Detailed Course Information |
Spring 2024 Semester
May 15, 2024 |
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| DS 4440 - Practical Neural Networks |
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Offers a hands-on introduction to modern neural network ("deep learning") methods and tools. Covers fundamentals of neural networks and introduces standard and new architectures from simple feedforward networks to recurrent and “transformer” architectures. Also covers stochastic gradient descent and backpropagation, along with related parameter estimation techniques. Emphasizes using these technologies in practice, via modern toolkits. 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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