Discover fundamental deep learning concepts, master coding models using NumPy and Keras, and test your knowledge with quizzes and coding challenges. Gain insights into effective deep learning techniques.

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This beginner level and highly comprehensive course is intended for learners who are familiar with Python programming. You will become familiar with the fundamental concepts and terminologies used in deep learning. In addition, this course 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 course 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. You can test your knowledge with the quizzes that are provided at the end of every lesson and coding challenges that will help you gain a higher understanding. By the end of the course, you should have a general understanding of the basics in deep learning and you will be equipped with the right tools to learn more advanced concepts.

A Beginner's Guide to Deep Learning

# Problem statement

Code the backpropagation operation for the three layered neural network to compute the gradient of the **cross-entropy** loss function with respect to weights and biases of the neural network.

The cross entropy loss function is as follows:
$$
\mathcal{L}(y, s) = \sum_{i=1}^{c} y_i \cdot log(s_i)
$$
Where, $y$ is the target output, $s$ is the predicted output, and $c$ is the number of classes.


# Sample input
- The target output: `y` 
- The predicted output: `out_y` 
- The output at hidden layer 1: `out_h1`
- The output at hidden layer 2: `out_h2` 
- The weights on the connections between hidden layer 2 and the output layer: `w3`
- The weights on the connections between hidden layer 1 and the hidden layer 2: `w2`
- The input values: `x`

# Sample output
The change of weights and bias at the respective layers. For example:
- The gradient of loss w.r.t weights at layer 3: `dW3`
- The gradient of loss w.r.t bias at layer 3: `db3`
- The gradient of loss w.r.t weights at layer 2: `dW2`
- The gradient of loss w.r.t bias at layer 2: `db2`
- The gradient of loss w.r.t weights at layer 1: `dW1`
- The gradient of loss w.r.t bias at layer 1: `db1`

# Coding exercise
Write your code below. It is recommended​ to solve the exercise before viewing the solution.

> 📝 
> There is a `backpropagation` function given in the code for testing purposes. Do not modify the function signature.


Good luck!🤞

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

✨Before We Begin

✨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

Deep Learning in Keras Exam

🍀 Challenge: Backpropagation - 3 Layered Neural Network

Problem statement