Become a Deep Reinforcement Learning Expert Nanodegree for free

Download and Learn Become a Deep Reinforcement Learning Expert Udacity Nanodegree Course 2023 for free with google drive download link.

Learn the deep reinforcement learning skills that are powering amazing advances in AI. Then start applying these to applications like video games and robotics.

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What You Will Learn in Deep Reinforcement Learning Expert Nanodegree

Deep Reinforcement Learning

4 months to complete

Learn cutting-edge deep reinforcement learning algorithms—from Deep Q-Networks (DQN) to Deep Deterministic Policy Gradients (DDPG). Apply these concepts to train agents to walk, drive, or perform other complex tasks, and build a robust portfolio of deep reinforcement learning projects.

Deep Reinforcement Learning Expert Into Video:

Prerequisite knowledge

This program requires experience with Python, probability, machine learning, and deep learning.

See detailed requirements Below ????

• Intermediate to advanced Python experience. You are familiar with object-oriented programming. You can write nested for loops and can read and understand code written by others.
• Intermediate statistics background. You are familiar with probability.
• Intermediate knowledge of machine learning techniques. You can describe backpropagation, and have seen a few examples of neural network architecture (like a CNN for image classification).
• You have seen or worked with a deep learning framework like TensorFlow, Keras, or PyTorch before.

Foundations of Reinforcement Learning Master

the fundamentals of reinforcement learning by writing your own implementations of many classical solution methods.

Value-Based Methods

Apply deep learning architectures to reinforcement learning tasks. Train your own agent that navigates a virtual world from sensory data.

Project – Navigation

Leverage neural networks to train an agent to navigate a virtual world and collect as many yellow bananas as possible while avoiding blue bananas.

Policy-Based Methods

Learn the theory behind evolutionary algorithms and policy-gradient methods. Design your own algorithm to train a simulated robotic arm to reach target locations.

Project – Continuous Control

Train a robotic arm to reach target locations. For an extra challenge, train a four-legged virtual creature to walk!

Multi-Agent Reinforcement Learning

Learn how to apply reinforcement learning methods to applications that involve multiple, interacting agents. These techniques are used in a variety of applications, such as the coordination of autonomous vehicles.

Project – Collaboration and Competition

Train a pair of agents to play tennis. For an extra challenge, train a team of agents to play soccer!

Need to prepare? We recommend our Deep Learning Nanodegree program.

Apple, Facebook, and Google are investing in deep reinforcement learning.

All our programs include:

❗❗ Important Must Read ❗❗

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