Mastering Neural Networks and Model Regularization
Learn advanced neural network techniques, from building networks from scratch to implementing CNNs with PyTorch for complex deep learning tasks.
Learn advanced neural network techniques, from building networks from scratch to implementing CNNs with PyTorch for complex deep learning tasks.
This course cannot be purchased separately - to access the complete learning experience, graded assignments, and earn certificates, you'll need to enroll in the full Applied Machine Learning Specialization program. You can audit this specific course for free to explore the content, which includes access to course materials and lectures. This allows you to learn at your own pace without any financial commitment.
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What you'll learn
Build and optimize neural networks from scratch
Implement back-propagation and computational graphs
Master regularization techniques for model optimization
Develop CNNs using PyTorch for complex tasks
Apply deep learning to image and audio processing
Skills you'll gain
This course includes:
8 Hours PreRecorded video
12 assignments
Access on Mobile, Tablet, Desktop
FullTime access
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There are 5 modules in this course
This comprehensive course covers advanced neural network concepts and implementation, focusing on building networks from scratch and applying them to real-world problems. Students learn essential techniques in model regularization, including L1/L2 regularization and dropout. The curriculum includes practical implementation using PyTorch, convolutional neural networks for image and audio processing, and optimization techniques for enhanced model performance.
Course Introduction
Module 1 · 10 Minutes to complete
Multilayer Artificial Neural Networks
Module 2 · 3 Hours to complete
Model Regularization
Module 3 · 3 Hours to complete
PyTorch
Module 4 · 3 Hours to complete
Convolutional Neural Networks
Module 5 · 5 Hours to complete
Fee Structure
Instructor
Innovator in Cybersecurity and Machine Learning at Johns Hopkins University
Dr. Erhan Guven is a distinguished faculty member at Johns Hopkins University, where he teaches courses in machine learning, generative AI, natural language processing (NLP), graph analytics, and formal methods. He holds a Ph.D. in Computer Science from The George Washington University and has made significant contributions to the fields of cybersecurity and disease forecasting through his extensive research and numerous publications. Dr. Guven is also an inventor of several patents related to speech emotion detection and Voice over Internet Protocol (VoIP) technologies, showcasing his commitment to advancing technology in practical applications. His interdisciplinary approach combines theoretical knowledge with real-world problem-solving, making him a valuable asset to both the academic community and the broader field of computer science. Through his teaching and research, Dr. Guven continues to inspire students and contribute to cutting-edge advancements in technology.
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