Perplexity’s CEO Sees AI Agents as the Next Web Battleground

[ad_1] Wait though … Perplexity—like other AI search engines—has been criticized for hallucinating and getting things wrong. We welcome this ...
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Data Drift Is Not the Actual Problem: Your Monitoring Strategy Is

[ad_1] is an approach to accuracy that devours data, learns patterns, and predicts. However, with the best models, even those ...
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Evaluating LLMs for Inference, or Lessons from Teaching for Machine Learning

[ad_1] opportunities recently to work on the task of evaluating LLM Inference performance, and I think it’s a good topic ...
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LLM Optimization: LoRA and QLoRA | Towards Data Science

[ad_1] With the appearance of ChatGPT, the world recognized the powerful potential of large language models, which can understand natural ...
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GAIA: The LLM Agent Benchmark Everyone’s Talking About

[ad_1] were making headlines last week. In Microsoft’s Build 2025, CEO Satya Nadella introduced the vision of an “open agentic ...
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From Data to Stories: Code Agents for KPI Narratives

[ad_1] , we often need to investigate what’s going on with KPIs: whether we’re reacting to anomalies on our dashboards ...
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Bayesian Optimization for Hyperparameter Tuning of Deep Learning Models

[ad_1] to tune hyperparamters of deep learning models (Keras Sequential model), in comparison with a traditional approach — Grid Search. Bayesian Optimization ...
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Why Regularization Isn’t Enough: A Better Way to Train Neural Networks with Two Objectives

[ad_1] neural networks, we often juggle two competing objectives. For example, maximizing predictive performance while also meeting a secondary goal like fairness, ...
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Code Agents: The Future of Agentic AI

[ad_1] of AI agents. LLMs are no longer just tools. They’ve become active participants in our lives, boosting productivity and ...
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Prototyping Gradient Descent in Machine Learning

[ad_1] Learning Supervised learning is a category of machine learning that uses labeled datasets to train algorithms to predict outcomes ...
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