TensorFlow Fundamentals
TensorFlow is a software platform for building machine learning models. You use it to work with numeric data, train models, and prepare them to run in environments such as servers, browsers, and devices.
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Intro
TensorFlow Fundamentals
TensorFlow is an end-to-end machine learning platform. It gives you numerical computation, automatic differentiation, model-building APIs, training support, and export paths in one ecosystem. You use it when a model must move from an experiment into a repeatable application or a target runtime.
Start with the workflow, not the package names. You prepare examples, define a model, choose how training measures error, adjust trainable weights, and evaluate the result on data held back from training. A model can fit its training data and still fail on new data. Evaluation is the check that matters.
The core mental model
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Sources
- https://www.tensorflow.org/
Supports
- TensorFlow as an end-to-end machine learning platform
- TensorFlow ecosystem components including TensorFlow.js, LiteRT, tf.data, TFX, and TensorBoard
- https://www.tensorflow.org/guide/basics
Supports
- Tensors as multidimensional arrays with shape and data type
- Variables as mutable state and tensors as immutable
- Automatic differentiation with GradientTape
- tf.function graph capture and export
- Modules, models, SavedModel, and custom training loops
- https://www.tensorflow.org/guide/keras
Supports
- Keras as TensorFlow's high-level API
- Keras as the normal starting API for TensorFlow users
- Layers as transformations and models as directed acyclic graphs of layers
- https://www.tensorflow.org/tutorials/quickstart/beginner
Supports
- Keras compile configuration
- Model.fit adjusting parameters to minimize loss
- The beginner Keras training and evaluation workflow
- https://www.tensorflow.org/guide
Supports
- Official guide areas for data input pipelines, SavedModel, accelerators, and performance
- https://www.tensorflow.org/guide/data
Supports
- tf.data input pipeline construction and transformations
- Batching, shuffling, and preprocessing in input pipelines
- https://www.tensorflow.org/guide/function
Supports
- tf.function tracing and graph execution behavior
- https://www.tensorflow.org/guide/saved_model
Supports
- SavedModel save, load, and signature behavior
- https://github.com/josephmisiti/awesome-machine-learning
Supports
- TensorFlow resource section with Awesome Keras and Awesome TensorFlow
- https://github.com/jtoy/awesome-tensorflow
Supports
- TensorFlow projects, libraries, and learning material beyond official documentation