Advanced AI: Deep Reinforcement Learning in Python

The Complete Guide to Mastering Artificial Intelligence using Deep Learning and Neural Networks

4.65 (6208 reviews)

Udemy

platform

English

language

Data Science

What you will learn

Build various deep learning agents (including DQN and A3C)

Apply a variety of advanced reinforcement learning algorithms to any problem

Q-Learning with Deep Neural Networks

Policy Gradient Methods with Neural Networks

Reinforcement Learning with RBF Networks

Use Convolutional Neural Networks with Deep Q-Learning

Understand important foundations for OpenAI ChatGPT, GPT-4

Course Gallery

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Advanced AI: Deep Reinforcement Learning in Python – Screenshot 2 — Screenshot 2 – Advanced AI: Deep Reinforcement Learning in Python

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Advanced AI: Deep Reinforcement Learning in Python – Screenshot 4 — Screenshot 4 – Advanced AI: Deep Reinforcement Learning in Python

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Comidoc Review

Our Verdict

This comprehensive course on advanced AI and deep reinforcement learning in Python offers extensive coverage of a variety of algorithms, concepts, and practical applications. Though the steep complexity curve and occasionally arrogant tone may pose challenges for some learners, the availability of code exercises, real-world examples, and clear explanations overall contribute to an enriching educational experience. While a few outdated libraries are used in the exercises, it's possible to adapt them to modern alternatives, ensuring that students stay up-to-date with current technologies.

What We Liked

Covers a wide range of advanced deep reinforcement learning algorithms and their applications
Code exercises provided for each section to help understand the techniques better
Thorough explanations that combine technical details with practical examples and metaphors
Well-structured curriculum, building upon prior lectures effectively

Potential Drawbacks

Arrogant tone of the instructor may be off-putting for some learners
Steep increase in complexity between lessons can make it difficult to follow without additional study
Lack of visual schemes and excessive mathematical details might hinder understanding for some students
Some exercises still use outdated libraries, such as Tensorflow 1 and Theano, which may require modifications