Introduction to Neural Networks

Introduction to Neural Networks

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1. Concept of Neural Network

First, we will discuss what a neural network is and how it works. And also consider the scope of its application.

What is a Neural Network?

Neural Network Structure

What is a Neuron?

Activation Functions

Forward and Backward Propagation

Applications of Deep Learning in the Real World

2. Neural Network from Scratch

Next, we will try to build our own neural network and see how efficiently it copes with learning. We will also consider a ready-made solution from the scikit-learn library.

Single Neuron Implementation

Perceptron Layers

Forward Propagation

Backward Propagation

Data Preprocessing

Model Training and Evaluation

Neural Network with scikit-learn

3. Conclusion

Finally, we will give you some additional useful information on how to understand which model to use and what types of neural networks there are. To complete the course, you will be tested on your acquired knowledge.

Neural Networks or Traditional Models

Other types of Neural Networks

Challenge: Manual Hyperparameter Tuning

Challenge: Automatic Hyperparameter Tuning

Quiz: Conclusion

Course Content

Introduction to Neural Networks

Introduction to Neural Networks

1. Concept of Neural Network

What is a Neural Network?Neural Network Structure What is a Neuron?Activation Functions Forward and Backward Propagation Applications of Deep Learning in the Real World

2. Neural Network from Scratch

Single Neuron Implementation Perceptron Layers Forward Propagation Backward Propagation Data Preprocessing Model Training and Evaluation Neural Network with scikit-learn

3. Conclusion

Neural Networks or Traditional Models Other types of Neural Networks Challenge: Manual Hyperparameter Tuning Challenge: Automatic Hyperparameter Tuning Quiz: Conclusion Summary

Challenge: Manual Hyperparameter Tuning

Task

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Until now, we have used the neural network only for the classification task. Now try to solve the regression problem using MLPRegression.

To do this, we will get the real world California Housing dataset and try to teach the model to predict median home value for households in tens of thousands of US dollars. Your goal is to have the Mean Absolute Error (MAE) be less than 0.4.

Experiment with the hidden layers (use 1-2 hidden layers with 10-30 neurons for each).
Experiment with the number of epochs (change number of epochs from 10 to 50).
Experiment with the learning rate (change it in from 0.0001 to 1).

Make sure your MAE is less than 0.4 before submitting.

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Section 3. Chapter 3

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Challenge: Manual Hyperparameter Tuning

Task

Swipe to show code editor

Until now, we have used the neural network only for the classification task. Now try to solve the regression problem using MLPRegression.

To do this, we will get the real world California Housing dataset and try to teach the model to predict median home value for households in tens of thousands of US dollars. Your goal is to have the Mean Absolute Error (MAE) be less than 0.4.

Experiment with the hidden layers (use 1-2 hidden layers with 10-30 neurons for each).
Experiment with the number of epochs (change number of epochs from 10 to 50).
Experiment with the learning rate (change it in from 0.0001 to 1).

Make sure your MAE is less than 0.4 before submitting.

Switch to desktop for real-world practiceContinue from where you are using one of the options below

Everything was clear?

Thanks for your feedback!

Section 3. Chapter 3

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