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. The article explains how to apply Friedman's h-statistic to understand if complex machine learning models use interactions to make predictions. It uses the artemis package and interprets the pairwise, overall, and unnormalised metrics.
  2. PygmalionAI's large-scale inference engine designed for serving Pygmalion models to a large number of users with blazing fast speeds. Integrates work from projects like vLLM, TensorRT-LLM, xFormers, AutoAWQ, AutoGPTQ, SqueezeLLM, Exllamav2, TabbyAPI, AQLM, KoboldAI, Text Generation WebUI, and Megatron-LM.
    2024-06-21 Tags: , , , by klotz
  3. Our study demonstrates that sperm whale vocalisations form a complex combinatorial communication system: the seemingly arbitrary inventory of coda types can be explained by combinations of rhythm, tempo, rubato, and ornamentation features. This study shows that these vocalisations have a significantly greater information capacity than was previously known.
  4. This article explains the PCA algorithm and its implementation in Python. It covers key concepts such as Dimensionality Reduction, eigenvectors, and eigenvalues. The tutorial aims to provide a solid understanding of the algorithm's inner workings and its application for dealing with high-dimensional data and the curse of dimensionality.
  5. use of the LLM to perform next-token prediction, and then convert the predicted next token into a classification label.
    2024-06-21 Tags: , by klotz
  6. Case study on measuring context relevance in retrieval-augmented generation systems using Ragas, TruLens, and DeepEval. Develop practical strategies to evaluate the accuracy and relevance of generated context.
  7. AI agent helping write and fix code, running tests and iterating till code passes tests or matches designs. Uses OpenAI API and aims to make coding easier.

    Micro Agent is an AI agent that assists with coding, helping with code generation and iteration processes. It's a focused agent that aims to write code based on provided test cases or design screenshots. It can work in tandem with OpenAI and Anthropic APIs for better visual matching. The agent is designed with a specific focus - creating a clear test case and providing feedback on code that helps improve the generated code. Installation requires Node.js v14 or later, and it can be installed globally using npm. To get started, running the agent in interactive mode is recommended. Micro Agent can work in both unit test matching mode and visual matching mode for coding assistance. It uses a multi-agent approach and connects with Figma for high fidelity design-to-code conversions. Configuration options are available via CLI or UI.
  8. This is a hands-on guide with Python example code that walks through the deployment of an ML-based search API using a simple 3-step approach. The article provides a deployment strategy applicable to most machine learning solutions, and the example code is available on GitHub.
  9. The highlighted articles cover a variety of topics, including algorithmic thinking for data scientists, outlier detection in time-series data, route optimization for visiting NFL teams, minimum vertex coloring problem solution, high-cardinality features, multilingual RAG (Rapidly-explainable AI) system development, fine-tuning smaller transformer models, long-form visual understanding, multimodal image-text models, the theoretical underpinnings of learning, data science stress management, and reinforcement learning.
  10. This tutorial covers fine-tuning BERT for sentiment analysis using Hugging Face Transformers. Learn to prepare data, set up environment, train and evaluate the model, and make predictions.

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