Skip to main content

Introduction to ML

What is ML, ANN, and DL?

These terms are often used interchangeably, and they represent distinct concepts within the field of AI.

Fig: Difference between ML, DL and ANN

Machine Learning (ML)

Machine Learning is a subset of Artificial Intelligence (AI) that focuses on enabling computers to learn and make decisions without being explicitly programmed.

It uses data to train models, which can then make predictions or perform tasks.

Artificial Neural Network(ANN)

ANN is the building block of deep learning. They consist of layers of interconnected nodes (neurons) that process and transmit data.

💡
ANNs are inspired by the structure and functioning of the human brain.

It consists of several nodes, such as input, one or more hidden layers, and output. Each hidden nodes are connected to one another based on weights and thresholds.

Key Terms used in ANN -

    • Weights: Connections between neurons that determine the strength of influence.
    • Activation Function: A mathematical function that determines whether a neuron should "fire" or not.

Deep Learning (DL)

💡
Number of hidden layers in Neural Networks determines how 'deep' the model is. If layers exceed more than two, we can consider that as deep learning.

DL is a more advanced subfield of ML that uses deep, complex ANNs to solve sophisticated problems.

It is a subset of ML that utilizes artificial neural networks (ANN) with multiple layers to process complex data.