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Dragonfall
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Any suggestions? I'm coming from a math/crypto background.
A neural network is a type of artificial intelligence that is modeled after the human brain. It consists of a network of interconnected nodes, or neurons, that work together to process information and make predictions or decisions.
Neural networks learn through a process called training, where they are exposed to a large amount of data and adjust the connections between neurons to improve their performance on a specific task. This is often done through an algorithm called backpropagation, which calculates the error between the network's output and the desired output and adjusts the weights accordingly.
There are many good intro to neural nets books available, but some popular options include "Neural Networks and Deep Learning" by Michael Nielsen, "Deep Learning" by Yoshua Bengio, Ian Goodfellow, and Aaron Courville, and "Make Your Own Neural Network" by Tariq Rashid.
Some understanding of math and programming is helpful when reading a neural nets book, but many introductory books provide explanations and examples that are accessible to beginners. It may require some additional effort to fully understand the concepts, but it is possible for those without a strong background in math or programming to learn about neural networks.
Yes, there are many online resources available for learning about neural networks. Some popular options include online courses on platforms like Coursera and Udemy, as well as tutorials and articles on websites like Towards Data Science and Medium. Additionally, many universities offer free online courses on neural networks through platforms like edX and Coursera.