This is the code repository for Causal Inference and Discovery in Python, published by Packt. Unlock the secrets of modern causal machine learning with DoWhy, EconML, PyTorch and more.
This project walks you through building your own Inventory Robot that can automatically identify, organize, and track the components you use in your workspace. After finishing a prototype, placing every part back in the right spot can become time-consuming and difficult. This robot solves that by intelligently sorting and maintaining a clear inventory of your items.
This project guides you through building a portable AI agent using the UNIHIKER K10, Xiaozhi AI firmware, and a custom 3D-printed case. It covers hardware overview, firmware flashing, console setup, and 3D printing services.
Cisco and Splunk have introduced the Cisco Time Series Model, a univariate zero shot time series foundation model designed for observability and security metrics. It is released as an open weight checkpoint on Hugging Face.
* **Multiresolution data is common:** The model handles data where fine-grained (e.g., 1-minute) and coarse-grained (e.g., hourly) data coexist, a typical pattern in observability platforms where older data is often aggregated.
* **Long context windows are needed:** It's built to leverage longer historical data (up to 16384 points) than many existing time series models, improving forecasting accuracy.
* **Zero-shot forecasting is desired:** The model aims to provide accurate forecasts *without* requiring task-specific fine-tuning, making it readily applicable to a variety of time series datasets.
* **Quantile forecasting is important:** It predicts not just the mean forecast but also a range of quantiles (0.1 to 0.9), providing a measure of uncertainty.
* **Multiresolution data is common:** The model handles data where fine-grained (e.g., 1-minute) and coarse-grained (e.g., hourly) data coexist, a typical pattern in observability platforms where older data is often aggregated.
* **Long context windows are needed:** It's built to leverage longer historical data (up to 16384 points) than many existing time series models, improving forecasting accuracy.
* **Zero-shot forecasting is desired:** The model aims to provide accurate forecasts *without* requiring task-specific fine-tuning, making it readily applicable to a variety of time series datasets.
* **Quantile forecasting is important:** It predicts not just the mean forecast but also a range of quantiles (0.1 to 0.9), providing a measure of uncertainty.
Amazon S3 Vectors is now generally available with increased scale and production-grade performance capabilities. It offers native support to store and query vector data, potentially reducing costs by up to 90% compared to specialized vector databases.
This article details the steps to move a Large Language Model (LLM) from a prototype to a production-ready system, covering aspects like observability, evaluation, cost management, and scalability.
This article explains the Greedy Boruta algorithm, a faster alternative to the traditional Boruta algorithm for feature selection. It details how it works, its advantages, and provides a Python implementation.
This article details how to train an image classification model on an ESP32 using both the SenseCraft AI platform and a custom TensorFlow Lite implementation. It covers setting up binary classification, training the model, and deploying it on ESP32-S3 devices.
A deep dive into the process of LLM inference, covering tokenization, transformer architecture, KV caching, and optimization techniques for efficient text generation.
"Talk to your data. Instantly analyze, visualize, and transform."
Analyzia is a data analysis tool that allows users to talk to their data, analyze, visualize, and transform CSV files using AI-powered insights without coding. It features natural language queries, Google Gemini integration, professional visualizations, and interactive dashboards, with a conversational interface that remembers previous questions. The tool requires Python 3.11+, a Google API key, and uses Streamlit, LangChain, and various data visualization libraries
Analyzia is a data analysis tool that allows users to talk to their data, analyze, visualize, and transform CSV files using AI-powered insights without coding. It features natural language queries, Google Gemini integration, professional visualizations, and interactive dashboards, with a conversational interface that remembers previous questions. The tool requires Python 3.11+, a Google API key, and uses Streamlit, LangChain, and various data visualization libraries
