how to practise music instrument

 The following process uses all free available tools to prepare a piece of music so that one can practice.

1. Generate music XML file.

Use musescore to generate a piece of music and save as music XML file (either compressed or non compressed)

2. Create a free soundslice account and upload the generate music xml file to your account. Doing this step from a computer is a lot easier.

3. Soundslice is a web based application, so there is no mobile app for mobile devices such as android or iOS tablets or phones, so you can go to www.soundslice.com via a browser, then use the share button to add the bookmark onto you homescreen, that will work just like a native app.

4. You can pick any uploaded the piece (slice) to play, you can even mute the sound if you like, so that the app simply read through your music, and you can simply follow it to practice.

Note:

One can also use the tool to convert to music xml file by doing the following:

1. Simply screen capture an image.

2. Convert that image into a pdf file using image viewer

3. Use musescore pdf to music xml file tool to convert to musescore file

4. Edit/correct convertion issues, then export to music xml file.

Machine learning terms

 Graph:

Graphs are data structures to describe relationships and interactions between entities in complex systems. In general, a graph contains a collection of entities called nodes and another collection of interactions between a pair of nodes called edges

Shape:

shape (Tensor)

The number of elements in each dimension of a tensor. The shape is represented as a list of integers. For example, the following two-dimensional tensor has a shape of [3,4]:

[[5, 7, 6, 4],
 [2, 9, 4, 8],
 [3, 6, 5, 1]]

TensorFlow uses row-major (C-style) format to represent the order of dimensions, which is why the shape in TensorFlow is [3,4] rather than [4,3]. In other words, in a two-dimensional TensorFlow Tensor, the shape is [number of rows, number of columns].

gradient

The vector of partial derivatives with respect to all of the independent variables. In machine learning, the gradient is the vector of partial derivatives of the model function. The gradient points in the direction of steepest ascent.