Articles About Machine Learning

A Gentle Introduction To Approximation

When it comes to machine learning tasks such as classification or regression, approximation techniques play a key role in learning from the data. Many machine learning methods approximate a function or a mapping between the inputs and outputs via a learning algorithm. In this tutorial, you will discover what is approximation and its importance in machine learning and pattern recognition. After completing this tutorial, you will know:  What is approximation Importance of approximation in machine learning Let’s get started.

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A Gentle Introduction to Taylor Series

A Gentle Introduction to Taylor Series Taylor series expansion is an awesome concept, not only the world of mathematics, but also in optimization theory, function approximation and machine learning. It is widely applied in numerical computations when estimates of a function’s values at different points are required. In this tutorial, you will discover Taylor series and how to approximate the values of a function around different points using its Taylor series expansion. After completing this tutorial, you will know: Taylor […]

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Calculus in Action: Neural Networks

An artificial neural network is a computational model that approximates a mapping between inputs and outputs.  It is inspired by the structure of the human brain, in that it is similarly composed of a network of interconnected neurons that propagate information upon receiving sets of stimuli from neighbouring neurons. Training a neural network involves a process that employs the backpropagation and gradient descent algorithms in tandem. As we will be seeing, both of these algorithms make extensive use of calculus. […]

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A Gentle Introduction To Sigmoid Function

Whether you implement a neural network yourself or you use a built in library for neural network learning, it is of paramount importance to understand the significance of a sigmoid function. The sigmoid function is the key to understanding how a neural network learns complex problems. This function also served as a basis for discovering other functions that lead to efficient and good solutions for supervised learning in deep learning architectures. In this tutorial, you will discover the sigmoid function […]

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Lagrange Multiplier Approach with Inequality Constraints

In a previous post, we introduced the method of Lagrange multipliers to find local minima or local maxima of a function with equality constraints. The same method can be applied to those with inequality constraints as well. In this tutorial, you will discover the method of Lagrange multipliers applied to find the local minimum or maximum of a function when inequality constraints are present, optionally together with equality constraints. After completing this tutorial, you will know How to find points […]

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A Gentle Introduction to Particle Swarm Optimization

Particle swarm optimization (PSO) is one of the bio-inspired algorithms and it is a simple one to search for an optimal solution in the solution space. It is different from other optimization algorithms in such a way that only the objective function is needed and it is not dependent on the gradient or any differential form of the objective. It also has very few hyperparameters. In this tutorial, you will learn the rationale of PSO and its algorithm with an […]

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Training-validation-test split and cross-validation done right

One crucial step in machine learning is the choice of model. A suitable model with suitable hyperparameter is the key to a good prediction result. When we are faced with a choice between models, how should the decision be made? This is why we have cross validation. In scikit-learn, there is a family of functions that help us do this. But quite often, we see cross validation used improperly, or the result of cross validation not being interpreted correctly. In […]

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How to Learn Python for Machine Learning

Python has become a de facto lingua franca for machine learning. It is not a difficult language to learn, but if you are not particularly familiar with the language, there are some tips that can help you learn faster or better. In this post, you will discover what the right way to learn a programming language is and how to get help. After reading this post, you will know: The right mentality to learn Python for use in machine learning […]

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Optimization for Machine Learning Crash Course

Optimization for Machine Learning Crash Course.Find function optima with Python in 7 days. All machine learning models involve optimization. As a practitioner, we optimize for the most suitable hyperparameters or the subset of features. Decision tree algorithm optimize for the split. Neural network optimize for the weight. Most likely, we use computational algorithms to optimize. There are many ways to optimize numerically. SciPy has a number of functions handy for this. We can also try to implement the optimization algorithms […]

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Principal Component Analysis for Visualization

Principal component analysis (PCA) is an unsupervised machine learning technique. Perhaps the most popular use of principal component analysis is dimensionality reduction. Besides using PCA as a data preparation technique, we can also use it to help visualize data. A picture is worth a thousand words. With the data visualized, it is easier for us to get some insights and decide on the next step in our machine learning models. In this tutorial, you will discover how to visualize data […]

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