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Convolutional Neural Networks

Convolutional Neural Networks

$9.99

$109.99

"Convolutional Neural Networks" is an advanced course focused on mastering CNNs, a pivotal technology in deep learning for image analysis and beyond. You'll explore the architecture and components of CNNs, including convolutional and pooling layers, and learn how to apply these techniques to tasks such as image classification, object detection, and image generation. Through practical exercises and real-world examples, this course will equip you with the skills to design, implement, and optimize CNN models effectively, making it ideal for those seeking to deepen their expertise in cutting-edge machine learning applications.

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Has discount
Expiry period Lifetime
Made in English
Last updated at Tue Jul 2024
Level
Beginner
Total lectures 42
Total quizzes 0
Total duration 06:01:10 Hours
Total enrolment 36
Number of reviews 7
Avg rating
Short description "Convolutional Neural Networks" is an advanced course focused on mastering CNNs, a pivotal technology in deep learning for image analysis and beyond. You'll explore the architecture and components of CNNs, including convolutional and pooling layers, and learn how to apply these techniques to tasks such as image classification, object detection, and image generation. Through practical exercises and real-world examples, this course will equip you with the skills to design, implement, and optimize CNN models effectively, making it ideal for those seeking to deepen their expertise in cutting-edge machine learning applications.
Outcomes
  • Design and Build CNN Models: Create effective convolutional neural network architectures for a variety of image-based tasks.
  • Apply CNN Techniques to Real-world Problems: Implement CNNs for practical applications such as image classification, object detection, and segmentation.
  • Utilize Advanced CNN Methods: Leverage advanced techniques including transfer learning and fine-tuning to improve model performance.
  • Optimize CNN Performance: Optimize your models through hyperparameter tuning and performance evaluation to achieve better accuracy and efficiency.
  • Understand CNN Challenges: Address and overcome common challenges in working with CNNs, including overfitting, computational complexity, and data quality issues.
Requirements

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