A practical TensorFlow-based project that demonstrates core tensor operations and fundamental deep learning concepts using Python.
This project is designed to introduce and demonstrate essential TensorFlow operations used in deep learning workflows. It covers the creation and manipulation of tensors, mathematical operations, reshaping, and basic computational concepts required for building neural network models.
The notebook serves as a strong foundation for students and beginners who want hands-on experience with TensorFlow.
- Understand TensorFlow and tensor fundamentals
- Perform core mathematical and matrix operations
- Learn tensor creation, reshaping, and manipulation
- Build a strong base for deep learning and neural networks
- Python
- TensorFlow
- NumPy
- Matplotlib
- Jupyter Notebook
TNAU-TensorFlow-Operations/
├── TNAU_TensorflowOperations.ipynb
├── README.md
- Tensor creation and data types
- Tensor shape and reshaping
- Mathematical operations on tensors
- Matrix multiplication
- Broadcasting
- Basic TensorFlow workflows
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Clone the repository
git clone https://github.com/your-username/TNAU-TensorFlow-Operations.git cd TNAU-TensorFlow-Operations -
Install required libraries
pip install tensorflow numpy matplotlib
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Open Jupyter Notebook
jupyter notebook
-
Run the notebook
TNAU_TensorflowOperations.ipynb
- Foundation for deep learning projects
- Understanding neural network computations
- Academic lab and coursework reference
- Precursor to CNNs, RNNs, and advanced AI models
- Add automatic differentiation examples
- Implement a small neural network model
- Visualize tensor transformations
- Include performance benchmarking