What is deep learning in ML?

Deep learning is a subset of machine learning that focuses on training complex neural networks to learn patterns from data. It involves processing and analyzing large amounts of data to make accurate predictions and classifications.

Neural Networks

Neural networks are the building blocks of deep learning algorithms. They are interconnected layers of nodes, inspired by the structure of the human brain, that process input data and produce output. Neural networks learn from data through a process called training, where the network adjusts its weights to minimize errors.

Layers

Deep learning models consist of multiple layers that transform the input data through a series of mathematical operations. Each layer extracts different features from the data, allowing the network to learn complex patterns and relationships.

Activations

Activation functions are used in neural networks to introduce non-linearities, enabling the network to learn complex functions. Common activation functions include ReLU, sigmoid, and tanh, which help the network model more intricate relationships in the data.

Algorithms

Deep learning algorithms, such as convolutional neural networks (CNNs) and recurrent neural networks (RNNs), are designed to handle different types of data and tasks. These algorithms are capable of processing and analyzing vast amounts of data efficiently to make accurate predictions and classifications.

Overall, deep learning in machine learning provides powerful tools for processing complex data and extracting meaningful insights to drive decision-making and innovation across various industries.