Author: Zen and the Art of Computer Programming
1 Introduction
With the rapid development of artificial intelligence technology, deep learning is increasingly used in people's lives. Whether it is autonomous driving, video game AI engines, medical diagnosis, image processing and other fields, deep learning plays a vital role. The deep learning model iteratively learns through a large number of data samples during the training process, thereby improving the model accuracy and obtaining better prediction results. However, for many people who are new to deep learning, mastering basic mathematical knowledge is very important to understand the principles of the model and its operation process. Therefore, this article will introduce some basic mathematical knowledge related to deep learning based on personal experience.
2.Basic concepts and terminology
First, we need to understand the following basic concepts and terminology of deep learning.
2.1 Deep Learning
In the 1990s, deep learning became an important research direction in machine learning. It is a machine learning method based on multiple neural network hierarchies. Deep learning refers to a machine learning model composed of multiple layers of neural networks that can make predictions or inferences about input data. The deep learning model can automatically extract features and does not require manual design of complex feature engineering, thus greatly shortening development time. The advantages of deep learning mainly include:
- The model parameter scale is small and easy to deploy to mobile devices or edge devices;
- Data-driven, easy to train, good for generalization performance;
- The model naturally represents abstract features and is insensitive to the distribution of data;
- It is beneficial to solve highly nonlinear learning problems.
With the popularity of deep learning, more and more companies are choosing deep learning as their basic framework for machine learning. For example, Internet companies such as Google, Microsoft, and Facebook have all adopted deep learning technology.
2.2 Activation Function
The activation function is a calculation formula for the output value of a neuron, which is used to perform nonlinear transformation of the input signal. Currently, commonly used