Book Image

TensorFlow: Powerful Predictive Analytics with TensorFlow

By : Md. Rezaul Karim
Book Image

TensorFlow: Powerful Predictive Analytics with TensorFlow

By: Md. Rezaul Karim

Overview of this book

Predictive 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. This book is repurposed for this specific learning experience from material from Packt's Predictive Analytics with TensorFlow by Md. Rezaul Karim.
Table of Contents (8 chapters)
TensorFlow: Powerful Predictive Analytics with TensorFlow

Notation, Policy, and Utility in RL

You may notice that reinforcement learning jargon involves anthropomorphizing the algorithm into taking actions in situations to receive rewards. In fact, the algorithm is often referred to as an agent that acts with the environment. You can just think of it like an intelligent hardware agent sensing with sensors and interacting with the environment using its actuators.

Therefore, it shouldn't be a surprise that much of RL theory is applied in robotics. Figure 2 demonstrates the interplay between states, actions, and rewards. If you start at state s1, you can perform action a1 to obtain a reward r (s1, a1). Actions are represented by arrows, and states are represented by circles:

Figure 2: An agent is performing an action on a state produces a reward

A robot performs actions to change between different states. But how does it decide which action to take? Well, it's all about using different or a concrete policy.


In reinforcement learning lingo, we call...