DeepMind Unveils Advanced Courses: A Deep Dive into Depth Learning and Reinforcement Learning
In a recent tweet, DeepMind excitedly announced the release of its advanced courses, now available in video format. The courses, titled “Advanced Deep Learning and Reinforcement Learning,” were previously taught at University College London (UCL) and have been eagerly anticipated by the AI community.
A Semester of Exploration: 18 Classes, Two Parts
The course is divided into two parts, which intersect and converge at the end of the semester. The first part focuses on depth learning, while the second part delves into reinforcement learning. This structure allows students to gain a comprehensive understanding of both concepts and their applications.
Part 1: Depth Learning
This section begins with a brief introduction to neural networks and supervised learning, utilizing TensorFlow. The course then explores various types of neural networks, including convolutional neural networks (CNNs), recurrent neural networks (RNNs), and end-to-end learning. Additionally, students will learn about optimization methods, unsupervised learning, attention, and memory.
The application-oriented classes will cover object recognition and natural language processing, providing students with hands-on experience in these areas.
Part 2: Reinforcement Learning
This section involves Markov decision processes, dynamic programming, and model-free prediction and control. Students will also learn about value functions, approximation, and policy gradient methods, as well as the integration of learning and planning.
The course will also cover the application of reinforcement learning, including learning to play classic games and board games. This will provide students with a deeper understanding of the complexities and challenges of reinforcement learning.
Tips for Success
The two parts of the course are designed to be interspersed, allowing students to construct their own mental framework in advance. By following this structure, students can gain a comprehensive understanding of both depth learning and reinforcement learning.
Course Video Portal
The course video portal is available on YouTube, and can be accessed through the following link: https://www.youtube.com/playlist?list=PLqYmG7hTraZDNJre23vqCGIVpfZ_K2RZs
Join the Community
The Qubit AI community is recruiting students interested in AI, and welcomes those who wish to join the community. To join, simply reply with the keyword “communication group” in the public qubit number (QbitAI) dialog interface.
Recruitment Opportunities
Qubits is also recruiting editors/reporters based in Beijing’s Zhongguancun. Talented and enthusiastic students are encouraged to join the team. Details can be found by replying with the keyword “recruitment” in the qubit public number (QbitAI) dialog interface.
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