Drench yourself in Deep Learning, Reinforcement Learning, Machine Learning, Computer Vision, and NLP by learning from these exciting lectures!!
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Updated
Feb 2, 2024 - HTML
Drench yourself in Deep Learning, Reinforcement Learning, Machine Learning, Computer Vision, and NLP by learning from these exciting lectures!!
For deep RL and the future of AI.
Source codes for the book "Reinforcement Learning: Theory and Python Implementation"
Collection of stats, modeling, and data science tools in Python and R.
Machine learning and data science blog.
A decentralised credit scoring platform based on Multichain blockchain
Reinforcement Learning Environments for Sustainable Energy Systems
This is yangxx's repo for machine learning
Implementation of RL in the cloud for energy minimization due to migration and excess power consumption.
🤖 Reinforcement Learning paper summaries, notebooks, and articles.
This repository presents a multi-agent reinforcement learning approach for energy-efficient collaborative control of base stations in 5G massive MIMO cellular networks.
An open-source project for Blender 3D connects all the controls within Blender to an AI, allowing the AI to learn how to use the program through trial-and-error. This results in an efficient AI assistant for Blender, allowing for the smooth creation of new models, improvement of existing ones, automatic material generation, and optimized workflows.
This repository contains homework assignments and projects completed for the course "Advanced Topics in Neuroscience" instructed by Dr. Ali Ghazizadeh at Sharif University of Technology.
Implementation of Deep Deterministic Policy Gradients (DDPG) to teach a Quadcopter How to Fly!
A matlab implementation of Fuzzy Q-Learning for making cloud auto-scaling more intelligent through online policy learning
A Deep Q Reinforcement Learning Demo
Network generation for paper Multi-Agent Reinforcement Learning for Active Voltage Control on Power Distribution Networks.
Auto-DL helps you make Deep Learning models without writing a single line of code and giving as little input as possible.
Markov Decision Processes in Python
Learning Continuous Control in Deep Reinforcement Learning
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