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

Predictive Models for Clustering Audio Files

For clustering music with audio data, the data points are the feature vectors from the audio files. If two points are close together, it means that their audio features are similar. We want to discover which audio files belong to the same neighborhood because these clusters will probably be a good way to organize your music files:

  1. Loading audio files with TensorFlow and Python.

    Some common input types in ML algorithms are audio and image files. This shouldn't come as a surprise because sound recordings and photographs are raw, redundant, ab nd often noisy representations of semantic concepts. ML is a tool to help handle these complications. These data files have various implementations, for example, an audio file can be an MP3 or WAV.

    Reading files from a disk isn't exactly a ML-specific ability. You can use a variety of Python libraries to load files onto the memory, such as Numpy or Scipy. Some developers like to treat the data preprocessing step...