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The Machine Learning Solutions Architect Handbook

You're reading from  The Machine Learning Solutions Architect Handbook

Product type Book
Published in Jan 2022
Publisher Packt
ISBN-13 9781801072168
Pages 442 pages
Edition 1st Edition
Languages
Author (1):
David Ping David Ping
Profile icon David Ping

Table of Contents (17) Chapters

Preface 1. Section 1: Solving Business Challenges with Machine Learning Solution Architecture
2. Chapter 1: Machine Learning and Machine Learning Solutions Architecture 3. Chapter 2: Business Use Cases for Machine Learning 4. Section 2: The Science, Tools, and Infrastructure Platform for Machine Learning
5. Chapter 3: Machine Learning Algorithms 6. Chapter 4: Data Management for Machine Learning 7. Chapter 5: Open Source Machine Learning Libraries 8. Chapter 6: Kubernetes Container Orchestration Infrastructure Management 9. Section 3: Technical Architecture Design and Regulatory Considerations for Enterprise ML Platforms
10. Chapter 7: Open Source Machine Learning Platforms 11. Chapter 8: Building a Data Science Environment Using AWS ML Services 12. Chapter 9: Building an Enterprise ML Architecture with AWS ML Services 13. Chapter 10: Advanced ML Engineering 14. Chapter 11: ML Governance, Bias, Explainability, and Privacy 15. Chapter 12: Building ML Solutions with AWS AI Services 16. Other Books You May Enjoy

Understanding the TensorFlow deep learning library

Initially released in 2015, TensorFlow is a popular open source machine learning library, primarily backed up by Google, that is mainly designed for deep learning. TensorFlow has been used by companies of all sizes for training and building state-of-the-art deep learning models for a range of use cases, including computer vision, speech recognition, question-answering, text summarization, forecasting, and robotics.

TensorFlow is based on the concept of a computational graph (that is, a dataflow graph), in which the data flow and operations that are performed on the data are constructed as a graph. TensorFlow takes input data in the form of an n-dimensional array/matrix, which is known as a tensor, and performs mathematical operations on this tensor, such as add or matrix multiplication. An example of a tensor could be a scalar value (for example, 1.0), a one-dimensional vector (for example, [1.0, 2.0, 3.0]), a two-dimensional...

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