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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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

## The perceptron trick
To find the best possible boundary, the perceptron algorithm should
predict the output, compare it with the actual output, and learn the optimal weights for predicting the best possible fit function that separates the two classes.



> 📝 ** Question: How does the model learn?**
>
>The model learns for a couple of iterations until it finds the best possible boundary that separates the two classes. An initial-boundary is drawn and then the error is computed. In each iteration, the boundary line moves in the direction so that it minimizes the error. This process continues until the error is below a certain threshold.

The following illustration will help you visualize this:


# The perceptron trick
To find the best possible boundary, the perceptron algorithm should
predict the output, compare it with the actual output, and learn the optimal weights for predicting the best possible fit function that separates the two classes.



> 📝 ** Question: How does the model learn?**
>
>The model learns for a couple of iterations until it finds the best possible boundary that separates the two classes. An initial-boundary is drawn and then the error is computed. In each iteration, the boundary line moves in the direction so that it minimizes the error. This process continues until the error is below a certain threshold.

The following illustration will help you visualize this:


Learn how the perceptron output can be best optimized so that it finds the best possible boundary.

✨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

Optimizing the Perceptron Output

The perceptron trick