In this blog post, we'll explore what deep learning is, its fundamental concepts, and the various key terms.
What is Deep Learning?
Deep learning is a subset branch of machine learning, a subset of AI. It involves using artificial neural networks—computational models inspired by the human brain—to analyze and interpret data. Unlike traditional machine learning, which often relies on manual feature extraction, deep learning automatically learns to represent data through multiple layers of abstraction.
The core idea behind deep learning is to build a neural network consisting of layers of interconnected nodes (neurons). Each layer captures specific features of the input data, and as data passes through the input layers, hidden layers, and output layers, the network learns increasingly complex representations. This layered structure enables deep learning models to excel at tasks that involve large amounts of unstructured data, such as images, audio, and text.
Key Terms in Deep Learning
Neurons: The fundamental units of a neural network. Neurons receive inputs, apply a mathematical operation, and pass the result to the next layer.
Layers: Groups of neurons. Neural networks consist of an input layer (receiving the data), one or more hidden layers (processing the data), and an output layer (producing the final result).
Activation Functions: Functions applied to neurons' outputs to introduce non-linearity, allowing the network to learn complex patterns. Common activation functions include ReLU, Sigmoid, and Tanh.
Weights and Biases: Parameters are adjusted during training to minimize the error model. Weights determine the strength of connections between neurons, while biases allow the model to shift the activation function.
Backpropagation: A training process where the network's error is calculated and propagated backward through the layers, adjusting the weights to improve accuracy.