Convolutional Neural Networks for Visual Recognition

CS231N

Stanford School of Engineering


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Description

Computer Vision is a dynamic and rapidly growing field with countless high-profile applications that have been developed in recent years. The potential uses are diverse, and its integration with cutting edge research has already been validated with self-driving cars, facial recognition, 3D reconstructions, photo search and augmented reality. Artificial Intelligence has become a fundamental component of everyday technology, and visual recognition is a key aspect of that.  It is a valuable tool for interpreting the wealth of visual data that surrounds us and on a scale impossible with natural vision.

This course covers the tasks and systems at the core of visual recognition with a detailed exploration of deep learning architectures. While there will be a brief introduction to computer vision and frameworks, such as Caffe, Torch, Theano and TensorFlow, the focus will be learning end-to-end models, particularly for image classification. Students will learn to implement, train and debug their own neural networks as well as gain a detailed understanding of cutting-edge research in computer vision.

The final assignment will include training a multi-million parameter convolutional neural network and applying it on the largest image classification dataset (ImageNet).

Instructor(s)

Prerequisites

Proficiency in Python; familiarity with C/C++; CS131 and CS229 or equivalents; Math21 or equivalent, linear algebra.

Topics include

  • End-to-end models
  • Image classification, localization and detection
  • Implementation, training and debugging
  • Learning algorithms, such as backpropagation
  • Long Short Term Memory (LSTM)
  • Recurrent Neural Networks (RNN)
  • Supervised and unsupervised learning

Note on Course Availability

The course schedule is displayed for planning purposes – courses can be modified, changed, or cancelled. Course availability will be considered finalized on the first day of open enrollment. For quarterly enrollment dates, please refer to our graduate certificate homepage.