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Hands-On Machine Learning with IBM Watson

Hands-On Machine Learning with IBM Watson

By : James D. Miller
1 (1)
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Hands-On Machine Learning with IBM Watson

Hands-On Machine Learning with IBM Watson

1 (1)
By: James D. Miller

Overview of this book

IBM Cloud is a collection of cloud computing services for data analytics using machine learning and artificial intelligence (AI). This book is a complete guide to help you become well versed with machine learning on the IBM Cloud using Python. Hands-On Machine Learning with IBM Watson starts with supervised and unsupervised machine learning concepts, in addition to providing you with an overview of IBM Cloud and Watson Machine Learning. You'll gain insights into running various techniques, such as K-means clustering, K-nearest neighbor (KNN), and time series prediction in IBM Cloud with real-world examples. The book will then help you delve into creating a Spark pipeline in Watson Studio. You will also be guided through deep learning and neural network principles on the IBM Cloud using TensorFlow. With the help of NLP techniques, you can then brush up on building a chatbot. In later chapters, you will cover three powerful case studies, including the facial expression classification platform, the automated classification of lithofacies, and the multi-biometric identity authentication platform, helping you to become well versed with these methodologies. By the end of this book, you will be ready to build efficient machine learning solutions on the IBM Cloud and draw insights from the data at hand using real-world examples.
Table of Contents (15 chapters)
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Section 1: Introduction and Foundation
6
Section 2: Tools and Ingredients for Machine Learning in IBM Cloud
10
Section 3: Real-Life Complete Case Studies

Multimodal fusion

Most simply put, the more gates surrounding an individual's property, the more secure it will be, since we would need to possess the ability to successfully pass more than a single test to proceed. The same applies to any system or solution that makes a decision to proceed based upon the outcome of a test (match or no-match). Adding additional tests produces additional outcomes and additional decisions based upon those outcomes.

Biometric authentication solutions are typically considered unimodal (conducts only a single test) or multimodal (conducts more than a single test):

  • Multimodal biometric authentication: This describes an implemented solution that utilizes multiple biometric indicators for identifying the authenticated individuals, such as a photo/image and a signature. A successful match of both biometrics is required to succeed.
  • Unimodal biometric...

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