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Introduction to TensorFlow in Python

Isaiah Hull

Visiting Associate Professor of Finance, BI Norwegian Business School

What you learned

  • Chapter 1
    • Low-level, basic, and advanced operations
    • Graph-based computation
    • Gradient computation and optimization
  • Chapter 2
    • Data loading and transformation
    • Predefined and custom loss functions
    • Linear models and batch training
Introduction to TensorFlow in Python

What you learned

  • Chapter 3
    • Dense neural network layers
    • Activation functions
    • Optimization algorithms
    • Training neural networks
  • Chapter 4
    • Neural networks in Keras
    • Training and validation
    • The Estimators API
Introduction to TensorFlow in Python

TensorFlow extensions

  • TensorFlow Hub
    • Pretrained models
    • Transfer learning

This image shows a screenshot of models returned from a TensorFlow Hub search.

  • TensorFlow Probability
    • More statistical distributions
    • Trainable distributions
    • Extended set of optimizers

This image shows a histogram with a fitted probability distribution.

1 Screenshot from https://tfhub.dev.
Introduction to TensorFlow in Python

TensorFlow 2.0

  • TensorFlow 2.0
    • eager_execution()
    • Tighter keras integration
    • Estimators
    • function()

This image shows a schematic of the Estimators API and how it relates to lower level APIs.

This image shows a diagram of a neural network.

The image shows a static graph in TensorFlow.

1 Screenshot taken from https://www.tensorflow.org/guide/premade_estimators
Introduction to TensorFlow in Python

Congratulations!

Introduction to TensorFlow in Python

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