Tag: ai

Chart and Diagram Analysis with Ollama LLaVA: Complete Guide

Unlock automated chart analysis using Ollama LLaVA, a vision model capable of understanding and interpreting complex visuals like bar charts, line graphs, and pie diagrams. This guide demonstrates how to quickly extract data points and identify trends from your reports without manual effort. Built on Large Language Model capabilities alongside computer vision, LLaVA offers key advantages including accurate numerical extraction, trend identification, support for various chart formats (PNG, JPG, PDF), and offline operation – all within a streamlined setup process using Ollama.

GitHub – elder-plinius/OBLITERATUS: OBLITERATE THE CHAINS THAT BIND YOU
OBLITERATUS is a groundbreaking open-source toolkit designed for analyzing and mitigating refusal behaviors in large language models. The project provides a comprehensive, pipeline-driven approach – from pinpointing refusal ‘directions’ within a model’s hidden states to directly intervening at inference time, without retraining. The project facilitates a collaborative research experiment, collecting anonymous benchmarking data that drives advancements in abliteration techniques. A user-friendly Gradio interface on Hugging Face Spaces enables easy experimentation, while a Python API offers granular control for more advanced users.

github.com

The WordPress MCP Adapter facilitates seamless integration between WordPress and the Model Context Protocol (MCP). It acts as a crucial bridge, converting native WordPress abilities—like plugins, themes, and core functionality—into standardized MCP tools, resources, and prompts, enabling AI agents to intuitively interact with your WordPress site. The adapter boasts a flexible architecture with HTTP and STDIO transport options, comprehensive error handling, and built-in observability features—ideal for developers building advanced AI-powered workflows within WordPress.


Home – Welcome to MLBox’s official documentation — MLBox Documentation

MLBox is a powerful Automated Machine Learning python library. It provides the following features:

Fast reading and distributed data preprocessing/cleaning/formatting.
Highly robust feature selection and leak detection.
Accurate hyper-parameter optimization in high-dimensional space.
State-of-the art predictive models for classification and regression (Deep Learning, Stacking, LightGBM,…).
Prediction with models interpretation.


ryanjay0/miles-deep: Deep Learning Porn Video Classifier/Editor with Caffe

Using a deep convolutional neural network with residual connections, Miles Deep quickly classifies each second of a pornographic video into 6 categories based on sexual act with 95% accuracy. Then it uses that classification to automatically edit the video. It can remove all the scenes not containing sexual contact, or edit out just a specific act.


xgboost-node

XGBoost-Node is the first port of XGBoost to run existing XGBoost model with Node.js.

XGBoost is a library from DMLC. It is designed and optimized for boosted trees. The underlying algorithm of XGBoost is an extension of the classic gbm algorithm. With multi-threads and regularization, XGBoost is able to utilize more computational power and get a more accurate prediction.


SmoothGrad

When a machine learning model makes a prediction, often times we would like to determine which features of the input (pixels, for images) were important for the prediction. If the model makes a misprediction, we might want to know which features contributed to the misclassification. We can visualize the feature importance mask as a grayscale image with the same dimensions as the original image with brightness corresponding to importance of the pixel.


Auto-Keras

Auto-Keras is an open source software library for automated machine learning (AutoML). It is developed by DATA Lab at Texas A&M University and community contributors. The ultimate goal of AutoML is to provide easily accessible deep learning tools to domain experts with limited data science or machine learning background. Auto-Keras provides functions to automatically search for architecture and hyperparameters of deep learning models.


cranium for Node.js – Pincer.io

The amount of data you need to build a good classifier increases with the number of features you have, so out of memory errors become a problem when dealing with thousands of features. For example, Weka fails to perform logistic regression with more than a couple thousand features on a 5mb dataset. Cranium never assumes that your instances can fit in memory, so you can use it on terabytes of data.

Cranium works with node streams, so you have a lot of flexibility with your input. Using streams sacrifies speed for memory efficiency — Cranium uses a constant amount of memory that is typically below 100mb. The speed penalty is significant: Cranium runs about 500x slower than LearnKit. If your dataset can fit in memory, Cranium is probably not right for you.