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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keras python

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

## Validation
The model performance on the training data is not a good indicator of how well the model will perform on the test data. For this, we should use the validation data to test the model's performance.

Validation data is only held out from training and is only used to test the model's performance.



The validation set checks overfitting and therefore eliminates errors that can be caused for future predictions and observations if an analysis corresponds too precisely to a specific dataset.










In the graph to the right, the training and validation error  decreases 
as the model complexity increases. However, at a certain point the 
training error decreases and the validation error increases.
This is where the model starts to overfit.
The green line is where the model should stop training to avoid overfitting.

# Validation
The model performance on the training data is not a good indicator of how well the model will perform on the test data. For this, we should use the validation data to test the model's performance.

Validation data is only held out from training and is only used to test the model's performance.



✨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

Model Validation - I

Validation