Advanced Computer Vision and Deep Learning

Learn to apply deep learning architectures to computer vision tasks. Discover how to combine CNN and RNN networks to build an automatic image captioning application.

• Advanced CNN Architectures
• Learn about advances in CNN architectures and see how region-based CNN’s, like Faster R-CNN, have allowed for fast, localized object recognition in images.
  
• YOLO
• Learn about the YOLO (You Only Look Once) multi-object detection model and work with a YOLO implementation.
• RNN's
• Explore how memory can be incorporated into a deep learning model using recurrent neural networks (RNNs). Learn how RNNs can learn from and generate ordered sequences of data.
• Long Short-Term Memory Networks (LSTMs)
• Luis explains Long Short-Term Memory Networks (LSTM), and similar architectures which have the benefits of preserving long term memory.
• Hyperparameters
• Learn about a number of different hyperparameters that are used in defining and training deep learning models. We'll discuss starting values and intuitions for tuning each hyperparameter.
• Optional: Attention Mechanisms
• Attention is one of the most important recent innovations in deep learning. In this section, you'll learn how attention models work and go over a basic code implementation.
• Image Captioning
• Learn how to combine CNNs and RNNs to build a complex, automatic image captioning model.
• Project: Image Captioning
• Train a CNN-RNN model to predict captions for a given image. Your main task will be to implement an effective RNN decoder for a CNN encoder.