In this notebook I build your first image recognition model using Logistic Regression on Numpy. This is a cat classifier that recognizes cats with 70% accuracy. In this assignment:
- I learned to Build the general architecture of a learning algorithm, including building functions for initializing parameters, calculating the cost function and its gradient, using an optimization algorithm (gradient descent), minimizing cost function and merging all functions into a model function.
- I Worked with logistic regression in a way that builds intuition relevant to neural networks.
- I Learned how to minimize the cost function.
- I Understoood how derivatives of the cost are used to update parameters.
- I brushed up on my Numpy skills