Advanced Computer Vision & Deep Learning

Describe advances in CNN architectures. Understand region-based CNN’s, Fast R-CNN, and Faster-R-CNN, which allow for fast, localized object recognition in images.

Understand grid, sliding windows, and bounding boxes in object detection. Implement YOLO, a real-time object detection algorithm.

Understand how recurrent neural networks learn from ordered sequences of data. Identify RNN applications in deep learning. Understand how feedforward and backpropagation through time work as well as RNN unfolded model.

Explore how memory can be incorporated into a deep learning model and implement long short-term memory networks.

Refresh important hyperparameters such as learning rate, epochs, and layer and understand hyperparameters in RNN.

Understand how attention allows models to focus on a specific piece of input data. Describe where attention is useful in natural language and computer vision applications. Describe attention and its encoder and decoder that empower applications like text translation. Understand basic attention methods like additive attention, Bahdanau and Luong attention

Understand image captioning and tokenize captions and words. Combine CNNs and RNNs to build a complex captioning model.

Combine CNN and RNN knowledge to build a deep learning model that produces captions given an input image. Image captioning requires that learners create a complex deep learning model with two components: a CNN that transforms an input image into a set of features, and an RNN that turns those features into rich, descriptive language. In this project, you will implement these cutting-edge deep learning architectures.