The-Hello-World-of-Deep-Learning-with-Neural-Networks

This is a beginner-level project that introduces the basics of building and training a neural network using TensorFlow and Keras. The project aims to demonstrate how a neural network can learn the patterns and relationships between two sets of data and make predictions based on that learning.

The project begins with defining a set of numbers, X and Y, and identifying the relationship between them. The goal is to train a neural network to recognize this relationship and predict the value of Y for any given value of X.

The project consists of the following steps:

1. Imports: Importing the necessary libraries and modules, including TensorFlow, NumPy, and Keras.

2. Define and compile the neural network: Creating a simple neural network with one layer and one neuron and compiling it with a loss function and an optimizer function.

3. Provide the data: Feeding the neural network with a set of X and Y values using NumPy arrays.

4. Train the neural network: Training the neural network to recognize the relationship between X and Y by fitting it with the provided data for a specified number of epochs.

5. Use the model: Predicting the value of Y for any given value of X using the trained neural network.