to tune hyperparamters of deep learning models (Keras Sequential model), in comparison with a traditional approach — Grid Search. ContentsBayesian Optimization1. Surrogate Model (Probabilistic Model)2. Acquisition FunctionBayesian Optimization Strategy (Iterative Process)Step 1. InitializationStep 2. Surrogate Model TrainingStep 3. Acquisition Function OptimizationStep 4. Objective Function EvaluationStep 5. Data UpdateStep 6. IterationResultsComparing with Grid SearchResultsGeneral Comparison with Grid Search1. …

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 »

ContentsLearningBatch Gradient DescentGradientComputationCost Function (Objective Function)Least Minimum Squares (LMS) RuleNormal EquationSimulationPredicting Credit Card Transaction2. Defining Batch GD Regresser3. Prediction & AssessmentStochastic Gradient DescentSimulationConclusion 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 …

Prototyping Gradient Descent in Machine Learning Read More »

Over the last decade, we’ve laid a lot of the foundations for the modern AI era, from pioneering the Transformer architecture on which all large language models are based, to developing agent systems that can learn and plan like AlphaGo and AlphaZero. We’ve applied these techniques to make breakthroughs in quantum computing, mathematics, life sciences …

Gemini as a universal AI assistant Read More »

days, job titles like data scientist, machine learning engineer, and Ai Engineer are everywhere — and if you were anything like me, it can be hard to understand what each of them actually does if you are not working within the field. And then there are titles that sound even more confusing — like quantum blockchain LLM robotic engineer …

Top Machine Learning Jobs and How to Prepare For Them Read More »

ContentsNew Gemini 2.5 capabilitiesNative audio output and improvements to Live API New Gemini 2.5 capabilities Native audio output and improvements to Live API Today, the Live API is introducing a preview version of audio-visual input and native audio out dialogue, so you can directly build conversational experiences, with a more natural and expressive Gemini. It …

Updates to Gemini 2.5 from Google DeepMind Read More »

ContentsIn the first post of this series (Agentic AI 101: Starting Your Journey Building AI Agents), we talked about the fundamentals of creating AI Agents and introduced concepts like reasoning, memory, and tools. Of course, that first post touched only the surface of this new area of the data industry. There is so much more …

Agentic AI 102: Guardrails and Agent Evaluation Read More »

In the , building Machine Learning models was a skill only data scientists with knowledge of Python could master. However, low-code AI platforms have made things much easier now. Anyone can now directly make a model, link it to data, and publish it as a web service with just a few clicks. Marketers can now …

The Automation Trap: Why Low-Code AI Models Fail When You Scale Read More »