a linear model, which cannot capture complex patterns in data. Non-linear activation functions are necessary to model complex relationships.
Step Function
The step function is an activation function that classifies based on a threshold (e.g., age > 46 for insurance). It has limitations, particularly in Multi-class Classification, where it can lead to ambiguous outputs.
Multi-class Classification
The Sigmoid Function is beneficial for Multi-class Classification as it provides outputs between 0 and 1, allowing for a maximum value selection to determine the predicted class.
Tanh Function
The Tanh Function outputs values between -1 and 1, which centers data around zero and is generally preferred over the Sigmoid Function in Hidden Layers.
Vanishing Gradient Problem
Both sigmoid and tanh functions can lead to slow learning due to the Vanishing Gradient Problem, where derivatives approach zero, hindering backpropagation.
ReLU Function
The ReLU (Rectified Linear Unit) function outputs zero for negative inputs and retains positive inputs, making it computationally efficient and widely used in Hidden Layers. It also suffers from a variant of the Vanishing Gradient Problem.
Leaky ReLU
The Leaky ReLU variant allows a small, non-zero gradient when the input is negative, addressing some of the limitations of standard ReLU.
Choosing Activation Functions
The choice of activation function can depend on the specific problem. Generally, sigmoid is used in output layers for binary classification, while ReLU or Leaky ReLU is preferred in Hidden Layers.

Methodology / Instructions

Choosing Activation Functions
- Use Sigmoid for binary classification in the output layer.
- Use Tanh in Hidden Layers if centering data is beneficial.
- Use ReLU or Leaky ReLU in Hidden Layers for most cases due to their computational efficiency.
Implementation in Python
- Sigmoid: `1 / (1 + exp(-z))`
- Tanh: `(exp(z) - exp(-z)) / (exp(z) + exp(-z))`
- ReLU: `max(0, x)`
- Leaky ReLU: `0.1 * x if x < 0 else x`

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