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Weβre extending Gemini to become a world model that can make plans and imagine new experiences by simulating aspects of the world.
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Announcing Gemma 3n preview: Powerful, efficient, mobile-first AI
Gemma 3n is a cutting-edge open model designed for fast, multimodal AI on devices, featuring optimized performance, unique flexibility with a 2-in-1 model, and expanded multimodal understanding with audio, empowering developers to build live, interactive applications and sophisticated audio-centric experiences.
Fuel your creativity with new generative media models and tools
Introducing Veo 3 and Imagen 4, and a new tool for filmmaking called Flow.
The Ultimate Guide to Learning Anything with NotebookLM
Learn about turning your notes and sources into a personalized, AI-powered tutor with NotebookLM.
WTF is Language Model Quantization?!?
Unveiling the origins, "ins and outs," and implications of quantization in language models: all in simple terms.
7 Best FREE Platforms to Host Machine Learning Models
Built an ML model? Here are 7 free platforms to share it with the world.
How to Set the Number of Trees in Random Forest
A practical introduction to the optRF package The post How to Set the Number of Trees in Random Forest appeared first on Towards Data Science.
How to Build an AI Journal with LlamaIndex
A step-by-step guide for building an AI assistant powered by LlamaIndex The post How to Build an AI Journal with LlamaIndex appeared first on Towards Data Science.
The Automation Trap: Why Low-Code AI Models Fail When You Scale
Low-code AI platforms promise speed, a model without a single line of code. But when you try to scale them, they deliver something else: silent crashes, cryptic failures, and models that choke under pressure. The post The Automation Trap: Why Low-Code AI Models Fail When You Scale appeared first on Towards Data Science.
Agentic AI 102: Guardrails and Agent Evaluation
An introduction to tools that make your model safer and more predictable and performant. The post Agentic AI 102: Guardrails and Agent Evaluation appeared first on Towards Data Science.
A Guide to Mastering Serverless Machine Learning
Discover the what, why, and how of serverless machine learning with the interactive course GitHub repository.
A Gentle Introduction to Learning Rate Schedulers
Ever wondered why your neural network seems to get stuck during training, or why it starts strong but fails to reach its full potential? The culprit might be your learning rate β arguably one of the most important hyperparameters in machine learning.
Qwen2.5-Omni is a Powerhouse: A Guide with Demo Project
This article guides you throughout a demo project to set up and run an instance of this powerful multi-modal model in a Python script or notebook.
Googleβs AlphaEvolve IsΒ EvolvingΒ New Algorithms β And It Could Be a Game Changer
A blend of LLMs' creative generation capabilities with genetic algorithms The post Googleβs AlphaEvolve IsEvolvingNew Algorithms β And It Could Be a Game Changer appeared first on Towards Data Science.
Understanding Random Forest using Python (scikit-learn)
A Random Forest is a powerful machine learning algorithm that can be used for classification and regression, is interpretable, and doesnβt require feature scaling. Hereβs how to apply it. The post Understanding Random Forest using Python (scikit-learn) appeared first on Towards Data Science.
How to Learn the Math Needed for MachineΒ Learning
A breakdown of the three fundamental math fields required for machine learning: statistics, linear algebra, and calculus. The post How to Learn the Math Needed for MachineLearning appeared first on Towards Data Science.
How To Build a Benchmark for Your Models
The Importance of Building a Benchmark and How To Do It The post How To Build a Benchmark for Your Models appeared first on Towards Data Science.
πͺπͺπ Lessons in Decision Making from the Monty Hall Problem
A journey into three intuitions: Common, Bayesian and Causal The post Lessons in Decision Making from the Monty Hall Problem appeared first on Towards Data Science.
Building AI Agents? A2A vs. MCP Explained Simply
Confused by AI agent frameworks? This article makes sense of A2A and MCP.
7 AWS Services for Machine Learning Projects
Learn about the AWS machine learning service that helps you build machine learning pipelines, from processing data to training and deploying models.
The Geospatial Capabilities of Microsoft Fabric and ESRI GeoAnalytics, Demonstrated
A step closer to spatial AI with geospatial processing with Fabric The post The Geospatial Capabilities of Microsoft Fabric and ESRI GeoAnalytics, Demonstrated appeared first on Towards Data Science.
Efficient Graph Storage for Entity Resolution Using Clique-Based Compression
Entity resolution systems face challenges with dense, interconnected graphs, and clique-based graph compression offers an efficient solution by reducing storage overhead and improving system performance during data deletion and reprocessing. The post Efficient Graph Storage for Entity Resolution Using Clique-Based Compression appeared first on Towards Data Science.
Parquet File Format β Everything You Need to Know!
New data flavors require new ways for storing it! Learn everything you need to know about the Parquet file format The post Parquet File Format β Everything You Need to Know! appeared first on Towards Data Science.
A Data Scientistβs Guide to Data Streaming
This guide introduces data streaming from a data science perspective. Weβll explain what it is, why it matters, and how to use tools like Apache Kafka, Apache Flink, and PyFlink to build real-time pipelines.
AlphaEvolve: A Gemini-powered coding agent for designing advanced algorithms
New AI agent evolves algorithms for math and practical applications in computing by combining the creativity of large language models with automated evaluators
10 Essential Linux File System Commands for Data Management
In this article, you'll master 10 essential Linux file system commands. This guide provides helpful examples to make working with files easier.
Custom Fine-Tuning for Domain-Specific LLMs
Fine-tuning a large language model (LLM) is the process of taking a pre-trained model β usually a vast one like GPT or Llama models, with millions to billions of weights β and continuing to train it, exposing it to new data so that the model weights (or typically parts of them) get updated.
4 Data Analytics Project To Impress Your Next Employer
Add these 4 data analytic-based projects to your resume to land your next job.
Survival Analysis When No One Dies: A Value-Based Approach
A generalized version of Kaplan-Meier allows to model a continuous value (like money) instead of a binary signal (like survival) The post Survival Analysis When No One Dies: A Value-Based Approach appeared first on Towards Data Science.
Get Started with Rust: Installation and Your First CLI Tool β A Beginnerβs Guide
From setup to your first command line application β step by step The post Get Started with Rust: Installation and Your First CLI Tool β A Beginnerβs Guide appeared first on Towards Data Science.
Non-Parametric Density Estimation: Theory and Applications
A theoretical and practical introduction to non-parametric density estimation. The post Non-Parametric Density Estimation: Theory and Applications appeared first on Towards Data Science.
Rethinking the Environmental Costs of Training AI β Why We Should Look Beyond Hardware
A statistical analysis of what drives energy, water, and carbon consumption in AI training β and whether hardware improvements are enough. The post Rethinking the Environmental Costs of Training AI β Why We Should Look Beyond Hardware appeared first on Towards Data Science.
TDS Authors Can Now Receive Payments Via Stripe
The Author Payment Program just became a lot more streamlined The post TDS Authors Can Now Receive Payments Via Stripe appeared first on Towards Data Science.
Daily Habits of Top 1% Freelancers in Data Science
Stop guessing and start applying the 5 daily habits that turn average freelancers into 6-figure earners.
Roadmap to Python in 2025
Python has evolved from a simple scripting language to the backbone of modern data science and machine learning.