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Fall 2019 Semester
Sep 19, 2019
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Information Select the desired Level or Schedule Type to find available classes for the course.

DS 4440 - Practical Neural Networks
Offers a hands-on introduction to modern neural network ("deep learning") tools and methods. Covers the fundamentals of neural networks and introduces standard and new architectures from simple feed forward networks to recurrent neural networks. Also covers stochastic gradient descent and backpropagation, along with related fitting techniques. Emphasizes using these technologies in practice, via modern toolkits. Specifically introduces Keras (together with TensorFlow) and PyTorch, which are illustrative of static and dynamic network implementations, respectively. Reviews applications of these models to various types of data, including images and text.
4.000 Credit hours
4.000 Lecture hours

Levels: Undergraduate
Schedule Types: Lecture

Data Science Department

Course Attributes:
NUpath Analyzing/Using Data, Computer&Info Sci

Restrictions:
Must be enrolled in one of the following Levels:     
      Undergraduate

Prerequisites:
Undergraduate level DS 4400 Minimum Grade of D-

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