klotz: machine learning*

"Machine learning is a subset of artificial intelligence in the field of computer science that often uses statistical techniques to give computers the ability to "learn" (i.e., progressively improve performance on a specific task) with data, without being explicitly programmed.

https://en.wikipedia.org/wiki/Machine_learning

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  1. This article guides readers through running the latest YOLO v10 object detection model on different hardware, specifically on a Raspberry Pi 5, and a computer. The article discusses the importance of computer vision in ML applications, the versatility of YOLO, and the cross-platform code presented in the article.
  2. This article discusses the differences between predictive and causal inference, explains why correlation does not imply causation, and why machine learning is not inherently suited for causal inference. It highlights the limitations of using machine learning for causal estimation and provides suggestions for when each type of inference should be used. The article also touches on causal machine learning and its role in addressing the challenges of high-dimensional data and complex functional forms.
  3. An article discussing recent updates and improvements in several foundation time-series models, including TimeGPT, TimesFM, MOIRAI, Tiny Time Mixers (TTM), and MOMENT. These models, initially released with significant impact, have since seen updates in benchmarks and model variants.
  4. Outlier treatment is a necessary step in data analysis. This article, part 3 of a four-part series, eases the process and provides insights on effective methods and tools for outlier detection.
  5. An explanation of the backpropagation through time algorithm and how it helps Recurrent Neural Networks (RNNs) learn from sequence-based data
  6. A Github Gist containing a Python script for text classification using the TxTail API
  7. OnDemand AI provides API services for media, services, and plugins, allowing developers to upload media, use NLP, and deploy machine learning models. It also facilitates serverless application deployment and allows BYOM (Bring Your Own Model) and BYOI (Bring Your Own Inference).
  8. Exploring and exploiting the seemingly innocent theorem behind Double Machine Learning. The theorem, rooted in econometrics, states that if we have a linear model that predicts an outcome variable based on multiple features, and we want to understand the causal effect of a specific feature on the outcome, we can use the residuals of the model as an instrumental variable to estimate the causal effect.
  9. Walkthrough on building a Q and A pipeline using various tools, and distributing it with ModelKits for collaboration.
    2024-07-10 Tags: , , , , , , by klotz
  10. A website for the Seeed Watcher, a physical AI agent for space management, with features like product catalog, ecosystem, support, and company information.

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