machine learning

Image nets are often refer to neural networks that takes in one image (usually RGB image) and are supposed to output the class of the object shown in the image. There are a lot of famous and published image nets. They were pre-trained on slightly different datasets, developed by different teams in different time, but all widely used in not only object classification, but also many other applications. This article will go through several famous image neural networks (AlexNet, VGG, ResNet, InceptionNet, EfficientNet)....

Thanks to nice illustrative pictures of LSTMs and RNNs by colah’s blog. Recurrent neural networks (RNNs) use the same set of parameters to deal with inputs that are sequential. Inputs are usually broke into pars of same lengths, and fed into RNNs sequentially. In this way, the model learned and preserve the information from sequences of arbitrary lengths. This trait becomes very useful in natural language use cases where a model that is capable of dealing with sentence of any length is needed....

Tree-based methods contains a lot of tricks that are easily tested in data/machine learning related interviews, but very often mixed up. Go through these tricks while knowing the reasons behind could be very helpful in understanding + memorization. Overview of Tree-based Methods Overall speaking, simple decision/regression trees are for better interpretation (as they can be visualized), with some loss of performance (when compared to regression with regularization and non-linear regression methods, e....

EM And GMM Expectation-maximization(EM) algorithm, provides a way to do MLE or MAE when there is incomplete data or unobserved latent variables. NOTE: EM is general way of getting MLE or MAE estimations, not necessarily for clustering. Gaussian mixture model(GMM) is a statistical model that can serve as a clustering algorithm. It assumes the data points to be from several gaussian distributions and uses EM algorithm to obtain the MLE estimations of those gaussians....

Motivation of Kernel Method In classifications, it is often the case that we want to obtain a non-linear decision boundary. For example, for this problem (figure 2), we want a desicion boundary that is somewhat like a circle, however, our model only yields linear boundaries. In order to let our model to have more flexibility without changing the basic algorithms, we can apply a transformation onto the feature space $X$. like the figure on the right....

Brief Introduction to Generative Models To talk about generative models (v.s. discriminative models), we can first learn from this story: A father has two kids, Kid A and Kid B. Kid A has a special character whereas he can learn everything in depth. Kid B have a special character whereas he can only learn the differences between what he saw. One fine day, The father takes two of his kids (Kid A and Kid B) to a zoo....

Logistic regression is a very important binary classification algorithm, in this article, some essential details inside the algorithm will be discussed. Plain language will be used to discuss the most detail aspects so that beginners of machine learning can easily get the idea. Assumptions of Logistic Regression Logistic regression does not require as many assumptions as linear regression. There are a few that are interested and we will shortly discussed about....