Unless you want to watch a video or recall something specific, it will be more convenient (and less scary) to view the content from a bigger display.
So, in case you are interested and ready (i.e know enough) to learn more about Neural Networks & Deep Learning, here are the resources we would recommend you to look at:
For complete beginners in AI
Well, assuming you already know differentiation and have dealt with multivariable functions. Ideally you should also know how to multiply matrices (that is it, nothing more advanced from Linear Algebra). You can understand certain bits without these things (in particular the general idea), but it will be better if you are familiar with those topics.
For the overview of "What is a Neural Network?" and how neural networks learn, I would suggest looking at this playlist from 3Blue1Brown. These are videos only, one hour in length in total, no exercises or anything (but a couple of useful references). Nor you need to know any programming to understand it.
Open PlaylistBeginner-friendly, but basic Python knowledge is assumed
Programming and cutting-edge AI projects are tied together. If for some reason you don't know or don't like programming, but you potentially want to get into AI (or Machine Learning, does not matter), you better change this. There is no reason to hate it or avoid by the way: it is not as difficult as it may seem, and e.g Python is very intuitive, simple and beginner friendly language.
1) A good follow up on the videos above is the Neural Networks and Deep Learning free online book, it is referenced by one of the videos anyway;
2) Though, if you have some experience with Python, you may wanna do this free online course on Practical Deep Learning from FastAI. It is highly recommended by many people from the field (e.g the director of Research at Google), and it is more modern. There are companies that require its junior employees to complete it. It takes a different approach to the above, less mathematical (at least initially). It is much longer though.
More advanced
Given the difficulty of the materials below, it is most suitable for the university students and people who have already graduated. Even though they claim to start from the "basics", it already assumes you know quite a lot.
1) The go-to big book with maths theory behind everything you will most probably ever need is the Probabilistic Machine Learning by Kevin P. Murphy. It is not technically free though... Anyway, foundations of probability theory (though theoretical and advanced), Linear Models, Deep Neural Networks, etc... are all in there;
2) If you are especially interested in the technology behind the ChatGPT and other modern large language models, which is the "Transformers", you may wanna do this free course. It covers other key topics as well. You need to know Python well enough for this one.