Practical Computer Simulations for Product Analysts | by Mariya Mansurova | Apr, 2024
With more iterations, we can see more modes (since more occurrences of the outlier are rarer), but all the confidence ...
Tag: Apr
Capture and Unlock Knowledge: A guide to foster your AI Business Plan | by Raul Vizcarra Chirinos | Apr, 2024
Photo by Pawel Czerwinski on Unsplashai solutions aren’t just a tool approach; it’s about well-understood use cases and ways to ...
Temperature Scaling and Beam Search Text Generation in LLMs, for the ML-Adjacent | by Mike Cvet | Apr, 2024
The most natural way to use a model to build an output sequence is to gradually predict the next best ...
Reinforcement Learning, Part 2: Policy Evaluation and Improvement | by Vyacheslav Efimov | Apr, 2024
From data to decisions: maximizing rewards with policy improvement methods for optimal strategiesReinforcement learning is a domain in machine learning ...
Designing the Relationship Between LLMs and User Experience | by Janna Lipenkova | Apr, 2024
How to make your LLM do the right things, and do them rightA while ago, I wrote the article Choosing ...
(Un)Objective Machines: A Look at Historical Bias in Machine Learning | by Gretel Tan | Apr, 2024
A deep dive into biases in machine learning, with a focus on historical (or social) biases.Humans are biased. To anyone ...
How Do We Know if AI Is Smoke and Mirrors? | by Stephanie Kirmer | Apr, 2024
Musings on whether the “ai Revolution” is more like the printing press or crypto. (Spoiler: it’s neither.)Photo by Daniele Levis ...
Pushing RL Boundaries: Integrating Foundational Models, e.g. LLMs and VLMs, into Reinforcement Learning | by Elahe Aghapour & Salar Rahili | Apr, 2024
In-Depth Exploration of Integrating Foundational Models such as LLMs and VLMs into RL Training LoopAuthors: Elahe Aghapour, Salar RahiliOverview:With the ...
Quantizing the AI Colossi. Streamlining Giants Part 2: Neural… | by Nate Cibik | Apr, 2024
To ground our investigation into quantization, it is important to reflect on exactly what we mean by “quantizing” numbers. So ...
Learning Generalist Models for Anomaly Detection | by Guansong Pang | Apr, 2024
Generalist Anomaly Detection (GAD) aims to train one single detection model that can generalize to detect anomalies in diverse datasets ...
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