Python: Check if File or Directory is Empty

Introduction Python has a set of built-in library objects and functions to help us with this task. In this tutorial, we’ll learn how to check if a file or directory is empty in Python. Distinguish Between a File and a Directory When we’d like to check if a path is empty or not, we’ll want to know if it’s a file or directory since this affects the approach we’ll want to use. Let’s say we have two placeholder variables dirpath […]

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Kernel Density Estimation in Python Using Scikit-Learn

Introduction This article is an introduction to kernel density estimation using Python’s machine learning library scikit-learn. Kernel density estimation (KDE) is a non-parametric method for estimating the probability density function of a given random variable. It is also referred to by its traditional name, the Parzen-Rosenblatt Window method, after its discoverers. Given a sample of independent, identically distributed (i.i.d) observations ((x_1,x_2,ldots,x_n)) of a random variable from an unknown source distribution, the kernel density estimate, is given by: $$p(x) = frac{1}{nh} […]

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Replace Occurrences of a Substring in String with Python

Introduction Replacing all or n occurrences of a substring in a given string is a fairly common problem of string manipulation and text processing in general. Luckily, most of these tasks are made easy in Python by its vast array of built-in functions, including this one. Let’s say, we have a string that contains the following sentence: The brown-eyed man drives a brown car. Our goal is to replace the word “brown” with the word “blue”: The blue-eyed man drives […]

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Facial Detection in Python with OpenCV

Introduction Facial detection is a powerful and common use-case of Machine Learning. It can be used to automatize manual tasks such as school attendance and law enforcement. In the other hand, it can be used for biometric authorization. In this article, we’ll perform facial detection in Python, using OpenCV. OpenCV OpenCV is one of the most popular computer vision libraries. It was written in C and C++ and also provides support for Python, besides Java and MATLAB. While it’s not […]

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Padding Strings in Python

Introduction String padding refers to adding, usually, non-informative characters to a string to one or both ends of it. This is most often done for output formatting and alignment purposes, but it can have useful practical applications. A frequent use case for padding strings is outputting table-like information in a table-like fashion. You can do this in a variety of ways, including using Pandas to convert your data to an actual table. This way, Python would handle the output formatting […]

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Serving Static Files in Python With Django, AWS S3 and WhiteNoise

Introduction Websites generally need additional files such as images, CSS, and JavaScript files that are necessary to render complete web pages in a browser. In small projects, we can work our way around by providing absolute paths to our resources or by writing inline CSS and JavaScript functions in the HTML files. This is not only against the best coding practices but it also gets tricky when we are handling bigger projects, especially with multiple applications. In Django, the files […]

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Change Figure Size in Matplotlib

Introduction Matplotlib is one of the most widely used data visualization libraries in Python. Much of Matplotlib’s popularity comes from its customization options – you can tweak just about any element from its hierarchy of objects. In this tutorial, we’ll take a look at how to change a figure size in Matplotlib. Creating a Plot Let’s first create a simple plot in a figure: import matplotlib.pyplot as plt import numpy as np x = np.arange(0, 10, 0.1) y = np.sin(x) […]

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Combined Algorithm Selection and Hyperparameter Optimization (CASH Optimization)

Machine learning model selection and configuration may be the biggest challenge in applied machine learning. Controlled experiments must be performed in order to discover what works best for a given classification or regression predictive modeling task. This can feel overwhelming given the large number of data preparation schemes, learning algorithms, and model hyperparameters that could be considered. The common approach is to use a shortcut, such as using a popular algorithm or testing a small number of algorithms with default […]

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Issue #102 – Nearest Neighbour Machine Translation

08 Oct20 Issue #102 – Nearest Neighbour Machine Translation Author: Dr. Patrik Lambert, Senior Machine Translation Scientist @ Iconic Introduction Taking into account context information in neural MT is an active area of research, with applications in document-level translation, domain adaptation and multilingual neural MT. Today we take a look at a method which combines predictions from a neural MT model and from a nearest neighbour classifier, retrieved from similar contexts in a datastore of cached examples. This approach, called […]

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Hyperparameter Optimization With Random Search and Grid Search

Last Updated on September 19, 2020 Machine learning models have hyperparameters that you must set in order to customize the model to your dataset. Often the general effects of hyperparameters on a model are known, but how to best set a hyperparameter and combinations of interacting hyperparameters for a given dataset is challenging. There are often general heuristics or rules of thumb for configuring hyperparameters. A better approach is to objectively search different values for model hyperparameters and choose a […]

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