Intelligent Algorithms - Lecture 2

Intelligent Algorithms - Lecture 2

• Artificial Neural Networks (ANNs) are information processing technologies inspired by the human brain.
• ANNs mimic biological neural networks' processes.
• Neurons form networks, enabling learning, memorizing, and revealing relationships between data.

Elements of a Neural Network

• Input Layer: Accepts input features from the external environment. No computations are performed; nodes transmit features to the hidden layer.
• Hidden Layer: Nodes in this layer are internal to the network's structure. They are not directly exposed to the outside world. Computations are performed here.
• Output Layer: This layer delivers the network's learned information to the outside world.

Activation Functions

• Activation functions transform weighted sums of inputs into outputs for each node in a layer.
• Common activation functions in ANNs include sigmoid, tanh, ReLU, and softmax.

Sigmoid Function

• A logistic function frequently used in deep learning.
• Output values range from 0 to 1.
• In binary classification, values below 0.5 are labeled as 0, and values above as 1.

Tanh Function

• A hyperbolic tangent function.
• Returns values between -1 and 1.

ReLU Function

• Rectified Linear Unit.
• A linear activation function.
• Outputs range from 0 to infinity (for positive input values).

Neural Network Parameters

• Weights: Learnable parameters in machine learning, updated for accurate predictions.
• Bias: Learnable parameters that are added to the weighted inputs.
• Neuron activation: A network node calculates a value based on an input value and weights. The activation function then transforms this to another value, the final neuron output.

Activation Function Choice

• The activation function used in the output layer depends on the problem type (classification or regression).
• Binary classification often uses sigmoid activation, while multi-class problems might use softmax.

Neuron Types

• Single-input neuron: A neuron with one input value.
• Multiple-input neuron: Neurons with multiple input values, each value adjusted by its own weight value.

Neural Network Structure

• Single layer: A single layer of neurons processes input data directly.
• Three-layer network: A more complex network design with connections between layers, allowing for non-linear relationships to be learned in data.

Importance of Neural Networks

• Neural networks are fundamental to modern machine learning.
• They are found in various applications, including image recognition, natural language processing, medicine, and self-driving cars.
• ANNs are crucial for classification and regression tasks.

Types of Problems

• Regression: The target output is a real number or vector of real numbers (e.g., stock prices, temperatures).
• Classification: The target output is a class label, often a choice between 0 and 1, or multiple possibilities (e.g., image recognition, spam detection).

Example Applications

• Pattern recognition.
• Anomalous event identification.
• Predictive modeling.

ANN Building Blocks

• Network topology: The arrangement of neurons and connections.
• Weight adjustments (learning): Process of updating weights to improve predictions.
• Activation functions: Used to control the output values from each neuron.

Network Topologies

• Feedforward: Input to output without loops.
• Perceptron/Multilayer Perceptron: Basic and advanced linear network models.
• Radial basis functions: Employ radial basis functions to define relationships between nodes.
• Recurrent networks, Modular networks, Convolutional, and Deconvolutional networks are also used in ANN.

This quiz explores fundamental concepts of Artificial Neural Networks (ANNs), inspired by biological processes. Key elements include the structure of neural networks, layers, and activation functions like sigmoid and ReLU. Test your understanding of how these elements contribute to learning and data processing.