In the vast universe of data, it’s not always about what we can see but rather what we can infer. Confidence intervals, a cornerstone of inferential statistics, empower us to make educated guesses about a larger population based on our sample data. Using the Ames Housing dataset, let’s unravel the concept of confidence intervals and […]
Whether working on a machine learning project, a simulation, or other models, you need to generate random numbers in your code. R as a programming language, has several functions for random number generation. In this post, you will learn about them and see how they can be used in a larger program. Specifically, you will […]
Deep Learning with PyTorch Learn Basic Deep Learning with Minimal Code in PyTorch 2.0 Why Are Deep Learning Models So Powerful? …the secret is “Representation Learning“ Deep learning techniques are so powerful because they learn the best way to represent the problem while learning how to solve the problem. This is called representation learning. Representation […]
The PyTorch library is for deep learning. Some applications of deep learning models are used to solve regression or classification problems. In this tutorial, you will discover how to use PyTorch to develop and evaluate neural network models for multi-class classification problems. After completing this step-by-step tutorial, you will know: How to load data from […]
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 […]
Calculus for Machine Learning Understanding the Language of Mathematics …why calculus? We are not mathematicians! Calculus is a sub-field of mathematics concerned with very small values. It can tell us what happens when we take a small step in one direction or another. It is a perfect tool to describe the progress of how machines […]
Dual Annealing is a stochastic global optimization algorithm. It is an implementation of the generalized simulated annealing algorithm, an extension of simulated annealing. In addition, it is paired with a local search algorithm that is automatically performed at the end of the simulated annealing procedure. This combination of effective global and local search procedures provides […]
Ensemble learning combines the predictions from machine learning models for classification and regression. We pursue using ensemble methods to achieve improved predictive performance, and it is this improvement over any of the contributing models that defines whether an ensemble is good or not. A property that is present in a good ensemble is the diversity […]
Ensemble Learning Algorithms With Python Crash Course. Get on top of ensemble learning with Python in 7 days. Ensemble learning refers to machine learning models that combine the predictions from two or more models. Ensembles are an advanced approach to machine learning that are often used when the capability and skill of the predictions are […]
Stacked generalization, or stacking, may be a less popular machine learning ensemble given that it describes a framework more than a specific model. Perhaps the reason it has been less popular in mainstream machine learning is that it can be tricky to train a stacking model correctly, without suffering data leakage. This has meant that […]
