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RLHF For High-Performance Decision-Making: Strategies and Optimization

Analytics Vidhya

It will be engineered to optimize decision-making and enhance performance in real-world complex systems. Introduction Reinforcement Learning from Human Factors/feedback (RLHF) is an emerging field that combines the principles of RL plus human feedback.

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Direct Preference Optimization (DPO): Andrew Ng’s Perspective on the Next Big Thing in AI

Analytics Vidhya

In the dynamic realm of language model development, a recent groundbreaking paper titled “Direct Preference Optimization (DPO)” by Rafael Rafailov, Archit Sharma, Eric Mitchell, Stefano Ermon, Chris Manning, and Chelsea Finn, has captured the attention of AI luminaries like Andrew Ng.

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Mastering AI Optimization and Deployment with Intel’s OpenVINO Toolkit

Analytics Vidhya

The most challenging part of integrating AI into an application is […] The post Mastering AI Optimization and Deployment with Intel’s OpenVINO Toolkit appeared first on Analytics Vidhya. Introduction We talk about AI almost daily due to its growing impact in replacing humans’ manual work.

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ML Hyperparameter Optimization App using Streamlit

Analytics Vidhya

The post ML Hyperparameter Optimization App using Streamlit appeared first on Analytics Vidhya. This article was published as a part of the Data Science Blogathon About Streamlit Streamlit is an open-source Python library that assists developers in creating interactive graphical user interfaces for their systems. Frontend […].

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Easily Build an Optimization App and Empower Your Data

Speaker: Gertjan de Lange

If the last few years have illustrated one thing, it’s that modeling techniques, forecasting strategies, and data optimization are imperative for solving complex business problems and weathering uncertainty. Experience how efficient you can be when you fit your model with actionable data.

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Parameter-Efficient Fine-Tuning of Large Language Models with LoRA and QLoRA

Analytics Vidhya

In this article, we will explore how PEFT methods optimize the adaptation of Large Language Models (LLMs) to specific tasks. We will unravel the advantages and disadvantages of PEFT, […] The post Parameter-Efficient Fine-Tuning of Large Language Models with LoRA and QLoRA appeared first on Analytics Vidhya.

Modeling 211
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Towards optimal experimentation in online systems

The Unofficial Google Data Science Blog

If the relationship of $X$ to $Y$ can be approximated as quadratic (or any polynomial), the objective and constraints as linear in $Y$, then there is a way to express the optimization as a quadratically constrained quadratic program (QCQP). Figure 2: Spreading measurements out makes estimates of model (slope of line) more accurate.

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Guide to Mathematical Optimization & Modeling

For decades, operations research professionals have been applying mathematical optimization to address challenges in the field of supply chain planning, manufacturing, energy modeling, and logistics. This guide is ideal if you: Want to understand the concept of mathematical optimization.

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The Modern Customer Success Playbook

This customer success playbook outlines best in class data-driven strategies to help your team successfully map and optimize the customer journey, including how to: Build a 360-degree view of your customer and drive more expansion opportunities. Satisfaction won’t cut it. But where do you start? Download the playbook today!