Computer Vision

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Welcome to the Computer Vision Nanodegree program!

Learn how images are represented numerically and implement image processing techniques, such as color masking and binary classification.

Learn about frequency in images and implement your own image filters for detecting edges and shapes in an image. Use a computer vision library to perform face detection.

Program a corner detector and learn techniques, like k-means clustering, for segmenting an image into unique parts.

Learn how to describe objects and images using feature vectors.

Define and train your own convolution neural network for clothing recognition. Use feature visualization techniques to see what a network has learned.

Apply your knowledge of image processing and deep learning to create a CNN for facial keypoint (eyes, mouth, nose, etc.) detection.

Take advantage of Amazon's GPUs to train your neural network faster. In this lesson, you'll learn how to setup an instance on AWS and train a neural network on a GPU.

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.

Learn about the YOLO (You Only Look Once) multi-object detection model and work with a YOLO implementation.

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.

Luis explains Long Short-Term Memory Networks (LSTM), and similar architectures which have the benefits of preserving long term memory.

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.

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.

Learn how to combine CNNs and RNNs to build a complex, automatic image captioning model.

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.