Google發布的第二代深度學習係統TensorFlow

VIP內容

在Jupyter Notebook環境中使用Python和TensorFlow 2.0創建、執行、修改和共享機器學習應用程序。這本書打破了編程機器學習應用程序的任何障礙,通過使用Jupyter Notebook而不是文本編輯器或常規IDE。

您將從學習如何使用Jupyter筆記本來改進使用Python編程的方式開始。在獲得一個良好的基礎與Python工作在木星的筆記本,你將深入什麼是TensorFlow,它如何幫助機器學習愛好者,以及如何解決它提出的挑戰。在此過程中,使用Jupyter筆記本創建的示例程序允許您應用本書前麵的概念。

那些剛接觸機器學習的人可以通過這些簡單的程序來學習基本技能。本書末尾的術語表提供了常見的機器學習和Python關鍵字和定義,使學習更加容易。

你將學到什麼

程序在Python和TensorFlow 解決機器學習的基本障礙 在Jupyter Notebook環境中發展

這本書是給誰的

理想的機器學習和深度學習愛好者誰對Python編程感興趣使用Tensorflow 2.0在Jupyter 筆記本應用程序。了解一些機器學習概念和Python編程(使用Python version 3)的基本知識會很有幫助。

http://file.allitebooks.com/20200923/Machine%20Learning%20Concepts%20with%20Python%20and%20the%20Jupyter%20Notebook%20Environment.pdf

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The M5 competition uncertainty track aims for probabilistic forecasting of sales of thousands of Walmart retail goods. We show that the M5 competition data faces strong overdispersion and sporadic demand, especially zero demand. We discuss resulting modeling issues concerning adequate probabilistic forecasting of such count data processes. Unfortunately, the majority of popular prediction methods used in the M5 competition (e.g. lightgbm and xgboost GBMs) fails to address the data characteristics due to the considered objective functions. The distributional forecasting provides a suitable modeling approach for to the overcome those problems. The GAMLSS framework allows flexible probabilistic forecasting using low dimensional distributions. We illustrate, how the GAMLSS approach can be applied for the M5 competition data by modeling the location and scale parameter of various distributions, e.g. the negative binomial distribution. Finally, we discuss software packages for distributional modeling and their drawback, like the R package gamlss with its package extensions, and (deep) distributional forecasting libraries such as TensorFlow Probability.

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