Discrete Fourier Transform (DFT) is a linear transform that converts a finite sequence of equally-spaced samples of a function in the time domain into a same-length sequence of equally-spaced samples in the frequency domain, which is a complex-valued function of frequency.
最大均值差异（Maximum Mean Discrepancy）是迁移学习（Transfer Learning）中常用的损失函数，它可以用来衡量两个随机变量分布的差异性。
Fourier transform has a strong conection with convolution.
Fourier transform is a transform that converts a function into a form that describes the frequencies present in the original function. To a certain extent, it can also be viewed as a coordinate transformation, which transform coordinate in time domain to frequency domain. It is the basis for understanding convolution and CNN.
In deep learning, numerical stability refers to making the training of the model stable and feasible. More spefically, numerical stability is the stability of model parameters, outputs and gradients.
Hyperparameters are parameters of neural networks other than w and b, including learning rate, depth of layer, size of each layer and so on. Compared to model parameters w and b, hyperparameters are determined entirely by ourselves. Tuning a model is to find better hyperparameters.
Dive into deep learning (D2L) is a online deep learning course by Mu Li. In this course, Mu Li teaches the basic concept of DL and usage of pytorch. This post includes the environment configuration in windows, introduction of this course and some basic concept about matrix derivative.
This post records the experimental process of labs of Unsupervised Learning, Recommenders, Reinforcement Learning by Andrew Ng and some bugs that may be encountered under Windows.
This post records the experimental process of labs of Advanced Learning Algorithms by Andrew Ng and some bugs that may be encountered under Windows.
This post records the experimental process of labs of Supervised Machine Learning - Regression and Classification by Andrew Ng and some bugs that may be encountered under Windows.