Introduction to Soft Computing

(Part 3)

Multi-layer Feed Forward Network:

The multi-layer feed-forward network enhances the single-layer version by adding one or more intermediate layers between the input and output layers. These layers allow the network to learn more complex patterns.

This structure is called a multi-layer feed-forward network and improves computation and model performance for complex tasks in machine learning.

Each of the layers may have a varying number of neurons. For example, the one shown in the above diagram has ‘m’ neurons in the input layer and ‘r’ neurons in the output layer and there is only one hidden layer with ‘n’ neurons.

Recurrent Network:

In feed-forward networks, the signal always flows from the input layer towards the output layer (in one direction only). In the case of recurrent neural networks, there is a feedback loop (from the neurons in the output layer to the input layer neurons). There can be self-loops too.

• Learning Techniques of a Neural Network:

Supervised and Unsupervised learning are the two techniques of machine learning. But both the techniques are used in different scenarios and with different datasets.

Supervised learning :

Supervised learning is a key machine learning method that involves training models using labeled data. In this approach, the model learns to establish a mapping function between input variables (X) and output variables (Y).

This method requires supervision during the training process, similar to how a student learns with the guidance of a teacher. Supervised learning is typically applied to two main types of problems: Classification and Regression.

Example

For instance, consider a dataset containing images of various fruits. The task of the supervised learning model is to identify and classify these fruits accurately. During the training phase, we provide both input data (the images) and corresponding output labels (fruit types).

The model learns to recognize features such as shape, size, color, and texture. After training, we can test the model with a new set of fruit images. The model will then identify the fruits and predict their types using the learned algorithm.

Unsupervised Machine Learning:

Unsupervised learning is a machine learning method that infers patterns from unlabeled input data. The primary goal is to discover the underlying structure and relationships within the data without any supervision. Unlike supervised learning, unsupervised learning does not require labeled data. Instead, it allows the model to analyze the input data independently, identifying patterns and groupings on its own.

Example

To illustrate unsupervised learning, consider the previous example of a dataset containing images of different fruits. In this case, we do not provide any labels or supervision. We simply input the dataset into the model and let it uncover patterns.

Using suitable algorithms, the model analyzes the features of the fruits—such as shape, size, color, and texture—and groups them based on similarities. For instance, it might cluster apples together and separate them from oranges or bananas, based purely on the learned features.

Reinforcement Learning

Understanding Reinforcement Learning in Machine Learning

Reinforcement Learning (RL) is a feedback-based machine learning technique where an agent learns to make decisions by interacting with its environment. The agent performs actions and observes the results, receiving positive feedback for beneficial actions and negative feedback or penalties for harmful ones.

Features

• Learning Without Labels: Unlike supervised learning, RL does not require labeled data. The agent learns purely from its experiences, adapting based on feedback received.
• Sequential Decision Making: RL is particularly effective for problems involving sequential decision-making, such as game playing and robotics. The agent aims for long-term goals rather than immediate rewards.
• Exploration and Exploitation: The agent explores the environment, experimenting with different actions to determine which lead to the best outcomes.

Example

Consider an AI agent navigating a maze with the goal of finding a diamond. As the agent interacts with the maze, it performs actions that change its state, receiving rewards or penalties based on its choices.

Through repeated interactions—taking actions, changing states, and receiving feedback—the agent learns which actions yield positive rewards and which lead to negative penalties. For instance, moving towards the diamond might provide positive points, while hitting a wall could result in a penalty.

The agent’s ability to learn through trial and error enables it to improve its performance over time. Reinforcement learning is a core component of artificial intelligence, allowing agents to adapt and thrive in dynamic environments without explicit programming.