🎥 Watch now: Usman Akinyemi shares his journey from our coding workshop to a successful career in Computer Science!🎬👨🏿‍💻 Watch the full video here: youtu.be/WB9xNSJrx2M

"We currently have an opening for a PhD student to conduct research in biomass/waste conversion at the Department of Mechanical Engineering, McMaster University. The research will involve both experimental and numerical studies on the processes to convert biomass into high-value

“Will AI really take our jobs???” by Adebayo Abdulganiyu Keji medium.com/@kejihela/mach….

Model management in production in machine learning is quite as important as developing the model. One of the most adopted tool for machine learning model management is MLflow. Though we have couple of others ranging from weight and Bias to Neptune.

Model management entails experiment tracking, model versioning and model deployment. You won’t understand how important the first two are until you start turning your machine learning models into software products.

Starting today, open source is leading the way. Introducing Llama 3.1: Our most capable models yet. Today we’re releasing a collection of new Llama 3.1 models including our long awaited 405B. These models deliver improved reasoning capabilities, a larger 128K token context

Let's build a RAG app using MetaAI's Llama-3.1 (100% local):

Ready to start working with Llama 3.1? Here are a few new resources from the Llama team to help you get started. 🧵

As part of our support of open science, we've published the full Llama 3 research paper covering a range of topics. • Model training • Model architecture • Results of our ongoing work on integrating image/video/speech capabilities • Much more Paper ➡️ go.fb.me/l5f55z

GitHub is one of the tools a machine learning engineer should have in his/her arsenal. “What you need to know about Git and GitHub.” by Adebayo Abdulganiyu Keji medium.com/@kejihela/what… You can also read more here: kejihela.github.io/portfolio/

I was reading a paper a while ago then an unexpected happen. While reading the lit review section, a paper of which I have read earlier was summarized to ‘ve an acc of 89.09% where as the paper had 99.09%. I begin to ask myself is this an error or a literature gap technique? #AI