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Convolutional Neural Networks
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Convolutional Neural Networks

Master deep learning and computer vision with CNNs. Learn to build neural networks for image recognition, object detection, and face recognition.

Course Cost

Free course

Intermediate

Skill Level

34 Hours

Self-paced lessons

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.

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4.9

5,19,099 Enrolled

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English

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olive-leaves-logo

4.9

5,19,099 Enrolled

olive-leaves-logo

English

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

Deep Learning
CNN
Computer Vision
Image Recognition
Neural Networks
TensorFlow
Object Detection
Face Recognition
Transfer Learning
Image Processing

This course includes:

7.5 Hours PreRecorded video

4 assignments

Access on Mobile, Tablet, Desktop

FullTime access

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Top companies offer this course to their employees

Top companies provide this course to enhance their employees' skills, ensuring they excel in handling complex projects and drive organizational success.

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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

Reviews

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Faculties

These are the expert instructors who will be teaching you throughout the course. With a wealth of knowledge and real-world experience, they're here to guide, inspire, and support you every step of the way. Get to know the people who will help you reach your learning goals and make the most of your journey.

Convolutional Neural Networks

Intermediate

Skill Level

34 Hours

Self-paced lessons

Course Cost

Free course

Completion

CERTIFICATE

Frequently asked Questions

Below are some of the most commonly asked questions about this course. We aim to provide clear and concise answers to help you better understand the course content, structure, and any other relevant information. If you have any additional questions or if your question is not listed here, please don't hesitate to reach out to our support team for further assistance.