Welcome to the Machine Learning Hub, your one-stop destination for all things related to machine learning!
Get ready to embark on an exciting journey into the realm of AI and discover how machines can learn and make intelligent decisions. Our blog articles are crafted with simplicity and clarity in mind, making complex machine learning concepts easy to understand for everyone. Whether you’re a beginner or an experienced practitioner, we’ve got you covered with informative and insightful content. Explore the fascinating world of algorithms, models, and data as we delve into supervised and unsupervised learning, reinforcement learning, and more. Discover practical applications in various domains like healthcare, finance, and autonomous vehicles. From introductory guides to advanced techniques, we’re here to help you demystify machine learning and unlock its potential. Join us on this journey as we unravel the secrets of machine learning and empower you to build intelligent systems that can analyze data, make predictions, and drive innovation.
Let’s shape the future together with the power of machine learning!
New benchmark for evaluating multimodal systems based on real-world video, audio, and text data From the Turing test to ImageNet, benchmarks have played an instrumental role in shaping artificial intelligence (AI) by helping define research goals and allowing researchers to measure progress towards those goals. Incredible breakthroughs in the past 10 years, such as AlexNet …
I talk to [large] organisations that have not yet properly started with Data Science (DS) and Machine Learning (ML), they often tell me that they have to run a data integration project first, because “…all the data is scattered across the organisation, hidden in silos and packed away at odd formats on obscure servers run …
Impact Published 13 October 2022 Developing a better malaria vaccine with the help of AI that could save hundreds of thousands of lives every year When biochemist Matthew Higgins established his research group in 2006, he had malaria firmly in his sights. The mosquito-borne disease is second only to tuberculosis in terms of its devastating …
In my previous article, I discussed how morphological feature extractors mimic the way biological experts visually assess images. time, I want to go a step further and explore a new question:Can different architectures complement each other to build an AI that “sees” like an expert? Introduction: Rethinking Model Architecture Design While building a high accuracy …
Building a responsible approach to data collection with the Partnership on AI At DeepMind, our goal is to make sure everything we do meets the highest standards of safety and ethics, in line with our Operating Principles. One of the most important places this starts with is how we collect our data. In the past …
Why build things the hard way when you can design them the smart way? As a Supply Chain Data Scientist, I’ve explored various frameworks like LangChain and LangGraph to build AI agents using Python. Leveraging LLMs with LangChain for Supply Chain Analytics — A Control Tower Powered by GPT — (Image by Samir Saci) The illustration above is from an …
Automate Supply Chain Analytics Workflows with AI Agents using n8n Read More »
Today we’re introducing Gemini 2.5, our most intelligent AI model. Our first 2.5 release is an experimental version of 2.5 Pro, which is state-of-the-art on a wide range of benchmarks and debuts at #1 on LMArena by a significant margin. Gemini 2.5 models are thinking models, capable of reasoning through their thoughts before responding, resulting …
0. Least Squares is used almost everywhere when it comes to numerical optimization and regression tasks in machine learning. It aims at minimizing the Mean Squared Error (MSE) of a given model. Both L1 (sum of absolute values) and L2 (sum of squares) norms offer an intuitive way to sum signed errors while preventing them …
Least Squares: Where Convenience Meets Optimality Read More »
Notes References [1] John M Jumper, Richard Evans, Alexander Pritzel, Tim Green, Michael Figurnov, Olaf Ron-neberger, Kathryn Tunyasuvunakool, Russ Bates, Augustin Zídek, Anna Potapenko, Alex Bridgland, Clemens Meyer, Simon A A Kohl, Andy Ballard, Andrew Cowie, Bernardino Romera-Paredes, Stanislav Nikolov, Rishub Jain, Jonas Adler, Trevor Back, Stig Petersen, David A. Reiman, Ellen Clancy, Michal Zielinski, …
Benchmarking the next generation of never-ending learners Read More »
previous article on organizing for AI (link), we looked at how the interplay between three key dimensions — ownership of outcomes, outsourcing of staff, and the geographical proximity of team members — can yield a variety of organizational archetypes for implementing strategic AI initiatives, each implying a different twist to the product operating model. Now …
Evolving Product Operating Models in the Age of AI Read More »
