Heartbeat Newsletter: Volume 30
Dear Heartbeat Readers,
Have you registered for Convergence Conference yet? This free, virtual event takes place over two days with 25+ speakers discussing the latest cutting-edge innovations in data science and machine learning. We’ve got sessions by speakers from the likes of Microsoft, Carvana, Twitter, and more. We hope to see you there!
We’ve also had a fantastic few weeks of Heartbeat content we’re excited to share. Be sure to check out numerous NLP projects, object detection algos, sentiment analysis with Comet, and more.
Happy Reading,
Emilie, Abby & the Heartbeat team
Tracking Your Sentiment Analysis With Comet
— by Oluseye Jeremiah
In this article, we’ll learn how to link Comet with Disneyland Sentiment Analysis. In order to accomplish this, we will perform some EDA on the Disneyland dataset, and then we will view the visualization on the Comet experimentation website or platform.
N-grams and How to Implement Them With the Python NLTK Library
— by Loyford Mwenda
Understanding and creating N-grams for Natural Language Processing (NLP) with the Python NLTK library.
Natural Language Processing with R
— by Daniel Tope Omole
The field of natural language processing (NLP) is becoming increasingly important in a variety of industries. R is a powerful language that meets the majority of NLP analysis requirements, particularly when used with the well-liked “tm” and “quanteda” packages.
Guide to Non-Linear Activation Functions in Deep Learning
— by Pralabh Saxena
In this article, we discuss various non-linear activation functions with their mathematical formulas. We also cover the use case of the activation functions.
First Step to Object Detection Algorithms
— by İrem Kömürcü
Object detection is a field of computer vision used to identify and position objects within an image. Examples of object detection applications include detecting abnormal movement from security cameras, obstacle detection in autonomous driving, and character detection from within a document.
Principles of MLOps
— by Tioluwani Oyedele
Machine Learning Operations (MLOps) are the aspects of ML that deal with the creation and advancement of these models.
Understanding Language Models in NLP
— by Jammie sandy
Understanding the concept of language models in natural language processing (NLP) is very important to anyone working in the Deep learning and machine learning space. They are essential to a variety of NLP activities, including speech recognition, machine translation, and text summarization.
