TensorFlow-Examples

GitHub describes it as TensorFlow Tutorial and Examples for Beginners (support TF v1 & v2). The repository metadata lists Jupyter Notebook as its primary language. The metadata lists the NOASSERTION license. This article stays within the project description and details documented in the GitHub repository README.

GitHub describes it as TensorFlow Tutorial and Examples for Beginners (support TF v1 & v2). The repository metadata lists Jupyter Notebook as its primary language. The metadata lists the NOASSERTION license. The README describes the project this way: This tutorial was designed for easily diving into TensorFlow, through examples. For readability, it includes both notebooks and source codes with explanation, for both TF v1 & v2.

The README section "TensorFlow Examples" states: It is suitable for beginners who want to find clear and concise examples about TensorFlow. Besides the traditional 'raw' TensorFlow implementations, you can also find the latest TensorFlow API practices (such as layers , estimator , dataset , ...).

The README section "Tutorial index" states: 1 - Introduction - Hello World (notebook). Very simple example to learn how to print "hello world" using TensorFlow 2.0+. - Basic Operations (notebook). A simple example that cover TensorFlow 2.0+ basic operations.

The README section "Tutorial index" states: 2 - Basic Models - Linear Regression (notebook). Implement a Linear Regression with TensorFlow 2.0+. - Logistic Regression (notebook). Implement a Logistic Regression with TensorFlow 2.0+. - Word2Vec (Word Embedding) (notebook). Build a Word Embedding Model (Word2Vec) from Wikipedia data, with TensorFlow 2.0+. - GBDT (Gradient Boosted Decision Trees) (notebooks). Implement a Gradient Boosted Decision Trees with TensorFlow 2.0+ to predict house value using Boston Housing dataset.

The repository README is the source for this review. It does not replace a local installation or an independent test.

• Source 1 · Project repository
• Source 2 · README