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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

We know what is forward propagation while calculating the predicted value for the perceptron algorithm. 
## Feedforward in a neural network

Feedforward is the process that is used by neural networks to turn the given input into an output.

We have learned that the neural network is a combination of perceptrons. The perceptrons in each layer compute the output, apply the activation function, and will pass the information to the next layer. This process continues until the output layer is reached. In short, each node feeds the information it has to the next node. For this reason, it is called a feed-forward propagation algorithm.

## Visualize feedforward propagation
The following illustration shows a feedforward propagation algorithm using a neural network with a hidden layer comprising of two hidden units as seen below:

We know what is forward propagation while calculating the predicted value for the perceptron algorithm. 
# Feedforward in a neural network

Feedforward is the process that is used by neural networks to turn the given input into an output.

We have learned that the neural network is a combination of perceptrons. The perceptrons in each layer compute the output, apply the activation function, and will pass the information to the next layer. This process continues until the output layer is reached. In short, each node feeds the information it has to the next node. For this reason, it is called a feed-forward propagation algorithm.

# Visualize feedforward propagation
The following illustration shows a feedforward propagation algorithm using a neural network with a hidden layer comprising of two hidden units as seen below:

Learn about the feedforward operation in a neural network.

✨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

Feed Forward Propagation

Feedforward in a neural network

Visualize feedforward propagation