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

## What is deep learning?

Deep learning is a subset of Machine learning. It is a discipline dedicated to the study and development of machines that can learn. 

While there are so many Machine learning algorithms, why should one gravitate towards deep learning algorithms? What does the learning paradigm offer that traditional Machine learning algorithms do not? Let's explore what makes deep learning special.


## Machine learning vs. deep learning
There are two key reasons that make deep learning more special than machine learning:
1. Decision Boundary
2. Feature Engineering

### Decision boundary
In the case of classification problems,  the algorithm learns the mapping function that separates the two classes. This is known as a Decision boundary. A Decision boundary helps determine whether a given data point belongs to a positive or negative class.

For example, imagine there is an image with stars and squares. You are asked to create a boundary to separate the two classes. 
The choice of the model would depend on the data distribution. Let's visualize this:




# What is deep learning?

Deep learning is a subset of Machine learning. It is a discipline dedicated to the study and development of machines that can learn. 

While there are so many Machine learning algorithms, why should one gravitate towards deep learning algorithms? What does the learning paradigm offer that traditional Machine learning algorithms do not? Let's explore what makes deep learning special.


# Machine learning vs. deep learning
There are two key reasons that make deep learning more special than machine learning:
1. Decision Boundary
2. Feature Engineering

## Decision boundary
In the case of classification problems,  the algorithm learns the mapping function that separates the two classes. This is known as a Decision boundary. A Decision boundary helps determine whether a given data point belongs to a positive or negative class.

For example, imagine there is an image with stars and squares. You are asked to create a boundary to separate the two classes. 
The choice of the model would depend on the data distribution. Let's visualize this:




A quick introduction to deep learning and why one should gravitate towards it? 

✨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

What is Deep Learning?

What is deep learning?