# instillai/tensorflow-course

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16,285 stars · 3,135 forks · Jupyter Notebook · MIT

## Links

- GitHub: https://github.com/instillai/TensorFlow-Course
- awesome-repositories: https://awesome-repositories.com/repository/instillai-tensorflow-course.md

## Topics

`deep-learning` `deep-learning-tutorial` `python` `tensorflow`

## Description

This project is a TensorFlow learning course consisting of a deep learning tutorial series and guided modules. It provides the source code and documentation necessary to build and train neural network architectures and machine learning algorithms.

The repository serves as a machine learning deployment guide, providing practical examples for moving trained models from development environments into production. It includes templates and guided tutorials for model development and prototyping.

The course covers AI model education through a structured curriculum focused on tensor-based computations. It includes the implementation of deep learning algorithms and the transition of those models into a production-ready state.

## Tags

### Education & Learning Resources

- [AI & Machine Learning Education](https://awesome-repositories.com/f/education-learning-resources/technical-domain-education/ai-machine-learning-education.md) — Offers a structured curriculum covering the fundamentals of tensor-based computations and AI algorithms.
- [Deep Learning Courses](https://awesome-repositories.com/f/education-learning-resources/deep-learning-courses.md) — Delivers a comprehensive course with tutorials and templates for building and deploying models with TensorFlow.
- [Deep Learning Tutorials](https://awesome-repositories.com/f/education-learning-resources/deep-learning-tutorials.md) — Provides a series of step-by-step educational modules on implementing neural network architectures.
- [Machine Learning Tutorials](https://awesome-repositories.com/f/education-learning-resources/machine-learning-tutorials.md) — Organizes educational content into discrete source files mapping machine learning concepts to executable code.
- [Deployment Workflows](https://awesome-repositories.com/f/education-learning-resources/educational-resources/systems-applied-computing/machine-learning-education/deployment-workflows.md) — Provides guided documentation and templates for moving trained deep learning models into production. ([source](https://cdn.jsdelivr.net/gh/instillai/tensorflow-course@master/README.md))

### Artificial Intelligence & ML

- [TensorFlow Framework Implementations](https://awesome-repositories.com/f/artificial-intelligence-ml/tensorflow-framework-implementations.md) — Leverages the TensorFlow library for tensor operations and automatic differentiation during neural network training.
- [TensorFlow Model Development](https://awesome-repositories.com/f/artificial-intelligence-ml/tensorflow-model-development.md) — Teaches the design, building, and training of deep learning architectures specifically within the TensorFlow ecosystem.
- [Production Transition](https://awesome-repositories.com/f/artificial-intelligence-ml/deep-learning-development/production-transition.md) — Guides the process of moving trained machine learning models from development into a production-ready state.
- [Machine Learning Prototyping](https://awesome-repositories.com/f/artificial-intelligence-ml/machine-learning/infrastructure/model-training-and-tuning/machine-learning-prototyping.md) — Provides tools and source code for rapid experimentation with different neural network structures.
- [Model Implementation Templates](https://awesome-repositories.com/f/artificial-intelligence-ml/model-implementation-templates.md) — Uses pre-defined source code blueprints to standardize the creation of deep learning architectures and training loops.
- [Production Machine Learning Guides](https://awesome-repositories.com/f/artificial-intelligence-ml/production-machine-learning-guides.md) — Offers practical documentation and code examples for maintaining and operating machine learning systems in production.