The course "Advanced Neural Network Techniques" delves into advanced neural network methodologies, offering learners an in-depth understanding of cutting-edge techniques such as Recurrent Neural Networks (RNNs), Autoencoders, Generative Neural Networks, and Deep Reinforcement Learning. Through hands-on projects and practical applications, learners will master the mathematical foundations and deployment strategies behind these models. You will explore how RNNs handle sequence data, uncover the power of Autoencoders for unsupervised learning, and dive into the transformative potential of generative models like GANs. The course also covers reinforcement learning, equipping you with the skills to solve complex decision-making problems using deep neural networks and Markov Chains. Designed to bridge theoretical knowledge and practical implementation, this course stands out by incorporating real-world challenges, ethical considerations, and future research directions.
Introduction and Policy Search
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Skills You'll Learn
Artificial Neural Networks, Deep Learning, Natural Language Processing, Data Ethics, Generative AI, Markov Model, Machine Learning, Artificial Intelligence, Unsupervised Learning, Reinforcement Learning
From the lesson
Deep Reinforcement Learning
This module will introduce reinforcement learning. We will discuss Markov Chains, Q-learning, and Deep Q-learning.
Taught By
Zerotti Woods