Python is a duck typing language. It means the data types of variables can change as long as the syntax is compatible. Python is also a dynamic programming language. Meaning we can change the program while it runs, including defining new functions and the scope of the name resolution. These give us not only a […]
The source code of a program should be readable to humans. Making it run correctly is only half of its purpose. Without a properly commenting code, it would be difficult for one, including the future you, to understand the rationale and intent behind the code. It would also make the code impossible to maintain. In […]
In all programming exercises, it is difficult to go far and deep without a handy debugger. The built-in debugger, pdb, in Python is a mature and capable one that can help us a lot if you know how to use it. In this tutorial, we are going to see what the pdb can do for […]
When an exception occurs in a Python program, often a traceback will be printed. Knowing how to read the traceback can help you easily identify the error and make a fix. In this tutorial, we are going to see what the traceback can tell you. After completing this tutorial, you will know: How to read […]
The Python language syntax is quite powerful and expressive. Hence it is concise to express an algorithm in Python. Maybe this is the reason why it is popular in machine learning, as we need to experiment a lot in developing a machine learning model. If you’re new to Python but with experience in another programming […]
Differential calculus is an important tool in machine learning algorithms. Neural networks in particular, the gradient descent algorithm depends on the gradient, which is a quantity computed by differentiation. In this tutorial, we will see how the back-propagation technique is used in finding the gradients in neural networks. After completing this tutorial, you will know […]
Deep learning was a recent invention. Partially, it is due to improved computation power that allows us to use more layers of perceptrons in a neural network. But at the same time, we can train a deep network only after we know how to work around the vanishing gradient problem. In this tutorial, we visually […]
Convolutional neural networks have their roots in image processing. It was first published in LeNet to recognize the MNIST handwritten digits. However, convolutional neural networks are not limited to handling images. In this tutorial, we are going to look at an example of using CNN for time series prediction with an application from financial markets. […]
Recent advance in machine learning has made face recognition not a difficult problem. But in the previous, researchers have made various attempts and developed various skills to make computer capable of identifying people. One of the early attempt with moderate success is eigenface, which is based on linear algebra techniques. In this tutorial, we will […]
Singular value decomposition is a very popular linear algebra technique to break down a matrix into the product of a few smaller matrices. In fact, it is a technique that has many uses. One example is that we can use SVD to discover relationship between items. A recommender system can be build easily from this. […]
