Implement Transformers (and Deep Learning) from scratch in NumPy
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Updated
Oct 3, 2023 - Python
Implement Transformers (and Deep Learning) from scratch in NumPy
A deep exploration of loyalty as a multi-dimensional behavioral system shaped by intent, habit, and sensitivity. This article introduces a geometric framework for modeling customer behavior, predicting churn trajectories, and designing ML systems that understand loyalty as a dynamic state, not a metric.
A tiny deep neural network framework developed from scratch in C++ and CUDA.
A template for every machine learning project
Pure Go machine learning framework. Train, run, and serve ML models with go build. Zero CGo.
ML Pipeline Automation Tool - Chain together data processing, model training, and deployment with minimal code. Build production-ready ML workflows in minutes, not hours.
An Agent-Computer Interface (ACI) for AI-driven machine learning.
Deep Classiflie is a framework for developing ML models that bolster fact-checking efficiency. As a POC, the initial alpha release of Deep Classiflie generates/analyzes a model that continuously classifies a single individual's statements (Donald Trump) using a single ground truth labeling source (The Washington Post). For statements the model d…
🤖 Solve ARC-AGI challenges efficiently using LLM-powered agentic AI for high accuracy and robust task performance.
Simple machine learning framework for Timeseries application to identify anomaly in dataset using Machine learning and Deep neural network
Deep_classiflie_db is the backend data system for managing Deep Classiflie metadata, analyzing Deep Classiflie intermediate datasets and orchestrating Deep Classiflie model training pipelines. Deep_classiflie_db includes data scraping modules for the initial model data sources. Deep Classiflie depends upon deep_classiflie_db for much of its anal…
Machine Learning 101
Official Repo of OpenArchX Framework.
A machine learning framework with easy-to-use functions from Pytorch in Python.
Official Repo of OpenArchX Framework.
A Pythonic approach to object detection using Detectron2, a clean, modular framework for training and deploying computer vision models. DetectX simplifies the complexity of object detection while maintaining high performance and extensibility.
Personal ML Journey
Unified AI/ML models repository with support for Gemini, OpenAI, Claude, DeepSeek, HuggingFace, and more. Production-ready implementations with streaming, async support, and comprehensive testing.
Advanced regression analysis suite featuring KNN optimization, multi-algorithm comparison, hyperparameter tuning with Optuna, and production-ready ML pipelines with comprehensive model evaluation and visualization.
Educational ML library implementing core algorithms from scratch with emphasis on intuition, failure modes, and learning theory.
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