I that most companies would have built or implemented their own Rag agents by now. An AI knowledge agent can dig through internal documentation — websites, PDFs, random docs — and answer employees in Slack (or Teams/Discord) within a few seconds. So, these bots should significantly reduce time sifting through information for employees. I’ve seen …
Agentic RAG Applications: Company Knowledge Slack Agents Read More »
is a game changer in Machine Learning. In fact, in the recent history of Deep Learning, the idea of allowing models to focus on the most relevant parts of an input sequence when making a prediction completely revolutionized the way we look at Neural Networks. That being said, there is one controversial take that I …
Hands-On Attention Mechanism for Time Series Classification, with Python Read More »
Advances in generative AI are making it possible for people to create content in entirely new ways — from text to high quality audio, images and videos. As these capabilities advance and become more broadly available, questions of authenticity, context and verification emerge. Today we’re announcing SynthID Detector, a verification portal to quickly and efficiently …
Never miss a new edition of The Variable, our weekly newsletter featuring a top-notch selection of editors’ picks, deep dives, community news, and more. We’re wrapping up another eventful month, one in which we published dozens of new articles on cutting-edge and evergreen topics alike: from math for machine learning engineers to the inner workings …
May Must-Reads: Math for Machine Learning Engineers, LLMs, Agent Protocols, and More Read More »
Today, we’re announcing our newest generative media models, which mark significant breakthroughs. These models create breathtaking images, videos and music, empowering artists to bring their creative vision to life. They also power amazing tools for everyone to express themselves. Veo 3 and Imagen 4, our newest video and image generation models, push the frontier of …
Fuel your creativity with new generative media models and tools Read More »
to tune hyperparamters of deep learning models (Keras Sequential model), in comparison with a traditional approach — Grid Search. Bayesian Optimization Bayesian Optimization is a sequential design strategy for global optimization of black-box functions. It is particularly well-suited for functions that are expensive to evaluate, lack an analytical form, or have unknown derivatives.In the context of hyperparameter …
Bayesian Optimization for Hyperparameter Tuning of Deep Learning Models Read More »
Following the exciting launches of Gemma 3 and Gemma 3 QAT, our family of state-of-the-art open models capable of running on a single cloud or desktop accelerator, we’re pushing our vision for accessible AI even further. Gemma 3 delivered powerful capabilities for developers, and we’re now extending that vision to highly capable, real-time AI operating …
Announcing Gemma 3n preview: powerful, efficient, mobile-first AI Read More »
In this article, I will introduce you to hierarchical Bayesian (HB) modelling, a flexible approach to automatically combine the results of multiple sub-models. This method enables estimation of individual-level effects by optimally combining information across different groupings of data through Bayesian updating. This is particularly valuable when individual units have limited observations but share common …
Estimating Product-Level Price Elasticities Using Hierarchical Bayesian Read More »
We’re publishing a new white paper outlining how we’ve made Gemini 2.5 our most secure model family to date. Imagine asking your AI agent to summarize your latest emails — a seemingly straightforward task. Gemini and other large language models (LLMs) are consistently improving at performing such tasks, by accessing information like our documents, calendars, …
Advancing Gemini’s security safeguards – Google DeepMind Read More »
Learning Supervised learning is a category of machine learning that uses labeled datasets to train algorithms to predict outcomes and recognize patterns. Unlike unsupervised learning, supervised learning algorithms are given labeled training to learn the relationship between the input and the outputs. Prerequisite: Linear algebra Suppose we have a regression problem where the model needs …
Prototyping Gradient Descent in Machine Learning Read More »
