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

🎯 Goal: Keep training until the validation score is improving and stop training when it is not improving. Do this with an early stopping monitor.


## Early stopping
The early stopping monitor allows continual training of the model until the validation score stops to improve before fitting the model. 


## Early stopping in Keras
The monitor takes the argument `patience`, i.e., how many epochs the model can take without improving before we stop training. 

`early_stopping_monitor` is assigned to the `callback` argument within the `fit` method.



🎯 Goal: Keep training until the validation score is improving and stop training when it is not improving. Do this with an early stopping monitor.


# Early stopping
The early stopping monitor allows continual training of the model until the validation score stops to improve before fitting the model. 


# Early stopping in Keras
The monitor takes the argument `patience`, i.e., how many epochs the model can take without improving before we stop training. 

`early_stopping_monitor` is assigned to the `callback` argument within the `fit` method.



Learn about model validation using an early stopping monitor.

✨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

Model Validation - II

Early stopping

Early stopping in Keras