🧠 Tensor and TensorFlow Proficiency Demonstration

This repository is designed to demonstrate practical knowledge of Tensors and TensorFlow, one of the most popular open-source machine learning libraries developed by Google.

🔍 Overview

This project includes:

• Tensor fundamentals: creation, types, shapes, indexing, broadcasting, and reshaping.
• Tensor operations: arithmetic, linear algebra, slicing, reshaping, and reduction operations.
• Gradient computation using tf.GradientTape.
• GPU utilization and tensor device placement.
• Simple neural network model built using the TensorFlow keras API.
• Custom training loop with manual gradient application.

Each concept is demonstrated through clean, commented Python scripts and notebooks.

---

📁 Repository Structure

.
├── README.md
├── requirements.txt
├── notebooks/
│   ├── 01_tensor_basics.ipynb
│   ├── 02_tensor_operations.ipynb
│   ├── 03_gradient_tape.ipynb
│   ├── 04_neural_network_model.ipynb
├── scripts/
│   ├── tensor_operations.py
│   ├── simple_model.py
│   └── gradient_descent_demo.py
└── data/
    └── dummy_dataset.csv (if applicable)