Deep Learning

What is a Multilayer Perceptron? What are the pros and cons of MLP? Can we classify handwritten digits accurately using an MLP classifier? How do learned weights look like?

Figure 1: A Multilayer Perceptron Network (source).

1. Short Introduction

1.1 What is a Multilayer Perceptron (MLP)?

An MLP is a supervised machine learning (ML) algorithm that belongs in the class of feedforward artificial neural networks [1]. The algorithm essentially is trained on the data in order to learn a function. Given a set of features and a target variable (e.g. labels) it learns a non-linear function…

In this post I show you how to predict stock prices using a forecasting LSTM model

Figure created by the author.

1. Introduction

1.1. Time-series & forecasting models

Traditionally most machine learning (ML) models use as input features some observations (samples / examples) but there is no time dimension in the data.

Time-series forecasting models are the models that are capable to predict future values based on previously observed values. Time-series forecasting is widely used for non-stationary data

What is a Multilayer Perceptron? What are the pros and cons of MLP? Can we classify handwritten digits accurately using a MLP classifier? How do learnt weights look like?

Figure 1: A Multilayer Perceptron Network (source).

1. Short Introduction

1.1 What is a Multilayer Perceptron (MLP)?

An MLP is a supervised machine learning (ML) algorithm that belongs in the class of feedforward artificial neural networks [1]. The algorithm essentially is trained on the data in order to learn a function. Given a set of features and a target variable (e.g. labels) it learns a non-linear function…

What is a Multilayer Perceptron? What are the pros and cons of MLP? Can we classify handwritten digits accurately using a MLP classifier? How do learnt weights look like?

Figure 1: A Multilayer Perceptron Network (source).

1. Short Introduction

1.1 What is a Multilayer Perceptron (MLP)?

An MLP is a supervised machine learning (ML) algorithm that belongs in the class of feedforward artificial neural networks [1]. The algorithm essentially is trained on the data in order to learn a function. Given a set of features and a target variable (e.g. labels) it learns a non-linear function…

In this post, I explain what PCA is, when, and why to use it, and how to implement it in Python using scikit-learn. Also, I explain how to get the feature importance after a PCA analysis.

Handmade sketch made by the author.

1. Introduction & Background

Principal Components Analysis (PCA) is a well-known unsupervised dimensionality reduction technique that constructs relevant features/variables through linear (linear PCA) or non-linear (kernel PCA) combinations of the original variables (features). In this post, we will only focus on the famous and widely used linear PCA method.

The construction of relevant features…

In this article I explain the core of the SVMs, why and how to use them. Additionally, I show how to plot the support vectors and the decision boundaries in 2D and 3D.

Handmade sketch made by the author. An SVM illustration.

Introduction

Everyone has heard about the famous and widely-used Support Vector Machines (SVMs). The original SVM algorithm was invented by Vladimir N. Vapnik and Alexey Ya. Chervonenkis in 1963.

SVMs are supervised machine learning models that are usually employed for classification (SVC — Support Vector Classification) or regression (SVR — Support…

Mathematical formulation, Finding the optimum number of clusters and a working example in Python

Image created by the author

Introduction

K-means is one of the most widely used unsupervised clustering methods.

The K-means algorithm clusters the data at hand by trying to separate samples into K groups of equal variance, minimizing a criterion known as the inertia or within-cluster sum-of-squares. This algorithm requires the number of clusters to be specified

In this post I show you how to predict the TESLA stock price using a forecasting ARIMA model

ARIMA model performance on the test set

1. Introduction

1.1. Time-series & forecasting models

Time-series forecasting models are the models that are capable to predict future values based on previously observed values. Time-series forecasting is widely used for non-stationary data. Non-stationary data are called the data whose statistical properties e.g. …

In this article I explain what feature selection is and how to perform it before training a regression model in Python.

1. Introduction

What is feature selection ?

Feature selection is the procedure of selecting a subset (some out of all available) of the input variables that are most relevant to the target variable (that we wish to predict).

Target variable here refers to the variable that we wish to predict.

For this article we will assume that…

Serafeim Loukas

Research Scientist at University of Geneva & University Hospital of Bern. PhD, MSc, M.Eng.

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