The interaction between deep learning and neural networks

Neural networks and deep learning are closely related, but different, like two sides of a coin.

Neural Network

Neural network is similar to the human brain, which is composed of many highly interconnected processing neurons. These neurons work together to solve complex machine learning problems in a highly sensitive way, giving us breakthrough advances.

The basic computing unit in a neural network is the neuron, which receives input and processes it through multiple neurons in multiple hidden layers, and finally generates an output through the output layer. In machine learning, the common model of neural networks is inspired by biological neurons. This model refers to a single-layer neural network with only one output.

Neural networks achieve convergence through forward propagation, calculation loss, back propagation, gradient descent, etc. It's called an artificial neural network (ANN) and is the basis of deep learning.

Deep Learning

Deep learning is an algorithm that uses deep neural networks to train complex data and predict output through multiple hidden layers and nodes. This algorithm can mimic the way the human brain works.

When traditional machine learning processes structured data, it can make predictions by completing feature engineering in advance, while deep learning performs both manual feature engineering and self-learning. As a result, deep learning is more efficient and can improve over time.

Deep learning plays a central role in research in today’s digital age. However, to fully realize deep learning, neural networks are indispensable. These algorithms are modeled after the human brain and nervous system, providing a huge aid to the broader deep learning process. Therefore, neural networks and deep learning are inseparable.

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