Introduction to Deep Learning
Master deep learning fundamentals from neural networks to GANs. Build practical skills with hands-on projects in computer vision and NLP.
Master deep learning fundamentals from neural networks to GANs. Build practical skills with hands-on projects in computer vision and NLP.
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 Machine Learning: Theory and Hands-on Practice with Python 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.
3.6
(27 ratings)
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What you'll learn
Build and train multilayer perceptron networks
Implement CNN architectures for image classification
Apply RNNs to sequential data analysis
Master optimization methods for neural network training
Develop practical skills through hands-on projects
Create generative models using GANs
Skills you'll gain
This course includes:
6.3 Hours PreRecorded video
4 quizzes
Access on Mobile, Tablet, Desktop
FullTime access
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There are 5 modules in this course
This comprehensive course covers the fundamentals of deep learning, from basic neural networks to advanced architectures. Students learn to implement multilayer perceptrons, convolutional neural networks (CNNs), recurrent neural networks (RNNs), autoencoders, and generative adversarial networks (GANs). The curriculum includes practical projects in cancer detection using CNNs, natural language processing with disaster tweets, and image generation with GANs. The course emphasizes hands-on experience with Python and modern deep learning frameworks.
Deep Learning Introduction, Multilayer Perceptron
Module 1 · 9 Hours to complete
Training Neural Networks
Module 2 · 8 Hours to complete
Deep Learning on Images
Module 3 · 15 Hours to complete
Deep Learning on Sequential Data
Module 4 · 13 Hours to complete
Unsupervised Approaches in Deep Learning
Module 5 · 13 Hours to complete
Fee Structure
Instructor
Adjunct Professor
Dr. Geena Kim is an Adjunct Professor in the Computer Science Department at the University of Colorado Boulder, where she specializes in deep learning and machine learning. She holds a Ph.D. from UC Berkeley and has extensive experience in both academia and industry, currently serving as a Research Scientist at Amazon. Her career also includes entrepreneurial ventures and technical advisory roles for Internet of Things (IoT) startups in the Bay Area, showcasing her versatile expertise in cutting-edge technology.Dr. Kim teaches several courses that focus on machine learning techniques, including "Introduction to Deep Learning," "Introduction to Machine Learning: Supervised Learning," and "Unsupervised Algorithms in Machine Learning." Her research interests encompass deep learning, computer vision, and medical image analysis, contributing to advancements in these fields through innovative applications. With a strong commitment to education and research, Geena Kim continues to influence the next generation of computer scientists and data analysts at CU Boulder.
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3.6 course rating
27 ratings
Frequently asked questions
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