TensorFlow: Powerful Predictive Analytics with TensorFlow

Learn how to solve real life problems using different methods like logic regression, random forests and SVM's with TensorFlow. Key Features Understand predictive analytics along with its challenges and best practices Embedded with assessments that will help you revise the concepts you have learned in this book Book DescriptionPredictive analytics discovers hidden patterns from structured and unstructured data for automated decision making in business intelligence. Predictive decisions are becoming a huge trend worldwide, catering to wide industry sectors by predicting which decisions are more likely to give maximum results. TensorFlow, Google's brainchild, is immensely popular and extensively used for predictive analysis. This book is a quick learning guide on all the three types of machine learning, that is, supervised, unsupervised, and reinforcement learning with TensorFlow. This book will teach you predictive analytics for high-dimensional and sequence data. In particular, you will learn the linear regression model for regression analysis. You will also learn how to use regression for predicting continuous values. You will learn supervised learning algorithms for predictive analytics. You will explore unsupervised learning and clustering using K-meansYou will then learn how to predict neighborhoods using K-means, and then, see another example of clustering audio clips based on their audio features. This book is ideal for developers, data analysts, machine learning practitioners, and deep learning enthusiasts who want to build powerful, robust, and accurate predictive models with the power of TensorFlow. This book is embedded with useful assessments that will help you revise the concepts you have learned in this book. What you will learn Learn TensorFlow features in a real-life problem, followed by detailed TensorFlow installation and configuration Explore computation graphs, data, and programming models also get an insight into an example of implementing linear regression model for predictive analytics Solve the Titanic survival problem using logistic regression, random forests, and SVMs for predictive analytics Dig deeper into predictive analytics and find out how to take advantage of it to cluster records belonging to the certain group or class for a dataset of unsupervised observations Learn several examples of how to apply reinforcement learning algorithms for developing predictive models on real-life datasets Who this book is forThis book is aimed at developers, data analysts, machine learning practitioners, and deep learning enthusiasts who want to build powerful, robust, and accurate predictive models with the power of TensorFlow.

學習如何使用TensorFlow解決現實生活中的問題，包括邏輯回歸、隨機森林和支持向量機等不同方法。主要特點包括了解預測分析及其挑戰和最佳實踐，並附有評估，以幫助您複習本書中學到的概念。

預測分析是在商業智能中從結構化和非結構化數據中發現隱藏模式以進行自動化決策的過程。預測決策正在全球范圍內成為一個巨大的趨勢，為廣泛的行業領域提供預測哪些決策更有可能產生最大結果的能力。TensorFlow是Google的心血結晶，廣受歡迎並被廣泛用於預測分析。

本書是一本關於TensorFlow的三種機器學習類型（監督學習、非監督學習和強化學習）的快速學習指南。本書將教您如何進行高維度和序列數據的預測分析。具體而言，您將學習用於回歸分析的線性回歸模型，以及如何使用回歸來預測連續值。您還將學習用於預測分析的監督學習算法。您將探索非監督學習和使用K-means進行聚類。然後，您將學習如何使用K-means來預測鄰域，並根據音頻特徵對音頻片段進行聚類的另一個示例。

本書適合開發人員、數據分析師、機器學習從業者和深度學習愛好者，他們希望利用TensorFlow的強大、穩健和準確的預測模型構建能力。本書附有有用的評估，可幫助您複習本書中學到的概念。

您將學到什麼：
- 在實際問題中學習TensorFlow的功能，然後進行詳細的TensorFlow安裝和配置
- 探索計算圖、數據和編程模型，並深入了解實現預測分析的線性回歸模型的示例
- 使用邏輯回歸、隨機森林和支持向量機解決Titanic生存問題的預測分析
- 深入研究預測分析，並了解如何利用它對屬於特定組或類的記錄進行聚類
- 學習如何應用強化學習算法在現實數據集上開發預測模型的多個示例

本書適合開發人員、數據分析師、機器學習從業者和深度學習愛好者，他們希望利用TensorFlow的強大、穩健和準確的預測模型構建能力。