Machine Learning Experimentation with TensorBoard: Visualize, debug, and optimize your models using the powerful capabilities of TensorBoard

Track experiment metrics and visualize model graphs with TensorFlow's built-in solution, TensorBoard Key Features * Understand the capabilities of TensorBoard and write unique callbacks for each service * Get up to speed with Collaborative machine learning (ML) with TensorBoard.dev * Explore best practices for deploying your TensorBoard Book Description 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 җhat 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. What you will learn * Integrate TensorBoard with TensorFlow and Keras * Understand how to export/import a TensorBoard * Use scalars and custom metrics inside TensorBoard * Add images and model graphs to be visualized in TensorBoard * Train a simple model and create TensorBoard logs * Upload and delete your TensorBoard.dev experiment * Perform hyperparameter tuning on AI Platform Who This Book Is For Machine learning developers working with TensorFlow and Keras who are looking to visualize some of their results efficiently and in real-time will find this machine learning TensorFlow book helpful. Beginner-level knowledge of Python coding is expected. Readers are also expected to know how to develop a model or a simple task in TensorFlow or Keras.

About the Author :
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.