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The best way to learn TensorFlow is to follow a sequence rather than open a random API reference. Start with the official Colab tutorials and Keras, then move to TensorFlow 2 concepts, data pipelines, custom training, distributed execution, deployment, and finally the TensorFlow 2.20 changes. The nine articles below are ordered so each one answers a different practical question.
The nine articles at a glance
| Article | Best for | API style or focus | Execution target | Main outcome |
|---|---|---|---|---|
| TensorFlow Tutorials | Beginners | Keras Sequential, quickstarts and fundamentals | Google Colab | Build a first model without local setup |
| Keras: The high-level API for TensorFlow | Beginners through intermediate users | High-level model, training and deployment APIs | CPU, GPU or TPU workflows | Use the default modeling workflow |
| TensorFlow 2 Guide | Intermediate users | Eager execution, flexible model building and optimization | Local or hosted TensorFlow environments | Understand TensorFlow 2 practices |
| Introduction to TensorFlow | Readers choosing a platform or product path | Platform and ecosystem overview | Desktop, mobile, web, cloud and edge | Map a project to TensorFlow tools |
| TensorFlow data-input guidance | Anyone with a nontrivial dataset | tf.data input pipelines |
CPU, GPU or TPU training | Build reusable, scalable data loading |
| Customization and advanced training tutorials | Intermediate to advanced users | Functional API, subclassing, custom layers and loops | Flexible training environments | Control model behavior and training |
| Distributed training tutorials | Teams scaling experiments or training jobs | Distribution strategies | Multiple GPUs, machines or TPUs | Scale training beyond one device |
| Deployment with Serving, LiteRT and TensorFlow.js | Engineers shipping inference | Server, on-device and browser tooling | Server, mobile/edge or browser | Choose an inference target |
| What’s new in TensorFlow 2.20 | Maintainers and current-project users | Release changes and migration awareness | Especially on-device development | Update code and deployment plans |
Start here: the two best TensorFlow tutorials for beginners
1. TensorFlow Tutorials
This is the strongest first stop for “best TensorFlow tutorials for beginners.” The notebooks run directly in Google Colab, so you can begin without installing TensorFlow locally. The official documentation describes them as “Jupyter notebooks” that run in Colab, “a hosted notebook environment that requires no setup.”
Follow the quickstarts first, then the Keras basics. The collection also introduces data loading with tf.data, customization and distributed training, giving you clear links to the later articles in this list. Its recommendation of the Keras Sequential API makes it appropriate for a first classifier, regression model or small prototype.
2. Keras: The high-level API for TensorFlow
Read this alongside the tutorials when you want to understand the normal TensorFlow workflow: prepare data, define a model, train it, tune it and deploy it. Keras covers those stages in one coherent API rather than making you assemble low-level operations immediately.
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The guide’s advice is unambiguous: “The short answer is that every TensorFlow user should use the Keras APIs by default.” Begin with Sequential models when your layers form a simple stack; move to the Functional API when you need multiple inputs, outputs or branches.
Build a reliable mental model of TensorFlow 2
3. TensorFlow 2 Guide
Use the TensorFlow 2 Guide after you can train a basic Keras model. It explains eager execution, higher-level APIs, flexible model construction, tf.data, serving and model optimization. That combination helps you understand why code behaves differently in TensorFlow 2 and which abstraction to choose.
This is the article to consult when a tutorial works but you do not yet understand tracing, input pipelines, saving, optimization or the boundary between a model and the system that operates it. It is more useful for forming durable habits than for copying a single project.
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4. Introduction to TensorFlow
Read this overview when your question is “Which TensorFlow product fits my application?” TensorFlow is an end-to-end platform, not only a neural-network layer library. The overview connects data preparation and model training with TensorFlow Serving, LiteRT, TensorFlow.js and TFX.
Its platform map covers desktop, mobile, web, cloud and edge scenarios. That makes it a good planning article before you commit to a deployment target or promise a production architecture.
