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.
Overview
Syllabus
- Course Introduction
- This course explores advanced concepts and methodologies in neural networks, focusing on Recurrent Neural Networks (RNNs) and Autoencoders. You will analyze the core elements of these architectures, evaluate their applications across various domains, and propose innovative research directions. The curriculum also covers Generative Neural Networks, including their mathematical foundations and deployment constraints. Additionally, learners will gain hands-on experience in Reinforcement Learning, utilizing Markov Chains and Deep Neural Networks to solve complex problems. By the end of the course, you will be equipped with the skills to drive advancements in the field of neural networks.
- Recurrent Neural Networks
- This module will discuss Recurrent Neural Networks. Students will explore the reasons for RNNS along with different techniques.
- Autoencoders
- This module will discuss Auto Encoders. Learners will explore the reasons for autoencoders along with different techniques and applications.
- Generative Deep Neural Networks
- This module will discuss Generative Deep Learning Models. You will study two particular models and go through examples of where they have been successfully deployed.
- Deep Reinforcement Learning
- This module will introduce reinforcement learning. We will discuss Markov Chains, Q-learning, and Deep Q-learning.
Taught by
Zerotti Woods
