Most people think hyperparameter tuning is a magic speed boost for ML. They're overthinking it. Here's what actually works ↓ I recently worked with a real student dataset and tested this. Four different classifiers. All carefully tuned. Performance barely moved. Why? Because the defaults were already strong. The dataset was small. The signal was weak. And we used rigorous nested cross‑validation with statistical tests. No hype. No leakage. Just honest validation. In that setup, tuning didn’t sav
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95% of AI work runs on 20-50B parameter models—deployed across sectors to boost efficiency and productivity. Speaking at the ‘World Economic Forum Annual Meeting’ in Davos, Shri Ashwini Vaishnaw, Hon’ble Minister for Ministry of Electronics & Information Technology, Government of India, Railways and Information & Broadcasting, highlighted India's bouquet of such models sharing the government’s focus on massive AI diffusion across diverse sectors. Know more about the upcoming India AI Impact Summ
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🚨 #Davos2026 | Nearly 95% of AI work can be handled by 20–50 billion parameter models, says Union Minister Ashwini Vaishnaw, as India deploys AI across sectors to boost productivity and efficiency. He adds that Stanford ranks India 3rd globally in AI penetration, preparedness, and talent. For more news & updates, visit 🔗 https://www.fortuneindia.com/ #AI #IndiaAI #AshwiniVaishnaw #WEF2026 #Davos #DigitalIndia | Fortune India
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