Move from notebook examples to real data
5. TensorFlow data-input guidance
Choose this guidance when loading data has become the bottleneck or the least maintainable part of your project. The tf.data API takes you from simple datasets to reusable input pipelines that can scale with the training job.
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Work through the pipeline concepts before adding performance tricks. A sound pipeline separates reading, parsing, transformation, batching and prefetching, so the same data logic can be reused when you move from a Colab experiment to CPU, GPU or TPU training. This is the natural next article after a Sequential-model quickstart.
Gain control when Sequential is no longer enough
6. Customization and advanced training tutorials
Read these tutorials when your architecture or loss function does not fit a straight stack of layers. They cover the Functional API, model subclassing, custom layers, custom activations and custom training loops.
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Use the Functional API for graph-shaped models with shared layers or several inputs and outputs. Subclass a model or layer when behavior is easier to express as Python logic. Choose a custom loop when you need per-step control that the standard fit workflow does not expose. This progression avoids dropping to low-level TensorFlow operations before you need them.
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Scale training beyond one device
7. Distributed training tutorials
These tutorials are the right choice for “TensorFlow distributed training.” They cover multiple GPUs, multiple machines and TPUs, so you can match the lesson to the hardware you actually have.
Read them after your input pipeline and single-device training code are stable. Distribution strategies can scale a sound workload, but they cannot repair unclear batching, non-deterministic preprocessing or a model that has not been validated on one device. The outcome is a path from an individual experiment to a larger training job, not merely a faster notebook.
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8. Deployment with Serving, LiteRT and TensorFlow.js
Use the deployment material when the question changes from “Can I train this model?” to “Where will predictions run?” The main choices are distinct:
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- TensorFlow Serving: server-side inference for an application or service, including a production API boundary.
- LiteRT: on-device and edge inference where latency, connectivity, power or privacy favors local execution.
- TensorFlow.js: inference in the browser, useful when the model must run in a web experience.
For “TensorFlow deployment to mobile,” start with the on-device path. For “TensorFlow.js in the browser,” use the browser-specific material rather than adapting a server example. For “TensorFlow Serving production,” study serving alongside the operational concerns in TFX.
Production pipelines with TFX
When training is part of a repeatable service rather than a one-off release, the TensorFlow platform overview points to TFX. It addresses automation, model tracking, monitoring and retraining—the concerns that appear after the first successful deployment.
Keep current with the TensorFlow 2.20 change
9. What’s new in TensorFlow 2.20
TensorFlow announced version 2.20 on August 19, 2025. The release note says tf.lite is being replaced by LiteRT and that on-device development is moving to a new independent repository.
This is essential reading if your project imports tf.lite, builds mobile or edge packages, or copies deployment code from an older tutorial. Check the current documentation before copying commands: an example that was correct for an earlier TensorFlow release may need a LiteRT-specific update.
A practical reading order by goal
Learning TensorFlow with Keras
- Run the TensorFlow Tutorials in Colab.
- Read the Keras guide while rebuilding the same model with
Sequential. - Use the TensorFlow 2 Guide to explain eager execution, saving and optimization.
Building TensorFlow projects in Google Colab
- Start with the Colab notebooks and a small dataset.
- Move data handling to a clear
tf.datapipeline. - Use customization tutorials only when the model requires a Functional API, subclassing or a custom loop.
Preparing a production system
- Read the platform overview to select server, edge or browser inference.
- Study the relevant deployment tool: Serving, LiteRT or TensorFlow.js.
- Use TFX concepts for automation, tracking, monitoring and retraining.
- Check the TensorFlow 2.20 notes before finalizing an on-device integration.
When a book is a better companion
Free documentation is ideal for an API-first path. If you want a structured curriculum with exercises and end-to-end projects, Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow, 3rd Edition by Aurélien Géron is a strong companion. O’Reilly lists the October 2022 edition at 864 pages, with TensorFlow and Keras projects and exercises. Use the free articles to answer current API and deployment questions, and the book for a sustained project-based progression.
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