TensorFlow Flashcards

Posted on by uplatzblog

This page gives you a quick, practical overview of TensorFlow—a popular
deep-learning library for building and deploying machine-learning models.
You’ll see where it fits in production and how to get from data to inference quickly.

In short, the platform offers flexible layers, efficient input pipelines, and multiple deployment options
(servers, browsers, and mobile). Moreover, the high-level Keras API streamlines model building, while lower-level
ops give you fine-grained control. Because of that design, beginners ship quickly and experts still optimise.

TensorFlow flashcards overview: data pipeline, Keras layers, training loop, and serving
Typical workflow: tf.data pipelines feed Keras models; training exports artifacts for serving.

If you plan to ship an ML service, start simple: define a model, train on a small sample, and validate early.
For an end-to-end walkthrough, see our guide on
training and serving a Keras model.
In addition, the official documentation covers performance, distribution strategies, and tooling.

TensorFlow Serving and TensorFlow Lite: deploy trained models to servers and mobile
Serve on servers with TF Serving, on devices with TF Lite, and in browsers using TF.js.

Scroll to the flashcards for a compact refresher. Then, use the links at the end to explore examples and templates.

🧠 TensorFlow Flashcards
💡 What is TensorFlow?
An open-source end-to-end ML platform from Google for building and deploying models.
🧱 What is a Tensor?
A multi-dimensional array used as the core data structure throughout the library.
📦 What is tf.data?
A high-performance API for loading, transforming, and batching inputs efficiently.
🎯 What is Keras?
A high-level API that simplifies neural-network definition, training, and evaluation.
⚙️ What are layers?
Composable building blocks that transform inputs—Dense, Conv2D, LSTM, and more.
🧪 What does model.compile() do?
It sets the loss, optimizer, and metrics before training starts.
📊 What about model.fit()?
It trains for a chosen number of epochs using your training data (and validation if provided).
🔁 What is an epoch?
One complete pass over the training set during learning.
🌐 Is it production-ready?
Yes—use TF Serving for servers, TF Lite for mobile/edge, and TF.js for browsers.
🧩 Is it open-source?
Yes, under the Apache 2.0 license with a large contributor community.

To begin, prototype with a small dataset, validate metrics, and profile performance.
Next, add callbacks (early stopping, checkpoints), then export a SavedModel for deployment.
Finally, choose the right runtime—Serving, Lite, or JS—based on your target platform.

Explore our hands-on tutorial:
Training and Serving a Keras Image Classifier.
For official guides and APIs, visit the
TensorFlow documentation.