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.
Advanced Neural Network Techniques
Advanced Neural Network Techniques
This course is part of Foundations of Neural Networks Specialization
Instructor: Zerotti Woods
Included with 
Gain insight into a topic and learn the fundamentals.
Intermediate level
Recommended experience
10 hours to complete
3 weeks at 3 hours a week
Flexible schedule
Learn at your own pace
What you'll learn
Analyze and implement Recurrent Neural Networks (RNNs) to process sequence data and solve tasks like time series prediction and language modeling.
Explore autoencoders for data compression, feature extraction, and anomaly detection, along with their applications in diverse fields.
Develop and evaluate generative models, such as GANs, understanding their mathematical foundations and deployment challenges.
Apply reinforcement learning techniques using Markov Chains and deep neural networks to tackle complex decision-making problems.
Skills you'll gain
Details to know
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Recently updated!
December 2024
Assessments
8 assignments
Taught in English
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This course is part of the Foundations of Neural Networks Specialization
When you enroll in this course, you'll also be enrolled in this Specialization.
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There are 5 modules in this course
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.
What's included
2 readings
This module will discuss Recurrent Neural Networks. Students will explore the reasons for RNNS along with different techniques.
What's included
1 video1 reading2 assignments1 ungraded lab
This module will discuss Auto Encoders. Learners will explore the reasons for autoencoders along with different techniques and applications.
What's included
1 video1 reading2 assignments
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.
What's included
1 video1 reading2 assignments
This module will introduce reinforcement learning. We will discuss Markov Chains, Q-learning, and Deep Q-learning.
What's included
4 videos1 reading2 assignments
Instructor
Offered by
Recommended if you're interested in Algorithms
DeepLearning.AI
Johns Hopkins University
University of Colorado Boulder
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