A.I, Data and Software Engineeringmath for data science
Lasso, Ridge, and Elastic Net are excellent methods to improve the performance of your linear model. This post will summarise the usage of these regularization techniques. Bias: Biases are the underlying assumptions that are made by data to simplify the target function. Bias does help us generalize the data better and make the model less sensitive to single data points. It also decreases the...
In this post, we have a look at using a queue and breath-first search algorithm to solve a Leetcode challenge. The problem is stated as follows. Given an m x n 2D binary grid grid which represents a map of '1's (land) and '0's (water), return the number of islands. An island is surrounded by water and is formed by connecting adjacent lands horizontally or vertically. You may assume all four edges...
Machines learn by means of a loss function which reflects how well a specific model performs with the given data. If predictions deviate too much from actual results, loss function would yield a very large value. Gradually, with function, parameters are modified accordingly to reduce the error in prediction. In this article, we will quickly review some common loss functions and their usage in the...
Vector norms are used in many machine learning and computer science problems. This article covers some common norms and related applications. From a high school entrance exam… Remember the day (?/?/1998) when I took an exam to a high school, there was a problem of finding the shortest path from A to B knowing that the person can only go left/right or up/down given the following grid of m x...
Continue with math for machine learning, this article will give a quick note on definition of linear dependence and demonstration with python. math for machine learning Linear Dependence In the theory of vector spaces, a set of vectors is said to be linearly dependent if at least one of the vectors in the set can be defined as a linear combination of...
A.I, Data and Software Engineering