python_tutorials
Python

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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python_tutorials
Python

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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python_tutorials
Python

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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python_tutorials
Python

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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Machine Learning

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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Nearest Neighbor MT, NLP

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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Machine Learning

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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python_tutorials
Python

Deep Learning in Keras – Building a Deep Learning Model

Introduction Deep learning is one of the most interesting and promising areas of artificial intelligence (AI) and machine learning currently. With great advances in technology and algorithms in recent years, deep learning has opened the door to a new era of AI applications. In many of these applications, deep learning algorithms performed equal to human experts and sometimes surpassed them. Python has become the go-to language for Machine Learning and many of the most popular and powerful deep learning libraries […]

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Machine Learning

HyperOpt for Automated Machine Learning With Scikit-Learn

Automated Machine Learning (AutoML) refers to techniques for automatically discovering well-performing models for predictive modeling tasks with very little user involvement. HyperOpt is an open-source library for large scale AutoML and HyperOpt-Sklearn is a wrapper for HyperOpt that supports AutoML with HyperOpt for the popular Scikit-Learn machine learning library, including the suite of data preparation transforms and classification and regression algorithms. In this tutorial, you will discover how to use HyperOpt for automatic machine learning with Scikit-Learn in Python. After […]

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Machine Learning

TPOT for Automated Machine Learning in Python

Automated Machine Learning (AutoML) refers to techniques for automatically discovering well-performing models for predictive modeling tasks with very little user involvement. TPOT is an open-source library for performing AutoML in Python. It makes use of the popular Scikit-Learn machine learning library for data transforms and machine learning algorithms and uses a Genetic Programming stochastic global search procedure to efficiently discover a top-performing model pipeline for a given dataset. In this tutorial, you will discover how to use TPOT for AutoML […]

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