https://github.com/timeless-residents/handson-tensorflow-numpy
A practical guide to learning and using TensorFlow and NumPy for data manipulation and machine learning
https://github.com/timeless-residents/handson-tensorflow-numpy
data-science machine-learning numpy tensorflow tutorial
Last synced: about 1 year ago
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A practical guide to learning and using TensorFlow and NumPy for data manipulation and machine learning
- Host: GitHub
- URL: https://github.com/timeless-residents/handson-tensorflow-numpy
- Owner: timeless-residents
- Created: 2025-03-12T19:55:00.000Z (about 1 year ago)
- Default Branch: main
- Last Pushed: 2025-03-12T20:11:12.000Z (about 1 year ago)
- Last Synced: 2025-03-12T20:33:17.638Z (about 1 year ago)
- Topics: data-science, machine-learning, numpy, tensorflow, tutorial
- Language: Jupyter Notebook
- Size: 0 Bytes
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
# Hands-on TensorFlow & NumPy
A practical guide to learning and using TensorFlow and NumPy for data manipulation and machine learning.
## Overview
This repository contains examples, exercises, and tutorials that demonstrate how to use TensorFlow and NumPy together effectively for data science and deep learning tasks.
## Contents
- Basic tensor operations
- NumPy integration with TensorFlow
- Data preprocessing techniques
- Simple neural networks
- Performance comparison between TensorFlow and NumPy
## Getting Started
### Option 1: With TensorFlow (Python 3.10)
```bash
# Create Python 3.10 virtual environment
python3.10 -m venv venv_tf
# Activate virtual environment
source venv_tf/bin/activate # On Unix/macOS
# or
.\venv_tf\Scripts\activate # On Windows
# Install requirements
pip install tensorflow numpy matplotlib pandas scikit-learn jupyter
# Run example
python examples/basic_operations.py
```
### Option 2: Without TensorFlow (any Python version)
This repository includes a TensorFlow substitute that provides basic functionality using only NumPy.
```bash
# Create virtual environment
python -m venv venv
# Activate virtual environment
source venv/bin/activate # On Unix/macOS
# or
.\venv\Scripts\activate # On Windows
# Install requirements (excluding TensorFlow)
pip install numpy matplotlib pandas scikit-learn jupyter
# Run example with TensorFlow substitute
python examples/basic_operations_substitute.py
```
## Running Jupyter Notebooks
```bash
# Activate your chosen virtual environment
source venv_tf/bin/activate # With TensorFlow
# or
source venv/bin/activate # Without TensorFlow
# Start Jupyter
jupyter notebook
```
## License
MIT