Understanding Deep Learning

What Is Deep Learning?

Deep learning is a subset of machine learning that uses neural networks with multiple layers to learn complex representations from data.

Traditional machine learning often depends heavily on manually designed features. Deep learning models can learn useful features directly from large amounts of raw or minimally processed data.

Major Types of Neural Networks

Feedforward Neural Networks: Information moves from the input layer toward the output layer. These networks are useful for tabular data and basic classification or regression.

Convolutional Neural Networks (CNNs): Designed to process grid-like data, especially images. CNNs learn spatial patterns such as edges, textures, and shapes.

Recurrent Neural Networks (RNNs): Designed for sequential data, with connections that allow information from earlier steps to influence later steps. They have been used for text, speech, and time-series processing.

Transformers: Use attention mechanisms to model relationships between elements in a sequence. They form the foundation of many modern language models and other generative AI systems.

Deep Learning vs Traditional Machine Learning

Deep learning is not always the best choice. For smaller structured datasets, models such as decision trees or gradient-boosted trees may be more efficient and competitive.