notes.txt

https://vaclavkosar.com/ml/Tokenization-in-Machine-Learning-Explained
Tokenization is cutting input data into meaningful parts that can be *embedded* into a vector space.latent vector:
    -embedding tokens: map tokens to their representations
    --input tokenized
    --tokens embedded
    -Step by step pool the sequences of embeddings into shorter sequences, 
    until we get a single full contextual data representation for the output.

quantize: https://en.wikipedia.org/wiki/Vector_quantization
- It is done by finding the nearest group with the data dimensions available, 
then predicting the result based on the values for the missing dimensions, 
assuming that they will have the same value as the group's centroid.
---digital music processing technology, quantization is the studio-software process of transforming performed musical notes, 
which may have some imprecision due to expressive performance, 
to an underlying musical representation that eliminates the imprecision. 
The process results in notes being set on beats and on exact fractions of beats.
---Quantization, in mathematics and digital signal processing, 
is the process of mapping input values from a large set (often a continuous set) to output values in a (countable) smaller set, 
often with a finite number of elements. 
Rounding and truncation are typical examples of quantization processes. 
Quantization is involved to some degree in nearly all digital signal processing, 
as the process of representing a signal in digital form ordinarily involves rounding. 
Quantization also forms the core of essentially all lossy compression algorithms. 
---Vector quantization (VQ) is a classical quantization technique from signal processing
that allows the modeling of probability density functions by the distribution of prototype vectors. 
It was originally used for data compression.
It works by dividing a large set of points (vectors) into groups having approximately the same number of points closest to them. Each group is represented by its centroid point, as in k-means and some other clustering algorithms. 

https://www.dsprelated.com/freebooks/sasp/Choice_Hop_Size.html
https://towardsdatascience.com/all-you-need-to-know-to-start-speech-processing-with-deep-learning-102c916edf62
hop lengths-
length of non-intersecting portion of window length.

codebook collapse,
wherein all encodings get mapped to a single or few em-
bedding vectors while the other embedding vectors in the
codebook are not used, reducing the information capacity
of the bottleneck

spectral components-is any of the waves that range outside the interval of frequencies assigned to a signal.

https://cs.stackexchange.com/questions/76647/what-is-meant-by-the-term-prior-in-machine-learning
learn a prior- learn some initial parameters/pairings about the data 
https://www.lesswrong.com/posts/SL9mKhgdmDKXmxwE4/learning-the-prior