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 order to evaluate a voiceprint recognition model, we need compute eer metric. In this tutorial, we will introduce some metrics TPR, FPR, FAR, FRR to help you understand how to compute ERR.
In machine learning, we may see these metrics: TPR, FPR, Precision and Recall metrics. In this tutorial, we will introduce their relation.
When we are using python to process audio, we may find audio amplitude and power spectrogram, what are them and how to understand them? In this tutorial, we will introduce them to you.
MelSpec, FBank and MFCC can be used as an audio feature in deep learning. What is the difference among them? In this tutorial, we will introduce it for you.
When we are building speech recognition system, we have faced 1:N and 1:1 recognition. In this tutorial, we will introduce the their difference.
ArcFace loss is widely used in face recognition and image classification. In this tutorial, we will introduce some important things you must notice when using it.
In this tutorial, we will introduce MagFace to beginners, it is a good way to measure the quality of the given face.
In this tutorial, we will introduce what is bahdanau attention mechanism and how to implement it.
Zoneout is proposed in paper: Zoneout: Regularizing RNNs by Randomly Preserving Hidden Activations. It is also used in Tacotron 2. In this tutorial, we will introduce what it is and how to implement it using tensorflow.
Smoothing normalization is proposed in paper: Attention-Based Models for Speech Recognition. In this tutorial, we will introduce how to implement it in tensorflow.