Introduction Recurrent Neural Networks (RNNs) are a specific type of Neural Networks (NNs) that are especially relevant for sequential data like time series, text, or audio data. Traditional neural networks process each input independently, meaning they cannot retain information about previous inputs. This makes them ineffective for tasks that require understanding sequences, such as time series forecasting or natural language processing. RNNs however, process the data sequentially, which enables them to remember data from the past.

Introduction Principal Component Analysis (PCA) is an unsupervised Machine Learning algorithm for dimensionality reduction. In Data Science and Machine Learning, large datasets with numerous features are often analyzed. PCA simplifies these complex datasets by retaining their essential information while reducing their dimensionality. It transforms a large set of correlated variables into a smaller set of uncorrelated variables known as principal components. These principal components capture the maximum variance in the data.

Introduction K-Means is an example of a clustering algorithm. Clustering is a fundamental concept in Machine Learning, where the goal is to group a set of objects so that objects in the same group are more similar to each other than to those in other groups. Clustering belongs to the set of unsupervised Machine Learning algorithms, that is no ground truth is needed. Among the various clustering algorithms, K-Means stands out for its simplicity and efficiency.

Introduction Gradient Boosting is an ensemble model of a sequential series of shallow Decision Trees. The single trees are weak learners with little predictive skill, but together, they form a strong learner with high predictive skill. For a more detailed explanation, please refer to the post Gradient Boosting for Regression - Explained. In this article, we will discuss different implementations of Gradient Boosting. The focus is to give a high-level overview of different implementations and discuss the differences.

Introduction In this post, we develop a Gradient Boosting model for a binary classification. We focus on the calculations of each single step for a specific example chosen. For a more general explanation of the algorithm and the derivation of the formulas for the individual steps, please refer to Gradient Boost for Classification - Explained and Gradient Boost for Regression - Explained. Additionally, we show a simple example of how to apply Gradient Boosting for classification in Python.

Introduction Gradient Boosting is an ensemble machine learning model, that - as the name suggests - is based on boosting. An ensemble model based on boosting refers to a model that sequentially builds models, and the new model depends on the previous model. In Gradient Boosting these models are built such that they improve the error of the previous model. These individual models are so-called weak learners, which means they have low predictive skills.

Introduction In this post, we will go through the development of a Gradient Boosting model for a regression problem, considering a simplified example. We calculate the individual steps in detail, which are defined and explained in the separate post Gradient Boost for Regression - Explained. Please refer to this post for a more general and detailed explanation of the algorithm. Data We will use a simplified dataset consisting of only 10 samples, which describes how many meters a person has climbed, depending on their age, whether they like height, and whether they like goats.

Introduction A neural network consists of a set of parameters - the weights and biases - which define the outcome of the network, that is the predictions. When training a neural network we aim to adjust these weights and biases such that the predictions improve. To achieve that Backpropagation is used. In this post, we discuss how backpropagation works, and explain it in detail for three simple examples. The first two examples will contain all the calculations, for the last one we will only illustrate the equations that need to be calculated.

Introduction Gradient Descent is a mathematical optimization technique, which is used to find the local minima of a function. In Machine Learning it is used in a variety of models such as Gradient Boosting or Neural Networks to minimize the Loss Function. It is an iterative algorithm that takes small steps towards the minimum in every iteration. The idea is to start at a random point and then take a small step into the direction of the steepest descent of this point.

Introduction In Machine Learning loss functions are used to evaluate the model. They compare the true target values with the predicted ones and are directly related to the error of the predictions. During the training of a model, the loss function is aimed to be optimized to minimize the error of the predictions. It is a general convention to define a loss function such that it is minimized rather than maximized.