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10:31 AM
A new tag with five questions.
11
Q: Abstract mathematical concepts/tools appeared in machine learning research

KhashFI am interested in knowing about abstract mathematical concepts, tools or methods that have come up in theoretical machine learning. By "abstract" I mean something that is not immediately related to that realm. For instance, a concept from mathematical optimization does not qualify since optimiza...

1
Q: Neural network quantization and arithmetic on affine functions

anirudhI'm trying to understand the basics of quantization in Neural networks. Quantization tries to convert a neural network that uses floating point arithmetic to one that uses a lower precision integer arithmetic (mostly during inference). While searching around about it, I ran into the Gemmlow docum...

4
Q: Difference between deep neural networks and expectation maximization algorithm

Manfred WeisHaving had a short encounter with deep neural networks, it seems to boil down to the task of determining the values of a vast amount of parameters. The expectation maximization algorithm, of which I only learned recently, deals with similar problem, namely determining the values of a vast amount ...

28
Q: Mathematics for machine learning

marcosdecarvalhoI would like to know what mathematics topics are the most important to learn before actually studying the theory on neural networks. I ask that because I will start to learn about neural networks and machine learning on my own to help in the analysis I am doing on my PhD about patterns of genome...

15
Q: Neural networks over gadgets other than $\mathbb{R}$

xuq01Recently, I learned that neural networks (NN) can be defined over fields other than $\mathbb{R}$: for example, Khrennikov and Tirozzi wrote a paper in 1999 (!) on $p$-adic neural networks, or neural networks over $p$-adic fields. It seems that there are some applications towards $p$-adic dynamica...

A neural network is a group of interconnected units called neurons that send signals to one another. Neurons can be either biological cells or mathematical models. While individual neurons are simple, many of them together in a network can perform complex tasks. There are two main types of neural network. In neuroscience, a biological neural network is a physical structure found in brains and complex nervous systems – a population of nerve cells connected by synapses. In machine learning, an artificial neural network is a mathematical model used to approximate nonlinear functions. Artificial neural...
 

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