Experimental TensorFlow Machine Learning Scripts

This repository contains a collection of experimental machine-learning scripts written using TensorFlow. These scripts demonstrate various machine-learning techniques and applications, ranging from image classification to time series forecasting.

These scripts were created as a part of my journey in picking up TensorFlow and deepening my understanding of its functionalities. Feel free to use, modify or experiment with any of these scripts for your own projects or learning purposes.

Requirements

• Python 3.x
• Tensorflow
• Numpy
• Jupyter Notebook (optional if you wish to run the .ipynb files)

Scripts Overview

Here's a brief summary of each script included in this repository:

1. hello_world.py: A basic demonstration that serves as an introductory TensorFlow "Hello World" example.
2. activation_demos.py: This script demonstrates different activation functions used in neural networks.
3. classify-images.py: A simple image classification script that uses Convolutional Neural Networks (CNNs).
4. classify-text.py: Implements text classification using Recurrent Neural Networks (RNNs).
5. fashion_mnist_classification.py: An implementation that classifies images from the Fashion MNIST dataset.
6. statistical-forecasting-with-synthetic-data.ipynb: A Jupyter Notebook that showcases statistical forecasting techniques using synthetic data.
7. time-series.ipynb: A Jupyter Notebook that demonstrates time series predictions with TensorFlow and LSTM layers.
8. train-text-lstm.py: A script that trains an LSTM network for text generation.