Probabilistic Deep Learning with Python (Oliver Duerr, Beate Sick, Elvis Murina) (Z-Library)

Author： Oliver Duerr, Beate Sick, Elvis Murina

Probabilistic Deep Learning: With Python, Keras and TensorFlow Probability teaches the increasingly popular probabilistic approach to deep learning that allows you to refine your results more quickly and accurately without much trial-and-error testing. Emphasizing practical techniques that use the Python-based Tensorflow Probability Framework, you’ll learn to build highly-performant deep learning applications that can reliably handle the noise and uncertainty of real-world data. About the technology The world is a noisy and uncertain place. Probabilistic deep learning models capture that noise and uncertainty, pulling it into real-world scenarios. Crucial for self-driving cars and scientific testing, these techniques help deep learning engineers assess the accuracy of their results, spot errors, and improve their understanding of how algorithms work.

AI Reading Assistant

Whole-book reading guide from stratified index samples; jump to passages in the text

Generate book guide

Help cover token costs

Tip the Site

Support this site Your recognition and a small knowledge-service contribution help keep this technical work open source. Scan the WeChat Pay or Alipay code below. Logged-in and guest visitors can both tip.

Open WeChat or Alipay and scan. No login required.

Regenerate

Passage locations

Ask

Collapse guide