Probabilistic Deep Learning shows how probabilistic deep learning models gives readers the tools to identify and account for uncertainty and potential errors in their results. Starting by applying the underlying maximum likelihood principle of curve fitting to deep learning, readers will move on to using the Python-based Tensorflow Probability framework, and set up Bayesian neural networks that can state their uncertainties. Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications.
人気のある作家
J KING (12) JJ TAM (12) yang hu (11) Al Sweigart (8) Mojang AB (8) desti publishhings (7) Hidenori Kusaka (6) John Bach (6) JP TAM (6) Andrea Vedaldi (5) Halonjash Publications (5) Hiro Ainana (5) Horst Bischof (5) Intelligent Feather Publications (5) Jan-Michael Frahm (5) Michael W. Lucas (5) Andrew Park (4) Benjamin Smith (4) Engr. Michael David (4) Harvey Deitel (4)最適なファイルサイズ
10531 KB 1079 KB 1116 KB 1233 KB 2661 KB 370 KB 484 KB 536 KB 649 KB 738 KB 790 KB 10049 KB 1006 KB 10137 KB 1016 KB 102097 KB 1029 KB 10325 KB 1032 KB 1035 KB