Free ebook on TensorFlow basics: build, train, save, and serve your first machine learning models with Keras and tf.data.
Free ebook content
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TensorFlow and Keras Essentials for Building Your First Model
+ Exercise: In a typical TensorFlow + Keras workflow, what is the main benefit of saving the full model before loading it for inference?
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Tensors in TensorFlow: Shapes, Dtypes, and Core Operations
+ Exercise: You have a tensor x with shape (batch, features). You compute m = tf.reduce_mean(x, axis=1) and then try to concatenate m back to x along axis=1. What is the correct fix to make the concatenation work as intended?
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Building Input Pipelines with tf.data.Dataset
+ Exercise: You need a randomized but repeatable train/validation split from a single tf.data.Dataset. Which approach best achieves this?
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Designing Models with Keras: Layers, Shapes, and Functional APIs
+ Exercise: In a Functional API model that merges numeric features with an embedded product ID, why is a Flatten (or Reshape) typically applied after the Embedding layer before Concatenate?
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Training with model.fit: Losses, Optimizers, and Metrics
+ Exercise: During Keras training with model.fit(), which statement correctly describes the role of metrics compared to the loss?
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Custom Training with GradientTape for More Control
+ Exercise: In a custom TensorFlow training loop using tf.GradientTape, what is a key reason to call the model with training=True during the training step?
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Saving and Loading TensorFlow Models as Reusable Artifacts
+ Exercise: You want to resume training later with the same optimizer state (e.g., Adam moments) after moving the model to another machine. Which saving approach best fits this goal?
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Basic Model Serving Patterns: From Python Inference to HTTP Endpoints
+ Exercise: When turning a trained TensorFlow SavedModel into a minimal HTTP prediction service, what is the most important practice to prevent common serving bugs?
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Clean Project Structure: From Notebook Prototype to Maintainable Codebase
+ Exercise: What is the main benefit of keeping preprocessing in a shared data pipeline module and reusing it during both training and inference?
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End-to-End Capstone: Train, Export, and Serve Your First TensorFlow Model
+ Exercise: Which approach best prevents training/serving skew when exporting and serving the tip prediction model?
About the free ebook
TensorFlow for Beginners: Building and Serving Your First Models
This free ebook introduces the practical workflow of building machine learning applications with TensorFlow and Keras. It focuses on the essential concepts needed to move from data and model design to training, exporting, and basic deployment.
Build a solid TensorFlow foundation
Learn how tensors represent data, how shapes and dtypes affect computations, and how core TensorFlow operations fit into a model-building workflow. The ebook also explains how to prepare reliable, efficient input pipelines with tf.data.Dataset.
Design and train models with Keras
Explore Keras layers, input and output shapes, Sequential-style design, and the Functional API for more flexible architectures. You will see how losses, optimizers, and metrics work together during training with model.fit, then use GradientTape when a custom training loop gives you more control.
Turn trained models into usable artifacts
A trained model is most valuable when it can be reused. This ebook covers saving and loading TensorFlow models, organizing project files beyond a notebook prototype, and applying simple serving patterns from Python inference to HTTP endpoints.
Practice an end-to-end workflow
The final capstone brings the process together: prepare data, train a model, export it, and serve predictions. By following this workflow, you can understand the path from an initial TensorFlow experiment to a maintainable machine learning application.
What you will be able to do
- Work with tensors, datasets, and Keras model components.
- Train models using built-in and custom TensorFlow workflows.
- Save models as reusable artifacts for inference.
- Structure a small project for clearer maintenance and deployment.
What is the difference between TensorFlow and Keras?
TensorFlow is the machine learning framework; Keras is its high-level API for designing and training models.
How do I save a TensorFlow model for later inference?
You can export a trained model as a reusable SavedModel artifact and load it later for predictions.
When should I use tf.data.Dataset?
Use tf.data.Dataset to load, transform, batch, and prefetch data efficiently during training.
This ebook includes:
10 content chapters
Digital certificate of course completion (Free)
Exercises to train your knowledge
100% free, from content to certificate
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