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CNN in deep learning

Track :

Computer Science

Course Presenter :

Learn With Jay

Lessons no : 12

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What will you learn in this course?
  • Design and implement convolutional neural networks for image recognition and classification tasks using Python and TensorFlow
  • Apply pooling, activation functions, and fully connected layers to optimize CNN performance in visual data analysis
  • Develop CNN architectures like VGG, ResNet, and Inception for advanced computer vision applications
  • Utilize data augmentation techniques to improve CNN model accuracy and robustness in real-world scenarios
  • Evaluate CNN models using performance metrics such as accuracy, precision, and recall for image processing tasks
  • Build face recognition and medical image analysis systems leveraging CNN deep learning techniques
  • Troubleshoot and fine-tune CNN models to enhance efficiency and accuracy in visual data tasks
  • Deploy trained CNN models for practical applications in object detection, medical imaging, and image classification

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Lessons | 12


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Related Courses

CNN in deep learning, in this course we will learn about CNN in deep learning course. Convolutional Neural Networks (CNNs) are a powerful type of neural network used primarily for image recognition, object detection, and visual data analysis. This course introduces you to the foundational concepts of CNNs including convolutional layers, pooling, activation functions, and fully connected layers. You’ll explore how CNNs work with image data and how to implement them using Python and popular libraries like TensorFlow and Keras. We’ll cover practical applications such as image classification, face recognition, and medical image processing. Additionally, the course dives into advanced architectures like VGG, ResNet, and Inception to demonstrate real-world use cases. Through hands-on projects, you will build and train CNN models, evaluate performance, and apply data augmentation techniques. By the end of this course, you will have the skills to design and deploy CNNs for a variety of computer vision tasks with confidence. Learn With Jay