Exploring the TensorFlow Ecosystem: A Comprehensive Overview

Author(s): Youssef Hosni

Originally published on Towards AI.

An Overview of Tensorflow EcoSystem

TensorFlow is a pivotal deep-learning framework developed by Google. Most people know TensorFlow for training deep learning models, however, the TensorFlow ecosystem has more components.

This emphasized the aim of providing a guided tour through its various components and applications. The blog promises to cover TensorFlow’s core library, TensorFlow Addons, TensorFlow Lite for mobile and embedded devices, and TensorFlow Extended (TFX) for end-to-end machine learning pipelines.

Practical code examples and real-world use cases will be included, making this blog a valuable resource for both beginners and experienced machine learning practitioners. The introduction invites readers to embark on an illuminating journey through the TensorFlow Ecosystem to unlock its remarkable capabilities and endless possibilities.

Mentoring sessions: https://lnkd.in/dXeg3KPWLong-term mentoring: https://lnkd.in/dtdUYBrMData Science, Machine Learning, AI, and what is beyond them. Click to read To Data & Beyond, by Youssef Hosni, a…

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While the progress bar we used in the first example of this chapter displayed useful information, we might want to access more detailed graphs. TensorFlow provides a powerful tool for monitoring — TensorBoard. Installed by default with TensorFlow, it is also very easy to use when combined with Keras’s callbacks:

callbacks = [tf.keras.callbacks.TensorBoard('./logs_keras')]model.fit(x_train, y_train, epochs=5, verbose=1, validation_data=(x_test,y_test), callbacks=callbacks)

In this updated code, we pass the TensorBoard callback to the… Read the full blog for free on Medium.

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Published via Towards AI