Python

Records and Sets: Selecting the Ideal Data Structure

There are a variety of ways for storing and managing data in your program and the choice of the right data structure has an effect on the readability of your code, ease of writing, and performance. Python has a wide selection of built-in mechanisms that meet most of your data structure needs. This course introduces you to two types of data structures: data records and sets. There are multiple types and classes for both of these data structures and this […]

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Advanced, NLP, Unsupervised, Videos

Tokenization and Text Normalization

Objective Text data is a type of unstructured data used in natural language processing. Understand how to preprocess the text data before feeding it to the machine learning algorithms. Introduction Text data is a form of unstructured data. The most prominent examples of text data available on the internet are social media data like tweets, posts, comments, or the Conversation data such as messages, emails, Chats. Also, it can be article data like news articles, blogs, etc. Note: If you […]

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Python

Build an Asteroids Game With Python and Pygame

Do you want to create your own computer games but like Python too much to abandon it for a career as a game developer? There’s a solution for that! With the Pygame module, you can use your amazing Python skills to create games, from the basic to the very complex. Below, you’ll learn how to use Pygame by making a clone of the Asteroids game! In this tutorial, you’ll learn how to build a complete game, including: Loading images and […]

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python_tutorials
Python

Matplotlib: Draw Vertical Lines on Plot

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 draw a vertical line on a Matplotlib plot, that allows us to mark and highlight certain regions of the plot, without zooming or changing the axis range. Creating a Plot Let’s first create a […]

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Neural Machine Translation (NMT), NMT Leave a comment

Machine Translation Weekly 72: Self-Training for Zero-Shot MT

This week, I will have a look at a pre-print that describes an unconventional setup for zero-shot machine translation. The title of the pre-print is Self-Learning for Zero-Shot Neural Machine Translation and was written by authors from the University of Trento. First of all, I have some doubt about this being really an instance of zero-shot learning (but it is just nitpicking, the paper is interesting regardless of the terminology). In machine learning, zero-shot learning means that a model trained […]

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python_tutorials
Python

Validating and Formatting Phone Numbers in Python with phonenumbers

Introduction Validating phone numbers can be a very challenging task. The format of a phone number can vary from one country to another. Heck, it can also vary within the same country! Some countries share the same country code, while some other countries use more than one country code. According to an example from the Google’s libphonenumber GitHub repository, USA, Canada, and Caribbean islands, all share the same country code (+1). On the other hand, it is possible to call […]

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NLP, target lemma annotations

Issue #122 – Can annotations help to get terminology right in MT?

18 Mar21 Issue #122 – Can annotations help to get terminology right in MT? Author: Dr. Carla Parra Escartín, Global Program Manager @ Iconic Introduction Getting terminology translated properly is a well known challenge for Machine Translation (MT) and an important element when measuring translation quality (both human and machine). In fact, forcing terminology, or getting terminology right is a frequent request from our customers. But getting it right is not a trivial task, and as researchers quest the best […]

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NLP

GANs for Good [My Takeaways]

Yesterday, I attended the amazing “GANs for Good” panel discussion hosted by deeplearning.ai, and here are my takeaways: Generative adversarial networks (GANs) have been improved over the years and are starting to see adoption in the real world in domains such as health, art, and augmented reality. A conversation on progress and responsible use is needed. Current progress and iterations of GANs show that we have gone from generating simple low-resolution images to high-resolution realistic images. However, applications beyond simple […]

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NLP

Getting Started with Applied ML Research

So you are interested in applied machine learning (ML) research? Oftentimes, a lot of young aspiring machine learning researchers jump straight into reading papers and either get discouraged with the amount of work published on a particular topic or get too caught up reading a lot of papers with very little progress on generating new and exciting research ideas. To avoid these situations and ensuring a healthy start on your research journey, here are some of my tips on how […]

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NLP

My Recommendations to Learn Mathematics for Machine Learning

I have always emphasized on the importance of mathematics in machine learning. Here is a compilation of resources (books, videos, and papers) to get you going. This is not an exhaustive list but I have carefully curated it based on my experience and observations. This is a repost of my Twitter thread that you can find here. I will keep updating the list here as I come across more useful resources. Mathematics for Machine Learning by Marc Peter Deisenroth, A. […]

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