Convolutional Neural Networks
Master deep learning and computer vision with CNNs. Learn to build neural networks for image recognition, object detection, and face recognition.
Master deep learning and computer vision with CNNs. Learn to build neural networks for image recognition, object detection, and face recognition.
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 Deep 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.
4.9
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English
پښتو, বাংলা, اردو, 4 more
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What you'll learn
Build and train convolutional neural networks for image recognition tasks
Implement modern CNN architectures including ResNets and Inception networks
Apply transfer learning techniques to leverage pre-trained models
Develop object detection systems using YOLO algorithm
Create face recognition systems using Siamese networks
Generate artistic images using neural style transfer
Skills you'll gain
This course includes:
7.5 Hours PreRecorded video
4 assignments
Access on Mobile, Tablet, Desktop
FullTime access
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There are 4 modules in this course
This comprehensive course explores the foundations and applications of Convolutional Neural Networks (CNNs) in computer vision. Students learn to implement CNNs from scratch, understand advanced architectures like ResNet and Inception, and apply these networks to real-world tasks including object detection, face recognition, and neural style transfer. The curriculum combines theoretical understanding with practical implementation through hands-on programming assignments, preparing learners for cutting-edge AI development.
Foundations of Convolutional Neural Networks
Module 1 · 9 Hours to complete
Deep Convolutional Models: Case Studies
Module 2 · 9 Hours to complete
Object Detection
Module 3 · 8 Hours to complete
Special Applications: Face recognition & Neural Style Transfer
Module 4 · 8 Hours to complete
Fee Structure
Instructors
Pioneer in AI and Online Education
Andrew Ng is the Founder of DeepLearning.AI, a General Partner at AI Fund, and the Chairman and Co-Founder of Coursera, where he also serves as an Adjunct Professor at Stanford University. Renowned for his groundbreaking contributions to machine learning and online education, Dr. Ng has transformed countless lives through his work in AI, having authored or co-authored over 100 research papers in machine learning, robotics, and related fields. His notable past roles include serving as chief scientist at Baidu and leading the founding team of Google Brain. Currently, Dr. Ng focuses on his entrepreneurial ventures, seeking innovative ways to promote responsible AI practices across the global economy.
Stanford AI Educator Pioneers Global Learning Through Course Innovation and EdTech Leadership
Younes Bensouda Mourri is a distinguished AI educator and entrepreneur who has significantly impacted global tech education. Born and raised in Morocco, he earned his B.S. in Applied Mathematics and Computer Science and M.S. in Statistics from Stanford University, where he now teaches Artificial Intelligence both on campus and online. As the founder of LiveTech.AI, he develops AI tools to transform academic institutions, while his courses have reached over 1.3 million learners worldwide, with 23% securing AI-related jobs after completion. His contributions include co-creating Stanford's Applied Machine Learning, Deep Learning, and Teaching AI courses, as well as developing the highly successful Natural Language Processing Specialization for DeepLearning.AI. Starting as a teaching assistant in Andrew Ng's Machine Learning course, he rose to become an Adjunct Lecturer at Stanford by age 22, demonstrating his commitment to democratizing AI education. Through his work with major companies like ASML, CISCO, and Boston Consulting Group, he continues to advance AI education while focusing on developing innovative NLP tools for personalized feedback and chain-of-thought reasoning
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4.9 course rating
42,281 ratings
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