Swift for TensorFlow Design Overview

April 27, 2018

Table of Contents

Swift for TensorFlow provides a new programming model for TensorFlow – one that combines the performance of graphs with the flexibility and expressivity of Eager execution, while keeping a strong focus on improved usability at every level of the stack. In order to achieve our goals, we scope in the possibility of making (carefully considered) compiler and language enhancements, which enables us to provide a great user experience.

Source: github.com

New A.I. application can write its own code

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Introduction to Decision Tree Learning

From Kaggle to classrooms, one of the first lessons in machine learning involves decision trees. The reason for the focus on decision trees is that they aren’t very mathematics heavy compared to other ML approaches, and at the same time, they provide reasonable accuracy on classification problems.

Swift for TensorFlow Design Overview

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