Tags: 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. A ready-to-run tutorial in Python and scikit-learn to evaluate a classification model compared to a baseline model
  2. - Embeddings transform words and sentences into sequences of numbers for computers to understand language.
    - This technology powers tools like Siri, Alexa, Google Translate, and generative AI systems like ChatGPT, Bard, and DALL-E.
    - In the early days, embeddings were crafted by hand, which was time-consuming and couldn't adapt to language nuances easily.
    - The 3D hand-crafted embedding app provides an interactive experience to understand this concept.
    - The star visualization method offers an intuitive way to understand word embeddings.
    - Machine learning models like Word2Vec and GloVe revolutionized the generation of word embeddings from large text datasets.
    - Universal Sentence Encoder (USE) extends the concept of word embeddings to entire sentences.
    - TensorFlow Projector is an advanced tool to interactively explore high-dimensional data like word and sentence embeddings.
  3. Apply sound data-based anomalous behavior detection, diagnose the root cause via object detection concurrently, and inform the user via SMS.
  4. With all the hype around AI/ML in observability, it's more likely than ever that companies benefit from storing and viewing data in one system and training ML models in another.
  5. Nvidia Researchers Developed and Open-Sourced a Standardized Machine Learning Framework for Time Series Forecasting

    Nvidia researchers have developed and open-sourced a standardized machine learning framework called TSPP (Time Series Prediction Platform) for time series forecasting. The framework is des
    igned to facilitate the integration and comparison of various models and datasets, covering all aspects of the machine learning process from data handling to model deployment.

    The TSPP framework includes critical components like data handling, model design, optimization, and training, as well as inference, predictions on unseen data, and a tuner component that s
    elects the top configuration for post-deployment monitoring and uncertainty quantification. The methodology of TSPP is comprehensive, covering all aspects of the machine learning process.
    2024-01-05 Tags: , , by klotz

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