Machine Learning

Face Recognition using Principal Component Analysis

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 see how we can build a primitive face recognition system with some simple linear algebra technique such as principal component analysis. After completing this tutorial, […]

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Machine Learning

Using CNN for financial time series prediction

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. By way of this example, we are going to explore some techniques in using Keras for model training as well. After completing this tutorial, you […]

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Machine Learning

Visualizing the vanishing gradient problem

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 examine why vanishing gradient problem exists. After completing this tutorial, you will know What is a vanishing gradient Which configuration of neural network will be […]

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Machine Learning

Method of Lagrange Multipliers: The Theory Behind Support Vector Machines (Part 1: The Separable Case)

This tutorial is designed for anyone looking for a deeper understanding of how Lagrange multipliers are used in building up the model for support vector machines (SVMs). SVMs were initially designed to solve binary classification problems and later extended and applied to regression and unsupervised learning. They have shown their success in solving many complex machine learning classification problems. In this tutorial, we’ll look at the simplest SVM that assumes that the positive and negative examples can be completely separated via […]

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Machine Learning

Application of differentiations in neural networks

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 What is a total differential and total derivative How to compute the total derivatives in neural networks How back-propagation helped in computing the total derivatives […]

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Machine Learning

Method of Lagrange Multipliers: The Theory Behind Support Vector Machines (Part 2: The Non-Separable Case)

This tutorial is an extension of Method Of Lagrange Multipliers: The Theory Behind Support Vector Machines (Part 1: The Separable Case)) and explains the non-separable case. In real life problems positive and negative training examples may not be completely separable by a linear decision boundary. This tutorial explains how a soft margin can be built that tolerates a certain amount of errors. In this tutorial, we’ll cover the basics of a linear SVM. We won’t go into details of non-linear […]

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Machine Learning

Method of Lagrange Multipliers: The Theory Behind Support Vector Machines (Part 3: Implementing An SVM From Scratch In Python)

The mathematics that powers a support vector machine (SVM) classifier is beautiful. It is important to not only learn the basic model of an SVM but also know how you can implement the entire model from scratch. This is a continuation of our series of tutorials on SVMs. In part1 and part2 of this series we discussed the mathematical model behind a linear SVM. In this tutorial, we’ll show how you can build an SVM linear classifier using the optimization routines […]

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Machine Learning

Some Language Features in Python

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 language, you will sometimes find Python syntax understandable but weird. If you are used to writing in C++ or Java and then transitioning to Python, […]

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Machine Learning

Python Classes and Their Use in Keras

Classes are one of the fundamental building blocks of the Python language, which may be applied in the development of machine learning applications. As we shall see, the Python syntax for developing classes is simple and can be applied to implement callbacks in Keras.  In this tutorial, you will discover the Python classes and their functionality.  After completing this tutorial, you will know: Why Python classes are important How to define and instantiate a class and set its attributes  How […]

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Machine Learning

Understanding Traceback in Python

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 a traceback How to print the call stack without an exception What is not shown in the traceback Kick-start your project with my new book […]

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