Machine Learning

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 »

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 »

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 also allows the user to steer its tone, accent and style …

Updates to Gemini 2.5 from Google DeepMind Read More »

In 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 »

will share how to build an AI journal with the LlamaIndex. We will cover one essential function of this AI journal: asking for advice. We will start with the most basic implementation and iterate from there. We can see significant improvements for this function when we apply design patterns like Agentic Rag and multi-agent workflow. …

How to Build an AI Journal with LlamaIndex Read More »

Scientific publication T. M. Lange, M. Gültas, A. O. Schmitt & F. Heinrich (2025). optRF: Optimising random forest stability by determining the optimal number of trees. BMC bioinformatics, 26(1), 95. Follow this LINK to the original publication. Forest — A Powerful Tool for Anyone Working With Data What is Random Forest? Have you ever wished …

How to Set the Number of Trees in Random Forest Read More »

AlphaEvolve imagined as a genetic algorithm coupled to a large language model. Picture created by the author using various tools including Dall-E3 via ChatGPT. Models have undeniably revolutionized how many of us approach coding, but they’re often more like a super-powered intern than a seasoned architect. Errors, bugs and hallucinations happen all the time, and …

Google’s AlphaEvolve Is Evolving New Algorithms — And It Could Be a Game Changer Read More »

is one of the key techniques for reducing the memory footprint of large language models (LLMs). It works by converting the data type of model parameters from higher-precision formats such as 32-bit floating point (FP32) or 16-bit floating point (FP16/BF16) to lower-precision integer formats, typically INT8 or INT4. For example, quantizing a model to 4-bit …

Boost 2-Bit LLM Accuracy with EoRA Read More »