Profiling is a technique to figure out how time is spent in a program. With these statistics, we can find the “hot spot” of a program and think about ways of improvement. Sometimes, a hot spot in an unexpected location may hint at a bug in the program as well. In this tutorial, we will […]
Static analyzers are tools that help you check your code without really running your code. The most basic form of static analyzers is the syntax highlighters in your favorite editors. If you need to compile your code (say, in C++), your compiler, such as LLVM, may also provide some static analyzer functions to warn you […]
We write a program to solve a problem or make a tool that we can repeatedly solve a similar problem. For the latter, it is inevitable that we come back to revisit the program we wrote, or someone else is reusing the program we write. There is also a chance that we will encounter data […]
When we finish a Python project and roll it out for other people to use, the easiest way is to present our project as a command-line program. If you want to make it friendlier, you may want to develop a GUI for your program so people can interact with it with mouse clicks while it […]
After all the hard work developing a project in Python, we want to share our project with other people. It can be your friends or your colleagues. Maybe they are not interested in your code, but they want to run it and make some real use of it. For example, you create a regression model […]
In the old days, it was a tedious job to collect data, and it was sometimes very expensive. Machine learning projects cannot live without data. Luckily, we have a lot of data on the web at our disposal nowadays. We can copy data from the web to create our dataset. We can manually download files […]
When we talk about managing data, it is quite inevitable to see data presented in tables. With column header, and sometimes with names for rows, it makes understanding data easier. In fact, it often happens that we see data of different types staying together. For example, we have quantity as numbers and name as strings […]
Python is a general-purpose computation language, but it is very welcomed in scientific computing. It can replace R and Matlab in many cases, thanks to some libraries in the Python ecosystem. In machine learning, we use some mathematical or statistical functions extensively, and often, we will find NumPy and SciPy useful. In the following, we […]
Compared to other programming exercises, a machine learning project is a blend of code and data. You need both to achieve the result and do something useful. Over the years, many well-known datasets have been created, and many have become standards or benchmarks. In this tutorial, we are going to see how we can obtain […]
Calculus for Machine Learning Crash Course. Get familiar with the calculus techniques in machine learning in 7 days. Calculus is an important mathematics technique behind many machine learning algorithms. You don’t always need to know it to use the algorithms. When you go deeper, you will see it is ubiquitous in every discussion on the […]
