Deep Learning

Deep Reinforcement Learning in Python: A Modern Introduction

Reinforcement Learning

Master the fundamentals of training intelligent agents using Python, PyTorch, and modern reinforcement learning algorithms like A2C and DDPG.

★ 4.7 (3,889)

Practical Reinforcement Learning in Python: Build Intelligent AI Agents

Reinforcement Learning

Master the fundamentals of deep reinforcement learning and build custom intelligent agents using Python, TensorFlow, and Gymnasium.

★ 4.6 (1,290)

Deep Reinforcement Learning with Python: Train Virtual Agents with TD3

Reinforcement Learning

Master the foundations of reinforcement learning and implement the advanced TD3 algorithm in Python to train virtual agents to walk, run, and navigate complex environments.

★ 4.7 (1,367)

Deep Reinforcement Learning: Implement Deep Q Agents from Papers

Reinforcement Learning

Read reinforcement learning research papers and implement Deep Q, Double Deep Q, and Dueling Deep Q networks from scratch using PyTorch and Gymnasium.

★ 4.3 (1,112)

Deep Reinforcement Learning: Implementing Research Papers in PyTorch and TensorFlow

Reinforcement Learning

Learn to translate complex AI research into functional code by building advanced agents for continuous control and decision-making tasks.

★ 4.3 (530)

Deep Reinforcement Learning with PyTorch: From DQN to SAC

Reinforcement Learning

Build and train intelligent AI agents from scratch using PyTorch and Gymnasium to solve complex decision-making and control tasks.

★ 4.3 (191)

Reinforcement Learning in Python: Build AI Agents with PyTorch and Gym

Reinforcement Learning

Learn to design, train, and evaluate intelligent AI agents from scratch using Python, PyTorch, and standard Gym simulation environments.

★ 4.3 (402)

Reinforcement Learning: Prediction and Control with Function Approximation

Reinforcement Learning

Scale reinforcement learning agents to large, continuous state spaces using value function approximation and modern neural networks.

★ 4.8 (848)

Foundations of Deep Learning and Reinforcement Learning

Reinforcement Learning

Understand the principles of neural networks and reward-based learning to build a solid foundation in modern artificial intelligence.

★ 4.6 (297)

Reinforcement Learning with Python for Beginners

Reinforcement Learning

Build and train intelligent agents to solve complex tasks and play games using modern Python libraries and core reinforcement learning principles.

★ 4.3 (164)

Introduction to Reinforcement Learning: From Q-Learning to Deep RL

Reinforcement Learning

Master foundational reinforcement learning concepts and implement key algorithms to solve complex decision-making problems through clear written explanations and code.

★ 4.7 (150)

Deep Reinforcement Learning Fundamentals

Reinforcement Learning

Learn to build intelligent agents that solve complex tasks by combining deep neural networks with reinforcement learning principles.

★ 5.0 (124)

Reinforcement Learning for Operations Research

Reinforcement Learning

Learn to solve complex scheduling, routing, and resource allocation problems by training intelligent decision-making agents using Python.

★ 4.4 (58)

Building Reward Learning Agents

Reinforcement Learning

Learn to design, structure, and implement intelligent agents that adapt and learn from rewards, suitable for beginners.

Reinforcement Learning Foundations for Engineers

Reinforcement Learning

Master the core principles of reinforcement learning to design, train, and evaluate intelligent agents that solve complex decision-making problems.

Reward Learning for AI Agents: Selection, Reflection, and Feedback

Reinforcement Learning

Build aligned AI agents by implementing reward selection, reflection, and human feedback loops using modern agent development frameworks.

Reinforcement Learning Fundamentals for Beginners

Reinforcement Learning

Build a strong foundation in reward-based machine learning by understanding agents, environments, and modern applications like RLHF through clear written explanations.

Reinforcement Learning: Core Concepts Explained

Reinforcement Learning

Learn the essential principles of how AI agents learn through interaction and feedback, enabling you to grasp their real-world uses.

Reward Design in Reinforcement Learning with LLMs

Reinforcement Learning

Empower your reinforcement learning agents by automating reward function creation using large language models.

Foundations of Unsupervised, Semi-supervised, and Reinforcement Learning

Reinforcement Learning

Understand the core concepts and practical applications of advanced machine learning paradigms to build intelligent systems.

Decision Making in Dynamic Environments: Foundations of Multi-Agent Systems

Reinforcement Learning

Learn how intelligent agents make strategic decisions, interact in complex environments, and adapt to constant change through clear, written explanations.

Imitation Learning with GAIL and PyBullet Gym

Reinforcement Learning

Understand and apply Generative Adversarial Imitation Learning (GAIL) to train agents in physics-based PyBullet Gym environments.

Loop Control for AI Reward Learning Agents

Reinforcement Learning

Develop foundational skills to implement robust loop control and exit conditions in AI reward learning systems.

Reinforcement Learning: Adaptive Agent Evaluation

Reinforcement Learning

Gain foundational knowledge to understand and evaluate adaptive reinforcement learning agents and their self-improvement mechanisms.