Gaussian Mixture Models: Mathematical insights, applications, and PyTorch Implementation hints

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"Math and Architectures of Deep Learning" sets out the foundations of DL in a way that’s both useful and accessible to working practitioners. Each chapter explores a new fundamental DL concept or architectural pattern, explaining the underpinning mathematics and demonstrating how they work in practice with well-annotated Python code. You’ll start with a primer of basic algebra, calculus, and statistics, working your way up to state-of-the-art DL paradigms taken from the latest research.
  1. Manning Publications
  2. what are gaussian mixture models
  3. machine learning
  4. statistics
  5. math
  6. deep learning
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