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Learn TensorFlow

Build, train and ship neural networks with TensorFlow and Keras.

Free to start. Built from a single prompt. No credit card.

TensorFlow is an open-source machine learning platform originally developed at Google, and most people use it through Keras, its high-level API for building neural networks. With Keras you stack layers in a Sequential model or wire them with the functional API, call compile to choose a loss and optimizer, and call fit to train. Underneath sit tensors, automatic differentiation with GradientTape, the tf.data input pipeline and tools such as TensorBoard for watching training. Learnr builds a TensorFlow course around your experience, moving from a first Keras model to custom training and deployment formats in short, practical lessons.

How to learn TensorFlow, step by step

Train a Keras model on a built-in dataset. Load MNIST from keras.datasets, normalize pixel values, stack Flatten and Dense layers in a Sequential model, compile with sparse categorical crossentropy and call fit. Reading the per-epoch output teaches loss and accuracy in practice.
Use validation data and callbacks. Pass validation_split or validation_data to fit, then add EarlyStopping and ModelCheckpoint callbacks. Open TensorBoard to compare training and validation curves and spot overfitting as soon as the lines diverge.
Learn the functional API. Rebuild your model with keras.Input and layers called like functions, then make one with two inputs or a skip connection. The functional style is what most real architectures need once a simple stack is not enough.
Feed data with tf.data. Build a Dataset from files, then chain map, shuffle, batch and prefetch. Efficient pipelines often matter as much as the model, and learning them early prevents a slow, GPU-starved training loop.
Write one custom training step. Use tf.GradientTape to compute gradients and apply them with an optimizer. Doing this once shows what fit does behind the scenes. Quick Learnr reviews between sessions keep layer names and loss choices fresh.
Save, reload and plan deployment. Save a model in the native Keras format, reload it and check that predictions match. Then read about the export paths for serving, browsers and mobile so you know which format your target platform expects.

What you'll learn

Build models with the Keras Sequential and functional APIs
Train and evaluate with compile, fit, evaluate and predict
Write efficient input pipelines with tf.data
Monitor training with callbacks and TensorBoard
Save, reload and export trained models

What's inside your TensorFlow course

A sample path Learnr builds for TensorFlow. Your real course adapts to your goal and level.

1. Tensors and the TensorFlow ecosystem
2. Your first Keras model
3. Losses, optimizers, metrics and callbacks
4. Input pipelines with tf.data
5. Convolutional and recurrent models
6. Custom training with GradientTape and saving models

How long does it take to learn TensorFlow?

With Python and basic machine learning knowledge, building and training Keras models takes about 3 to 5 weeks at 5 hours a week. Custom training loops, tf.data tuning and deployment add another 1 to 3 months of project practice.

What you need before you start

Comfortable Python and NumPy, and a basic understanding of neural networks, loss functions and gradient descent. No GPU is required to start; free hosted notebooks are enough.

Common mistakes to avoid

Following outdated tutorials written for TensorFlow 1, with sessions and placeholders. Check that examples use TensorFlow 2 and Keras before copying them.
Choosing the wrong loss for your labels, such as categorical crossentropy with integer labels. Match sparse or one-hot losses to the label format.
Skipping a validation set and judging a model by training accuracy alone, which hides overfitting until the model meets real data.

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TensorFlow questions, answered

What is the difference between TensorFlow and Keras?

Keras is a high-level API for defining and training neural networks. TensorFlow is the broader platform with tensors, automatic differentiation, data pipelines and deployment tools. Keras is the recommended way to build models in TensorFlow, and recent Keras versions can also run on other backends.

Is TensorFlow still worth learning?

Yes, if your team, course or deployment target uses it. Many concepts, such as layers, losses, optimizers and data pipelines, carry over directly to PyTorch and other frameworks.