A tutorial repository providing notebooks and source code that cover TensorFlow v1 and v2 implementations, ranging from basic models to neural networks and data management. It is designed to help developers, researchers, and educators learn TensorFlow APIs through clear, executable examples.
Project overview
Offers a comprehensive learning path by maintaining examples for both TensorFlow v1 and v2, allowing learners to study legacy and current API implementations side by side.
Project type
AI Coding · Model Development · Data Processing · Learning Resources
Use cases
Learning & Education
Deployment
Refer to project documentation
License
License pending
Best for
Developers, researchers, and educators seeking hands-on, executable code examples to learn or teach TensorFlow v1 and v2 APIs.
Users who need a self-hosted, locally runnable environment to practice basic machine learning models and data management techniques.
Key capabilities
Includes 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 designed for TensorFlow v1 implementations.
Limitations and risks
The repository exclusively focuses on the TensorFlow framework and does not cover other machine learning libraries.
Getting started
Setup is rated as easy, requiring users to clone the repository and run pip install tensorflow. GPU support is optional depending on the user's hardware and model training needs.
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.