numpy.random.shuffle() function can help us to permute a sequence randomly along the first axis , in this tutorial we will introduce how to use this function correctly.
numpy.random.permutation() can return a sequence randomly, which is very helpful in random two or more sequences. In this tutorial, we will introduce how to use this function correctly.
numpy.full() function can allow us to create an array with given shape and value, in this tutorial, we will introduce how to use this function correctly.
Numpy data type is called dtype, in this tutorial, we will list some common used numpy data types, which is much richer than python standard data types.
numpy.logspace() function can generate geometric series based on base( such as base = 10.0). In this tutorial, we will write some examples to show you how to use this function correctly.
Hilbert matrix is highly ill-conditioned matrix, in this tutorial, we write an python function to generate a hilbert matrix with numpy, you can use this function in your machine learning model.
numpy.newaxis represents a new axis in numpy array, in this tutorial, we will write some examples to help you understand how to use it correctly in python application.
To generate a random integer, we can use python random.randint() and numpy.random.randint(), however, they are different. In this tutorial, we will discuss the difference between them.
Cosine distance is often used as evaluate the similarity of two vectors, the bigger the value is, the more similar between these two vectors. In this tutorial, we will introduce how to calculate the cosine distance between two vectors using numpy, you can refer to our example to learn how to do.
Spearman’s Correlation Coefficient is widely used in deep learning right now, which is very useful to estiment the correlation of two variables. In this tutorial, we will introduce how to calculate spearman’s correlation coefficient.