What is the difference between deep learning and usual machine learning?

Machine Learning (ML): ML algorithms rely on various statistical methods and mathematical models to learn from data. These models can be relatively simple, like linear regression or decision trees, or more complex like support vector machines.

Deep Learning (DL): DL is a subfield of ML that uses artificial neural networks (ANNs) with multiple layers to process information. Inspired by the structure of the human brain, these ANNs learn intricate patterns from data by passing it through these layers.

Data Requirements:

ML: ML algorithms typically perform well with smaller datasets, especially when the data is well-structured and labeled.

DL: Deep learning models often require vast amounts of data to train effectively. This is because the complex ANNs need a lot of information to identify subtle patterns and relationships.

Human Intervention:

ML: Traditional machine learning algorithms often involve more human effort in feature engineering. This means a data scientist needs to identify and extract the relevant features from the raw data that the model can learn from.

DL: Deep learning can sometimes automate feature extraction through its layered architecture. This allows the model to learn features directly from the data itself, reducing the need for manual feature engineering.

Applications:

ML: ML excels at tasks with well-defined rules and patterns, like spam filtering, fraud detection, or stock price prediction (based on historical data).

DL: Deep learning is particularly powerful for complex tasks involving unstructured data, such as image recognition (facial recognition, self-driving cars), natural language processing (machine translation, chatbots), and even creative applications like music generation.