But what is a neural network? | Deep learning chapter 1

I'm in the same boat. For long time, I was interested in AI but at the same time intimidated by math. I'm relatively comfortable with discrete mathematics and classical algorithms and at the same time calculus and linear algebra is completely foreign to me. Also, I do not accept way to learn ML without good understanding of core principles behind it. So math is a must. A few months ago, I stumbled upon very amazing YouTube Channel 3Blue1Brown which explains math in very accessible way and at the same time I got feeling that I finally started understanding core ideas behind linear algebra and calculus. Just recently he published 4 videos about deep neural networks: [this video] [link] [link] [link] So my fear of ML was gone away and I'm very excited to explore whole new world for neural networks and other things like support vector machines etc

Essentially a computer neural network is just a lot of addition (and matrix multiplication) of floating point numbers. The parameters are the "strength" or "weights" of the connections between neurons on different layers and the "bias" of each neuron. If neuron Alice is connected to neuron Bob and Alice has a value of 0.7, and the weight of Alice's connection to bob is 0.5, then the value sent from Alice to Bob is 0.35. This value (and the values from all the other incoming connections) are summed at added to the neuron's negative bias. I highly recommend checking out 3blue1brown series on how neural nets, gradient descent, and the dot product (implemented as a matrix multiplication) all tie together: [this video]

If a human brain doesn’t use maths, what does it use? It’s Magic? > "training", more accurately described as references Uh, no, that's not at all how the training process works. The training process very roughly mirrors evolution/natural selection except more more direct and faster. This is a good introduction to the concept of how a computer could learn: [link] This is a more technical look at how exactly it works in more modern AIs: [this video] (it's a series) > If it encounters a problem it hasn't been programmed for, it cannot reason a new solution. But an LLM can? It's not as good at it as a human but it can Relevant: [link] (if you don’t want to click it it says “referring to AI models as "just math" or "matrix multiplication" is as uselessly reductive as referring to tigers as "just biology" or "biochemical reactions"”) > By your logic, video game characters must use reasoning, when no, they don't. It's maths. “By your logic, if tigers are dangerous, then bananas must be too, when no, they aren’t. It’s biology.”