aymericdamien/TensorFlow-Examples

Project overview

This repository provides concise tutorials and source code for learning TensorFlow v1 and v2, covering basic models, neural networks, data management, and hardware. It is designed for developers and educators who need clear, executable examples to understand TensorFlow APIs without relying on external managed services.

Project type

AI Coding · Model Development · Data Processing

Use cases

Learning & Education

Deployment

Refer to project documentation

License

License pending

Best for

• Developers, educators, and researchers seeking self-hosted, executable tutorial code strictly focused on TensorFlow v1 and v2 basic models.

Key capabilities

• Provides notebooks and source code for implementing basic models such as Linear Regression, Logistic Regression, Word2Vec, and GBDT using TensorFlow 2.0+.
• Maintains a comprehensive archive of examples and notebooks specifically targeted at TensorFlow v1 implementations.

Limitations and risks

• The materials are exclusively focused on the TensorFlow framework and do not cover other machine learning libraries.

Getting started

• Setup is rated easy, requiring users to clone the repository and run pip install tensorflow. Interaction occurs via the command-line interface.

Evidence and sources

• README: 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.
• GitHub project description: TensorFlow Tutorial and Examples for Beginners (support TF v1 & v2)
• README: Implement a Linear Regression with TensorFlow 2.0+.
• README: Use TensorFlow 2.0 'layers' and 'model' API to build a simple neural network to classify MNIST digits dataset.
• README: Build a Deep Convolutional Generative Adversarial Network (DCGAN) to generate images from noise.

Reviewed at: 2026-07-17T07:22:27Z