A neural network is a learning model that makes decisions similar to how the human brain does. It uses nodes to mimic the way biological neurons work together to identify patterns and solve problems. Deep learning refers to a system that uses multiple neural network layers. Here is a basic explanation of how neural networks function:
1. Data is inputted into the system. 
2. A large mathematical equation, or algorithm, organizes the data along a series of nodes and pathways.
3. Next, a human enters a question or a prompt.
4. Finally, the system uses the algorithm to pull bits of data from the neural networks, reorganizes them, and generates an output.
When text is involved, these systems are known as large language models (LLM). An LLM is a deep learning algorithm that can recognize, summarize, translate, predict, and generate text and other forms of content based on knowledge gained from massive datasets. The system determines the relationships among the data rather than having human involvement.
Why Machines Learn
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A rich, narrative explanation of the mathematics that has brought us machine learning and the ongoing explosion of artificial intelligence Machine learning systems are making life-altering decisions for us: approving mortgage loans, determining whether a tumor is cancerous, or deciding if someone gets bail. They now influence developments and discoveries in chemistry, biology, and physics--the study of genomes, extrasolar planets, even the intricacies of quantum systems. And all this before large language models such as ChatGPT came on the scene. We are living through a revolution in machine learning-powered AI that shows no signs of slowing down. This technology is based on relatively simple mathematical ideas, some of which go back centuries, including linear algebra and calculus, the stuff of seventeenth- and eighteenth-century mathematics. It took the birth and advancement of computer science and the kindling of 1990s computer chips designed for video games to ignite the explosion of AI that we see today. In this enlightening book, Anil Ananthaswamy explains the fundamental math behind machine learning, while suggesting intriguing links between artificial and natural intelligence. Might the same math underpin them both? As Ananthaswamy resonantly concludes, to make safe and effective use of artificial intelligence, we need to understand its profound capabilities and limitations, the clues to which lie in the math that makes machine learning possible.
LinkedIn Learning offers many videos and courses about neural networks. Consider the following courses:
Udemy provides patrons with opportunities to take many courses about neural networks. Here are just a few of the options: