What is Deep Learning?: An Introduction to Deep Learning

Deep Learning is a subfield of Machine Learning based on artificial neural networks with multiple layers ("deep" networks) that can automatically learn hierarchical representations directly from raw data.

What Makes Deep Learning Different

Traditional ML often requires manual feature engineering — humans decide what characteristics of the data matter. Deep Learning models learn these features automatically through multiple layers of transformation, which is especially powerful for unstructured data like images, audio, and text.

How Neural Networks Work

• Neurons: Basic computational units that apply a weighted sum and an activation function.
• Layers: Neurons are organized into an input layer, hidden layers, and an output layer.
• Weights & Biases: Parameters adjusted during training to minimize prediction error.
• Backpropagation: The algorithm used to update weights based on the error at the output.

Common Deep Learning Architectures

Machine Learning vs Deep Learning

• Data Requirements: Deep Learning typically needs much larger datasets to perform well.
• Feature Engineering: ML often needs manual features; Deep Learning learns them automatically.
• Compute: Deep Learning generally requires GPUs and more computational power.
• Interpretability: Traditional ML models are usually easier to interpret than deep networks.