Gain insights into deep learning fundamentals, explore perceptron and advanced models, and discover practical coding in NumPy and Keras. Test your knowledge with quizzes and coding challenges.

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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

Train the 3 layered neural network.

- Call the `forward_propagation` function. 
- Call the error function `calculate_error` and save the loss in each epoch.
- Call the `backpropagation` function.
- Call the `update_parameters` function to update the weights and biases in each epoch.

## Sample input
- The input features: `X` 
- The target output: `y` 
- The weights of the three layers: `w1`, `w2`, and `w3` respectively
- The bias of the three layers `b1`, `b2`, and `b3` respectively 
- Total epochs: `epochs`
- The learning rate: `learning_rate`

## Sample output
- The updated weights and biases at the three layers respectively, i.e., `w1`, `b1`, `w2`, `b2`,`w3`, and `b3`
- The cross-entropy loss in each epoch saved in the `losses` array

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

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


Good luck!🤞

# Problem statement

Train the 3 layered neural network.

- Call the `forward_propagation` function. 
- Call the error function `calculate_error` and save the loss in each epoch.
- Call the `backpropagation` function.
- Call the `update_parameters` function to update the weights and biases in each epoch.

# Sample input
- The input features: `X` 
- The target output: `y` 
- The weights of the three layers: `w1`, `w2`, and `w3` respectively
- The bias of the three layers `b1`, `b2`, and `b3` respectively 
- Total epochs: `epochs`
- The learning rate: `learning_rate`

# Sample output
- The updated weights and biases at the three layers respectively, i.e., `w1`, `b1`, `w2`, `b2`,`w3`, and `b3`
- The cross-entropy loss in each epoch saved in the `losses` array

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

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


Good luck!🤞

Train the 3 layered neural network so that it finds the optimal weights that classify the three letters.

✨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

Anomaly Detection in Medical Images: Python, TF, and PyTorch

🖥️ Project: Build a Digit Recognition Model

✨Conclusion

Deep Learning in Keras Exam

Purchase Price Prediction for Used Cars Using Deep Learning

🍀 Challenge: Training - 3 Layered Neural Network

Problem statement