Artificial Intelligence (AI), Machine Learning (ML), and Deep Learning (DL) are interconnected fields, but they have distinct characteristics:
1. Artificial Intelligence (AI):
– Definition: AI is a broad area of computer science that focuses on creating systems capable of performing tasks that normally require human intelligence. These tasks include decision-making, visual perception, speech recognition, and language translation.
– Scope: AI encompasses a wide range of techniques, from rule-based systems to machine learning.
– Goal: The primary goal is to enable machines to perform tasks that would typically require human intelligence.
2. Machine Learning (ML):
– Definition: ML is a subset of AI focused on the concept that machines can learn from data, identify patterns, and make decisions with minimal human intervention.
– Techniques: It involves various techniques like regression, classification, clustering, and more.
– Characteristic: Unlike traditional software, ML systems improve their performance as they are exposed to more data over time.
3. Deep Learning (DL):
– Definition: DL is a subset of ML based on artificial neural networks with representation learning.
– Structure: It involves networks capable of learning unsupervised from unstructured or unlabeled data.
– Application: DL is particularly known for its use in fields like computer vision and natural language processing, where it can learn from a large amount of data.
AI is the broadest concept, aimed at creating intelligent machines. ML is a specific approach within AI that allows machines to learn from data. Deep Learning is a specialized ML approach that uses complex neural networks. As you move from AI to ML to DL, you go from a broader concept to more specific, technically advanced methodologies.
