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Scan for outdated or missing drivers - takes under a minuteDriver Scan →Clear out junk files and repair common Windows errorsFree Scan →You can train your first TensorFlow model without installing anything locally: open TensorFlow’s beginner quickstart in Google Colab, connect to a runtime, and follow its notebook. It walks through loading the MNIST image dataset, preparing the pixels, building a neural network with Keras, training it, and checking its accuracy on test data. It is one complete introductory workflow—not a full machine-learning course or a guide to production deployment.
Choose how to run the TensorFlow tutorial
The official TensorFlow 2 quickstart for beginners is a notebook. TensorFlow says its tutorials can run in Google Colab without setup, making Colab the most direct way to try this example. Open the quickstart, choose the option to run it in Colab, and connect to a runtime when prompted.
| Option | Setup | What to consider |
|---|---|---|
| Google Colab | No local TensorFlow installation is needed for this tutorial. | Runs in a browser-hosted notebook. Runtime availability and performance are not guaranteed. |
| Local environment | Install TensorFlow and its prerequisites on your computer. | Check the current TensorFlow installation guide for supported operating systems, Python versions, and CPU or GPU setup. These details can change. |
You do not need to buy a GPU just to follow this quickstart: its documented Colab route avoids local setup, and the tutorial does not specify a hardware requirement. That does not establish that every TensorFlow project can run on any computer or without a GPU.
What the beginner quickstart teaches
The notebook builds a neural network that classifies images. It uses MNIST, a prebuilt dataset of handwritten-digit images, so you can focus on the model workflow instead of collecting and labeling data. The example proceeds from data preparation to evaluation:
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- Import TensorFlow. The notebook uses TensorFlow’s APIs to load data and create the model.
- Load MNIST. It separates the data into training examples, used to fit the model, and test examples, held aside for evaluation.
- Normalize the image pixels. The example scales pixel values from 0–255 to 0–1. Rescaling puts the inputs in a more convenient numeric range for training.
- Build a Sequential model. Keras layers are composable transformations; connecting layers creates the model’s trainable computation. The example uses a straightforward sequence of layers for image classification.
- Configure training. The sample compiles the model with the Adam optimizer, sparse categorical cross-entropy loss, and accuracy as a metric.
- Train with
model.fit. The displayed example trains for five epochs. An epoch is one pass through the training data. - Evaluate on test data. The notebook checks how accurately the trained model classifies examples it did not use for fitting.
The five-epoch setting and any accuracy displayed in the notebook are part of a teaching example, not a promise of a particular result or training time. Your outcome can depend on the runtime and execution conditions.
Why TensorFlow uses Keras here
Keras is TensorFlow’s high-level API for building and training models. Its standard model-building and training methods let a beginner work through a complete example without starting with low-level TensorFlow operations. In this tutorial, the Sequential API organizes layers into a model, while model.fit handles the introductory training flow.
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TensorFlow’s tutorial index recommends beginning with the Keras Sequential API. That is a useful first choice when your goal is to understand the basic lifecycle of a model; more customization and lower-level techniques can wait until you need them.
What this tutorial does—and does not—cover
The quickstart shows the essential shape of a first model project: prepare a known dataset, define a model, train it, and evaluate it. It does not teach the full theory behind machine learning, robust data engineering, or how to deploy and monitor a production system. TensorFlow covers wider topics—including data pipelines, transfer learning, deployment, and MLOps—in its broader introduction to TensorFlow.
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What to learn after the first model
Continue with TensorFlow tutorials
After the quickstart, explore the tutorial index’s Keras basics and data-loading material. These are natural next steps: they deepen your understanding of the API and show how data enters a model. When you are ready to adapt standard workflows, move on to customization and advanced quickstarts.
Build a broader foundation if you want one
TensorFlow describes its ML basics curriculum as intended for people new to machine learning who have an intermediate programming background. It recommends Deep Learning with Python by François Chollet for foundational understanding and Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow by Aurélien Géron as a broader practical follow-up. Both are optional reading, not prerequisites for the free quickstart.
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