This is a comprehensive module for learners who wish to learn deep learning techniques with practical examples.

Neural Networks: Beginners to Advanced

This beginner level and highly comprehensive module is intended for learners who are familiar with Python programming. You will become familiar with the fundamental concepts and terminologies that are used in deep learning. In addition, this module will help you understand the importance of deep learning techniques. You will examine simple models like perceptron before learning more complex yet powerful deep learning models. The module will provide hands-on practical knowledge of how to code simple and complex deep learning models in NumPy, a powerful Python library, and Keras, a cutting-edge library for deep learning in Python. By the end of the module, you should have a general understanding of the basics of deep learning, and you will be equipped with the right tools to learn more advanced concepts.

Deep Learning with Keras

## Problem statement

Code the feedforward propagation algorithm for a 3 layered neural network.

>📝 1. Apply the sigmoid activation function to the net hidden layer outputs respectively.
>
>📝 2. Apply the softmax activation function to the net output of the final layer.


# Problem statement

Code the feedforward propagation algorithm for a 3 layered neural network.

>📝 1. Apply the sigmoid activation function to the net hidden layer outputs respectively.
>
>📝 2. Apply the softmax activation function to the net output of the final layer.


As a challenge, code the feedforward propagation pass for a 3 layered neural network.

✨Introduction to Deep Learning

✨Simple Perceptron Models in NumPY

✨Towards Deep Neural Networks in NumPY

🖥️ Project: Build a Letter Classification Model

Deep Learning in NumPy Exam

✨Building Deep Learning Models with Keras

✨Fine-tune Keras Model

🖥️ Project: Build a Digit Recognition Model

✨Conclusion

🍀 Challenge: Forward Propagation - 3 Layered Neural Network

Problem statement