Heartbeat Newsletter: Volume 32
Dear Heartbeat Readers,
The Comet team is still buzzing from our recent Convergence Conference - We were so excited to have thousands of you there learning from the most innovative minds in the industry! Do you have any suggestions for next year’s event? Let us know in our Convergence Slack channel.
This week, we’ve got some fantastic pieces on NLP, including a discussion of RoBERTa, and some deep dives into computer vision. We’ve got a lot of great articles lined up as well so be sure to subscribe to Heartbeat and your favorite authors to stay up-to-date!
Happy Reading,
Emilie, Abby & The Heartbeat Team
An Analysis of the Loss Functions in Keras CV Tutorials
— by Kristen Kehrer
This particular article is about the loss functions available in Keras. A loss function (sometimes called an error function), it is a measure of the difference between the actual values and the estimated values in your model. ML models use loss functions to help choose the model that is creating the best model fit for a given set of data (actual values are the most like the estimated values).
Dataset Tracking with Comet ML Artifacts
— by Mwanikii
In this article, I intend to show how someone can keep track of changes with Comet ML’s dataset storage feature: Artifacts.
A Vision for the Future: How Computer Vision is Transforming Robotics
— by Randy Baraka
Computer vision is crucial to robotics because it allows robots to see and interpret their environment. Thanks to this capability, robots can perform tasks such as object identification and tracking, navigation, and scene interpretation. These responsibilities are crucial for robots to perform their functions and make judgments in a dynamic setting.
RoBERTa: A Modified BERT Model for NLP
— by Khushboo Kumari
An open-source machine learning model called BERT was developed by Google in 2018 for NLP, but this model had some limitations, and due to this, a modified BERT model called RoBERTa (Robustly Optimized BERT Pre-Training Approach) was developed by the team at Facebook in the year 2019.
Train Your Own YoloV7 Object Detection Model
— by Gourav Bais
Object detection is one of the most important concepts in the deep learning space. It is the process of identifying certain objects in an image and correctly classifying them to the respective classes. Models used for this task are called Object Detection Models (ODM), which create a bounding box (rectangular or square box) over the object in an image so that users can directly pay attention to the object they are looking for.
Monitoring Your Time Series Model in Comet
— by David Fagbuyiro
In this tutorial, we will go through steps on how to use Comet to monitor our time-series forecasting model. We will carry out some EDA on our dataset, and then we will log the visualizations onto the Comet experimentation website or platform.
Natural Language Processing (NLP) Concepts With NLTK
— by Brian Mutea
Learn NLP data processing operations with NLTK, visualize the data with Kangas, build a spam classifier, and track it with Comet Machine Learning Platform.
