Deploying deep learning on embedded platforms involves optimizing models and ensuring real-time performance for efficient execution and unlocking new possibilities.
This article takes a journey into the future of cloud computing, discussing emerging trends such as autonomous and distributed cloud-generative AI tools.
This article briefs about the impact of spam and how it can be addressed with emerging machine-learning technology based on our journey in this domain.
This presentation from Hilary Mason at devs love bacon in April, titled "Everything You Need to know about Machine Learning in 30 Minutes or Less," is an introduction to machine learning for those who have no prior experience with it. Take a look if you're interested in a quick, fun overview to help you get started: Hilary Mason - Machine Learning for Hackers from BACON: things developers love on Vimeo. Then, if you want to get a bit deeper, check out this intro to machine learning in R, or to get a lot deeper, our Machine Learning Refcard.
Learn about overfitting and generalization, and how they relate to the bias-variance tradeoff in machine learning. We’ll also cover techniques for finding the optimal balance between bias and variance in deep learning models.
In the tech world, the Metaverse's allure has dimmed, while generative AI, particularly LLMs, is now in the spotlight. Explore why this shift occurred.
You may want to think twice before jumping on the AI hype train for your OKRs. Security concerns and algorithmic bias can cause your OKRs to cause more harm than good.
Text classification is a machine learning subfield that teaches computers how to classify text into different categories. In this tutorial, we will use BERT to develop your own text classification model.
Explainable AI (XAI) has been gaining popularity among tech enthusiasts, data scientists, and software engineers in a world where AI is becoming more prevalent.