Machine learning is a data analytics technique that teaches computers to do what comes naturally to humans and animals: learn from experience.
In this page, we write some tutorials and examples on machine learning algorithms and applications. You can learn how to use machine learning in life by following our tutorials.
In deep learning, we usually place a dropout layer after a dense layer. However, here is a problem? Dropout layer is placed before or after activation function.
In this tutorial, we will introduce post-norm and pre-norm residual units, they are often used to improve transformer in deep learning.
Dropout and batch normalization are two well-recognized approaches to tackle overfitting in multilayered neural networks, which one is better? In this tutorial, we will discuss this topic.
GE2E loss is proposed in paper << GENERALIZED END-TO-END LOSS FOR SPEAKER VERIFICATION>>. In this tutorial, we will introduce it for some beginners.
In this tutorial, we will introduce how to compute KL (Kullback–Leibler) divergence between two multivariate gaussian distributions.
Accuracy, Precision, Recall & F1-Score are widely used in machine learning. In this tutorial, we will discuss how to compute them.
GELU (GAUSSIAN ERROR LINEAR UNITS) activation function is often used in Bert, GPT-3. In this tutorial, we will introduce it for deep learning beginners.
Einsum is a powerful operation to matrix in deep learning. In this tutorial, we will introduce how to use it for beginners.
In chinese jieba tool, we have not found a list about chinese part-of-speech used in it. In this tutorial, we will introduce you how to get this list.
If you have got the wav fbank feature, you can see it using a heatmap. In this tutorial, we will introduce you how to do.