Reinforcement Learning
Learn the fundamentals of reinforcement learning and implement basic concepts using Python for data science applications.
Probability
Explore Bayes Theorem, Hidden Markov Models, Shannon Entropy, Naive Bayes classifier, Beta distribution, and Thompson sampling in this friendly introduction to key probability concepts.
Neural Networks
This Course is a friendly introduction series to machine learning, deep learning, neural networks and generative adversarial networks.
Machine Learning
Comprehensive introduction to key machine learning concepts, algorithms, and applications, covering testing, error metrics, recommendation systems, and various classification methods.
Reinforcement Learning
Explore the fundamentals and advanced concepts of Reinforcement Learning in this comprehensive tutorial series.
Reinforcement Learning - Machine Learning Meets Control Theory
Explore reinforcement learning's intersection with control theory, covering key concepts, algorithms, and applications in this comprehensive overview of machine learning's powerful technique.
Data-Driven Dynamical Systems with Machine Learning
Explore modern machine learning methods for modeling complex dynamical systems from data. Learn data-driven control techniques and apply them to various fields of science and engineering.
Reinforcement Learning
Explora el Aprendizaje por Refuerzo, desde métodos básicos hasta redes neuronales y aplicaciones en dinámica de fluidos y control, en esta serie de 3.5 horas por Steve Brunton.
Data-Driven Control with Machine Learning
Explore machine learning techniques for data-driven control, covering system identification, model reduction, and advanced control strategies like MPC and reinforcement learning.
MDPs - Markov Decision Processes - Decision Making Under Uncertainty Using POMDPs.jl
Explore Markov Decision Processes and decision-making under uncertainty using POMDPs.jl. Learn about MDPs, Grid World, solvers, reinforcement learning, and visualization techniques in Julia.
Flux - The Elegant Machine Learning Library for Julia
Explore Flux, an intuitive machine learning library for Julia, featuring state-of-the-art performance, automatic differentiation, and innovative compiler technology for efficient and flexible ML development.
TF-Agents - A Flexible Reinforcement Learning Library for TensorFlow
Explore TF-Agents, a flexible Deep Reinforcement Learning library for TensorFlow. Learn about recent advancements, implementation techniques, and how to jumpstart your RL projects using this modular open-source tool.
TensorFlow and Deep Reinforcement Learning, Without a PhD - Google I/O '18
Learn to implement deep reinforcement learning with TensorFlow, teaching a neural network to play Pong using only pixel data. No prior expertise required.
MIT: Deep Reinforcement Learning
Explore deep reinforcement learning concepts, algorithms, and applications, including Q-learning, policy gradients, and breakthroughs like AlphaGo and AlphaZero in game AI.
Beyond Deep Learning - Learning+Reasoning
Explore advanced AI concepts beyond deep learning, focusing on integrating reasoning with learning for more robust and interpretable systems in this IBM Research lecture.
Introduction to Deep Learning - MIT 2018
Explore foundations of deep learning, including perceptrons, neural networks, loss functions, and optimization techniques. Gain insights into training challenges and strategies for effective model development.
How Data Science Works
Explore the fundamentals of data science through plain English explanations, covering key concepts like feature engineering, machine learning, and deep learning without technical jargon.
Deep Learning in Gaming with Idan Beck
Explore AI/ML applications in gaming, covering navigation, task planning, procedural generation, and reinforcement learning, with insights from Unity expert Idan Beck.
Latent State Recovery in Reinforcement Learning - John Langford
Explore latent state recovery in reinforcement learning, covering core problems, alternative approaches, and state abstraction. Gain insights from John Langford's expertise in theoretical machine learning.
Efficient Robot Skill Learning via Grounded Simulation Learning, Imitation Learning - Peter Stone
Explore efficient robot skill learning through grounded simulation, imitation learning, and off-policy reinforcement learning. Discover innovative approaches for transferring skills from simulation to real-world robots.
The Challenges of Model-Based Reinforcement Learning and How to Overcome Them - Csaba Szepesvári
Explore challenges in model-based reinforcement learning, including modeling errors and efficient planning, and discover strategies to overcome them in complex environments.