If you're diving deeper into the world of Convolutional Neural Networks (CNNs), here are some advanced topics you might want to explore:

  • Advanced Architectures: Learn about architectures like ResNet, DenseNet, and Xception, which push the boundaries of CNN performance.
  • Faster R-CNN: Understand object detection using region proposals and CNNs.
  • Generative Adversarial Networks (GANs): Combine CNNs with GANs to generate novel images.
  • Transfer Learning: Utilize pre-trained models to save time and improve performance on your specific task.

For more information on advanced CNN topics, check out our Deep Learning Tutorial.

  • ResNet: This architecture uses residual learning to allow training of very deep networks.

    • ResNet

  • DenseNet: It connects each layer to every other layer in a feed-forward manner, which allows the representation at each layer to be rich by leveraging the learned feature hierarchy of all preceding layers.

    • DenseNet

  • Faster R-CNN: This network is designed for real-time object detection and segmentation.

    • Faster R-CNN

  • GANs: GANs consist of two networks, a generator and a discriminator, which compete with each other.

    • GANs

To delve further into these advanced topics, consider joining our CNN Masterclass.