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 - 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

## The `compile` method

After the model architecture is set, the model can be compiled using the `compile` method. This sets up the network for optimization. It creates an internal function to perform backpropagation efficiently.


## Arguments for the `compile` method
The compile method has two arguments:
 
### 1. Optimizer

  The optimizer helps specify the learning rate. The learning rate is important since it will help the model quickly find a good set of weights.  

> 📝 Note: There are a few optimizers that automatically tune the learning rate. We do not need to know the details of each optimizer. However, we need to choose an optimizer that serves as a good example for many problems. 

`Adam` (**adaptive moment estimation**) is a good choice. It adjusts the learning rate during gradient descent and ensures reasonable values for the weights during the weight optimization process. 



### 2. Loss function
The loss function specifies the loss during model training. 

#### Regression
Mean squared error is the most common choice for _regression problems_.






# The `compile` method

After the model architecture is set, the model can be compiled using the `compile` method. This sets up the network for optimization. It creates an internal function to perform backpropagation efficiently.


# Arguments for the `compile` method
The compile method has two arguments:
 
## 1. Optimizer

  The optimizer helps specify the learning rate. The learning rate is important since it will help the model quickly find a good set of weights.  

> 📝 Note: There are a few optimizers that automatically tune the learning rate. We do not need to know the details of each optimizer. However, we need to choose an optimizer that serves as a good example for many problems. 

`Adam` (**adaptive moment estimation**) is a good choice. It adjusts the learning rate during gradient descent and ensures reasonable values for the weights during the weight optimization process. 



## 2. Loss function
The loss function specifies the loss during model training. 

### Regression
Mean squared error is the most common choice for _regression problems_.






Study another step, i.e.,Keras' model compilation.

✨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 Compilation

The `compile` method

Arguments for the `compile` method

1. Optimizer