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

# What is a neural network?
Neural networks are multilayer networks of neurons that are used to classify and make predictions.

# Architecture of a neural network
The Neural Network consists of an input layer, a hidden layer, and an output layer.

- **Input units**: The input units represent raw information that is fed into the network. These input units make the input layer of the neural network. The hidden layer is next to the input layer and it consists of hidden units.

- **Hidden units:** The hidden unit output is generated by the weights on the connections between the input and the hidden units. The output layer is next to the hidden layer and it consists of the output units.

- **Output units**: The output units represent the output generated by the hidden units and the weights on the connections between the hidden and output units. 


> 📝 Note: Notice that each node in the hidden layer aggregates the information from the input layer. Hence adding to the model's ability to capture interactions. Consequently, the model makes useful 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

Neural Network

What is a neural network?

Architecture of a neural network