MP4 | Video: AVC 1920×1080 | Audio: AAC 44KHz 2ch | Duration: 47M | 435 MB
Genre: eLearning | Language: English
Understanding how to create a deep learning neural network is an essential component of any data scientist’s knowledge base. This video continues the explanation of how to build neural networks using Python and MXNet (a flexible and efficient deep learning library) described in “Introduction to Deep Learning with MXNet.” This course covers some of the challenges that arise when training neural networks. It focuses on the problem of overfitting and its potential remedy: regularization. Learners should have a basic understanding of Python, linear algebra, and calculus.
Discover what overfitting means and how to recognize it in deep learning models
Understand how to sample your data to reduce the likelihood of overfitting
Learn about regularization and its use as a remedy for overfitting
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