This Repository contains a series of google colab notebooks which I created to help people dive into deep reinforcement learning.This notebooks contain both theory and implementation of different algorithms.

Reinforcement Learning and Decision Making tutorials explained at an intuitive level and with Jupyter Notebooks

The Machine Learning project including ML/DL projects, notebooks, cheat codes of ML/DL, useful information on AI/AGI and codes or snippets/scripts/tasks with tips.

TensorFlow and Deep Learning Tutorials

Curso de Aprendizaje por Refuerzo, de 0 a 100 con notebooks y slides muy sencillas para entenderlo todo perfectamente.

Implementation notebooks and scripts of Deep Reinforcement learning Algorithms in PyTorch and TensorFlow.

Notebooks and exercises for the Fast Deep Reinforcement Learning Course https://courses.dibya.online/p/fastdeeprl

Reinforcement Learning Notebooks

My solution notebooks for the Deep Reinforcement Learning Nanodegree by Udacity

Deep Reinforcement Learning Course Notebooks: Model and Algorithm Implementations and Experiments

Collection of Jupiter Notebooks I have made. This could be anything from Code Alongs to Model Testing

Reinforcement Learning Notebooks

Collection of Notebooks in Google Colab directly usable for study activities

Collection of Machine Learning Jupyter Notebooks.

All my course projects, exercises and notebooks of Microsoft LEarn Courses.

🎬 Movie Recommender System using Collaborative Filtering 🔍 Suggests movies based on user ratings and similarity with others 🤖 Step-by-step ML pipeline: preprocessing → similarity calculation → recommendations 📊 Tools: Python, Pandas, Scikit-learn, Jupyter Notebook

This repository consists of notebook, backtesting logs and dataset along with the Problem Statement. This is was our approach to KDSH 2024 by Zelts Labs

Fly Quadcopter using Deep Reinforcement Learning

A deep learning implementation to recognize single integers from integers. Implemented with tensorflow. **Requires IPython Notebooks to be run ;)

This notebook is part of the first assignment for the Deep Reinforcement Learning (DRL) course. It implements a simplified grid-world environment modeled as a deterministic Markov Decision Process (MDP). The purpose of the notebook is to practice key reinforcement learning concepts, including state transitions, rewards, and termination conditions.