Contents# Introduction# What Transformers.js Actually Is# The pipeline() API# Task 1: Text Classification// Full Working Example# Task 2: Zero-Shot Classification// How It Works Under the Hood// Full Working Example # Introduction  For a long time, running transformer models meant maintaining a Python server, paying for GPU time, and routing every inference request through an API. The user typed something, it left their machine, …

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Introducing Google Antigravity 2.0

coding agents sequentially and not in multiple runs in parallel, you’re losing out. One of the key benefits of coding agents is that you can start completing work in parallel, something that was never really possible before when working on software engineering tasks. However, when I start running a lot of parallel coding sessions, it’s …

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  Contents# Introduction# Visualizing Gradients, Losses, and Embeddings# TensorBoard and Its Alternatives# Using Breakpoints and Hooks for Machine Learning Computations# Conclusion # Introduction  Training a machine learning model and observing the loss decrease is a feeling of progress, until the validation accuracy reaches a plateau or the loss begins to spike, and you’re not sure what caused it. At that point, …

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Last year, Nano Banana brought Gemini’s intelligence to image generation and editing. Since then, it’s helped millions of people restore old photos, design from sketches and visualize ideas in ways that weren’t possible before. From the start we built Gemini to be natively multimodal from the ground up, and now we’re taking the next step. …

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ContentsIntroduction to AI AgentsWhat are AI Agents?How does an AI Agent work?Building an AI Educational Agent in PythonInstalling PythonConclusion Introduction to AI Agents of the decade. You hear it everywhere on job descriptions, tech companies’ profiles, freelancers’ projects, etc. As overwhelming as it may sound, building an AI Agent is not that difficult. On the …

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  Contents# Introduction# From Conversation to Agency# The Architectural Reuse# Tool 1: A Sandboxed Filesystem Explorer# Tool 2: A Restricted Python Interpreter# The Orchestration Loop# Testing the Tools# Conclusion # Introduction  In a recent article on Machine Learning Mastery, we built a tool-calling agent that reached outward, that is pulling weather, news, currency rates, and time from public APIs. That article covered the synthesis …

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The Asia-Pacific region is a global engine for economic growth, but it’s also highly vulnerable to climate change. While green technologies are gaining momentum, a recent report shows they aren’t scaling fast enough to keep up with the region’s rising environmental risks. To help innovators tackle these environmental challenges, we’re launching an inaugural Google DeepMind …

We’re launching the Google DeepMind Accelerator program in Asia Pacific to tackle environmental risks. Read More »

themes from a call corpus to the customer table. Customers without transcripts get NULL. NULL gets filled with zero, or with “no issue mentioned,” or quietly omitted as a reference category. In one line of preprocessing, the pipeline converts did not call support into did not experience billing frustration. The regression that follows looks clean. …

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  Contents# Introduction# Step-by-Step Procedure# Wrapping Up # Introduction  Production data is typically subject to notable privacy and compliance constraints. For this reason, anonymizing such data becomes critical in virtually every real-world data science project involving the launch of a data-driven product, service, or solution. Mimesis is an open-source Python library that stands out for its ability to generate …

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