Machine learning with TensorFlow: From linear algebra to neural networks (Day 2)

Description

Incorporating machine learning capabilities into software or apps is quickly becoming a necessity rather than a “nice to have” feature, and the open source machine learning framework TensorFlow is becoming an industry standard.

Rich Ott leads you through two days of intensive learning that include a review of linear algebra essential to machine learning, an introduction to TensorFlow, and a dive into neural networks. You’ll master simple machine learning models such as classification and regression models, construct and launch graphs in TensorFlow by using TensorBoard to visualize workflow, and build and test models in TensorFlow using real-world data. You’ll leave with both a theoretical and practical understanding of the algorithms behind machine learning and be ready to incorporate them into your next project.

The class will be taught using TensorFlow’s Python interface.

Outline

Day 1

• Practical linear algebra
• Introduction to TensorFlow
• Iterative algorithms

Exercises

• Implementing a basic graph
• Reducing tensors of arbitrary shape
• Fibonacci numbers
• Minimizing functions

Day 2

• Machine learning
• Basic neural networks
• Deep neural networks

Exercises

• Multidimensional linear regression
• Tuning hyperparameters and visualizing the weight matrix
• Build an Iris classifier
• Adding neurons and layers to a neural network
• Implementing early stopping
• Exploring activation functions, dropout, and learning rates