This consists of the following works:

• Creating a classification model using a Logistic Regression with a Neural Network mindset
• A sample neural net with a 1 hidde layer that is used for a image classification problem
• A deep neural net with multiple hidden layers that has better accuracy when compared to above problems

Concepts covered are:

• Initialization of the Weights and biases
• Forward Propagation: Linear and Activation using different activation functions
• Backward Propagation: derivatives using the cache
• Compute Cost: uses the loss function to come up with the cost
• Updating the parameters: leverages the learning rate too
• Predict using the model
• Cost Optimization Analysis