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.
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Pure Go machine learning framework. Train, run, and serve ML models with go build. Zero CGo.
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Simple machine learning framework for Timeseries application to identify anomaly in dataset using Machine learning and Deep neural network
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Official Repo of OpenArchX Framework.
A machine learning framework with easy-to-use functions from Pytorch in Python.
Machine Learning 101
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.
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.
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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.
Hybrid Reasoning Policy Optimization (HRPO): a research prototype for hybrid latent reasoning with RL.
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