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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keras python - using models

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

# Discrete vs. continuous predictions
The prediction is the answer we receive from the algorithm. 
A discrete answer would result in the output of "yes" or "no". And, a continuous output would be a number between 0 and 1. 




Consider the classification problem where it is required to draw a decision boundary that separates the stars from squares. A discrete algorithm would tell us whether the class is stars by predicting 1 for stars and 0 for squares respectively.  In the case of continuous output, the probability of belonging to a class is the function of distance from the separating line (decision boundary).

In the illustration provided below, the discrete output gives the answer of "yes" or "no" if the class is stars. The continuous output gives the probability of shape belonging to the stars class.



So far you have learned to make discrete predictions using the step activation function. Now, learn about continuous predictions.

✨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

Discrete vs. Continuous Prediction

Discrete vs. continuous predictions