Advanced Neural Network Techniques

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Advanced Neural Network Techniques

This course is part of Foundations of Neural Networks Specialization

Instructor: Zerotti Woods

Included with

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5 modules

Gain insight into a topic and learn the fundamentals.

Intermediate level

Recommended experience

1 week to complete

at 10 hours a week

Flexible schedule

Learn at your own pace

5 modules

Gain insight into a topic and learn the fundamentals.

Intermediate level

Recommended experience

1 week to complete

at 10 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

Tools you'll learn

Details to know

Shareable certificate

Add to your LinkedIn profile

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

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.

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

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Instructor

Zerotti Woods

Johns Hopkins University

3 Courses 3,042 learners

Offered by

Johns Hopkins University

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