Machine Learning Experimentation with TensorBoard

By Imran us Salam Mian
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About this book

TensorBoard, TensorFlow's in-built visualizer, is a promising tool that enables you to track metrics such as loss and accuracy, model graph visualization, project embeddings at lower-dimensional spaces, and more.

Machine Learning Experimentation with TensorBoard begins with a detailed description of the importance and key aspects of TensorBoard. Moving ahead, you will learn how to use checkpoints information to deal with TensorBoard, understand how the ‘What if tool’ and profiling work within TensorBoard, graphically represent high-dimensional embeddings, add TensorBoard logs data into pandas DataFrames, and more. Furthermore, you will get well versed with important tasks such as checking the fairness of models and customizing your board for your own needs. As you progress, you will learn how to upload and share your experiments to your desired audience with the help of TensorBoard's collaborative ML feature, TensorBoard.dev. Finally, you will understand how to easily import and export your board to different environments and how to deploy your board locally, on a virtual machine as well as on AI Platform.

By the end of this machine learning book, you will be able to successfully use, train and deploy a TensorBoard in your machine learning workflow.

Publication date:
October 2022
Publisher
Packt
Pages
143
ISBN
9781801073967

About the Author

  • Imran us Salam Mian

    Imran us Salam Mian is a Sr. Machine Learning Engineer at Red Buffer and a GDE (ML) at Google. He has also been an AI Scholar at PI School, Rome. He has worked extensively on TensorFlow and his main focus lies in Generative Modeling and Latent Variable methods.

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