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Collection of awesome LLM apps with RAG using OpenAI, Anthropic, Gemini and opensource models.
Machine Learning Journal for Intermediate to Advanced Topics.
Official community-driven Azure AI Examples
This repository showcases various advanced techniques for Retrieval-Augmented Generation (RAG) systems. RAG systems combine information retrieval with generative models to provide accurate and cont…
Kaggle-Replica of the original 1993 competition
Deep Reinforcement Learning AI Approach to Control HVAC Systems
A Dual-Stage Attention-Based Recurrent Neural Network for Time Series Prediction
Example 📓 Jupyter notebooks that demonstrate how to build, train, and deploy machine learning models using 🧠 Amazon SageMaker.
Machine Learning and Artificial Intelligence for Medicine.
Practice your pandas skills!
Hands-on Deep Reinforcement Learning, published by Packt
Python interface for reading and writing GRIB data
All Algorithms implemented in Python
Generac (and other models) Generator Monitoring using a Raspberry Pi and WiFi
A collection of design patterns/idioms in Python
python client library for Radio Thermostat wifi-enabled home thermostats
Radio Thermostat CT50 & CT80 REST API notes
An opinionated list of awesome Python frameworks, libraries, software and resources.
Logistic Regression in Spark Streaming with Online Updating
Differentiable ODE solvers with full GPU support and O(1)-memory backpropagation.
An adversarial example library for constructing attacks, building defenses, and benchmarking both
A platform for Reasoning systems (Reinforcement Learning, Contextual Bandits, etc.)
Dopamine is a research framework for fast prototyping of reinforcement learning algorithms.
A curated list of the most important and useful resources about elasticsearch: articles, videos, blogs, tips and tricks, use cases. All about Elasticsearch!
MangoAutomation / BACnet4J
Forked from mlohbihler/BACnet4JBACnet/IP stack written in Java. Forked from http://sourceforge.net/projects/bacnet4j/
Talk on Reinforcement Learning and Multi-Armed Bandits for the Data Incubator
Developed an online BPTT based deep learning system for multiple look ahead predictions. Achieved 9.7 percent mean relative error for t+4 predictions.