TensorFlow

As explained in Machine Learning for Tabular Data: XGBoost, Deep Learning, and AI

TensorFlow is an open-source software platform for building and executing machine learning computations, including neural networks.

TensorFlow is an open-source platform for building and executing machine-learning computations, including neural networks. In tabular-data workflows, it serves as the low-level deep-learning framework beneath Keras. section 8.1

A practical TensorFlow stack separates framework responsibilities from model-building code. Keras supplies the recommended high-level API, while TensorFlow executes the underlying deep-learning workflow. section 8.1

TensorFlow is positioned alongside several alternatives for deep learning with tabular data:

• TensorFlow is used more frequently in industry, while PyTorch is predominant in research. section 8.7
• Keras abstracts aspects of TensorFlow; comparable higher-level options include fastai and Lightning Flash for PyTorch. section 8.7
• TensorFlow supports tabular libraries such as SAINT, while TabNet is available for both TensorFlow and PyTorch. section 8.7

Airbnb listing price classification

A TensorFlow-and-Keras model can accept multiple inputs—one for each selected Airbnb listing column—and produce one prediction: whether the price is above or below the median. Model code defines layers for categorical, continuous, and text inputs; categorical inputs can use embedding and batch-normalization layers. section 8.1

Figure 8.2 The stack for the Airbnb NYC solution in chapter 3

The stack diagram helps separate TensorFlow's low-level role from Keras and the model components used in the Airbnb workflow. section 8.1

How Machine Learning for Tabular Data: XGBoost, Deep Learning, and AI covers it

How deep it goes

Explains TensorFlow's stack role and contrasts APIs, but defers detailed layer definitions and framework internals.

Examples

Uses an Airbnb price classifier with categorical, continuous, and text inputs plus embedding and batch-normalization layers.

What it assumes

Assumes familiarity with deep learning and tabular inputs while keeping detailed layer construction beyond scope.

What you can do after

Evaluate TensorFlow with Keras as a valid option for deep learning with tabular data on Colab.

How it connects

Connects TensorFlow with Keras, PyTorch, TabNet, SAINT, and deep learning with tabular data.

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Also explained in

• 

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  Deep Learning with R, Third EditionTensorFlow is an open-source platform and software ecosystem for numerical computation and machine learning.
• 

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  Deep Learning with Python, Third EditionA Python-based open source machine learning platform with an ecosystem of components for training, deployment, and related workflows.
• 

  liveBookmanning.com
  Distributed Machine Learning PatternsAn open-source platform for numerical computation and machine learning, including tools for building and training neural networks.

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Related concepts

• Kerasused by · Keras is an open-source, high-level deep-learning API for building, training, evaluating, and deploying neural-network models.
• autoencoderused by · An autoencoder is a neural network that encodes input data into an internal representation and decodes it to reconstruct the input or an approximation.
• PyTorchalternative to · PyTorch is an open-source machine-learning framework for Python that provides tensor computation, automatic differentiation, and tools for building and training neural networks.
• Pythonuses · Python is a high-level, general-purpose, dynamically typed programming language known for readable syntax and automatic memory management.
• backpropagationimplements · Backpropagation is a procedure for computing derivatives of an output or loss with respect to inputs and parameters by applying the chain rule backward through a computational graph.

This explainer was generated by Manning's knowledge graph from the text of Machine Learning for Tabular Data: XGBoost, Deep Learning, and AI. The section links point to the parts of the book it draws on.