Neural Networks from Scratch in Python by Harrison Kinsley, Daniel Kukie a

Chapter 2 - Coding Our First Neurons - Neural Networks from Scratch in Python
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Anim 2.13:​ ​https://nnfs.io/lqw
Recall that in Python, and in our case, lists are useful containers for holding a sample as well
as multiple samples that make up a batch of observations. Such an example of a batch of
observations, each with its own sample, looks like:
inputs ​= ​[[​1​, ​2​, ​3​, ​2.5​], [​2​, ​5​, ​-​1​, ​2​], [​-​1.5​, ​2.7​, ​3.3​, ​-​0.8​]]
This list of lists could be made into an array since it is homologous. Note that each “list” in this
larger list is a sample representing a feature set. ​[​1​, ​2​, ​3​, ​2.5​]​, ​[​2​, ​5​, ​-​1​, ​2​]​, and
[​-​1.5​, ​2.7​, ​3.3​, ​-​0.8​]​ are all ​samples​, and are also referred to as ​feature set instances ​or
observations​.
We have a matrix of inputs and a matrix of weights now, and we need to perform the dot product
on them somehow, but how and what will the result be? Similarly, as we performed a dot product
on a matrix and a vector, we treated the matrix as a list of vectors, resulting in a list of dot
products. In this example, we need to manage both matrices as lists of vectors and perform dot
products on all of them in all combinations, resulting in a list of lists of outputs, or a matrix; this
operation is called the ​matrix product​.