Course Content

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

Single Neuron Implementation

The fundamental computational unit of a neural network is the neuron. A neuron can be visualized as a small processing unit that takes multiple inputs, processes them, and gives a single output.

Here's what happens step by step:

Each input is multiplied by a corresponding weight. The weights are learnable parameters and they determine the importance of the corresponding input;
All the weighted inputs are summed together;
In our implementation, we will also add an additional parameter called bias to the input sum. The bias allows the neuron to shift its output up or down, adding flexibility to the modeling capability;
Then the input sum is passed through an activation function. We are using the sigmoid function, which squashes values into the range (0, 1).

Note
Bias of the neuron is also a trainable parameter.

Task

Implement the basic structure of a neuron. Complete the missing parts of the neuron class:

Enter the number of inputs of the neuron.
Use the uniform function to generate a random bias for every neuron.
Enter the activation function of the neuron.

_{Once you've completed this task, click the button below the code to check your solution.}

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Section 2. Chapter 1

Single Neuron Implementation

Here's what happens step by step:

Each input is multiplied by a corresponding weight. The weights are learnable parameters and they determine the importance of the corresponding input;
All the weighted inputs are summed together;
In our implementation, we will also add an additional parameter called bias to the input sum. The bias allows the neuron to shift its output up or down, adding flexibility to the modeling capability;
Then the input sum is passed through an activation function. We are using the sigmoid function, which squashes values into the range (0, 1).

Note
Bias of the neuron is also a trainable parameter.

Task

Implement the basic structure of a neuron. Complete the missing parts of the neuron class:

Enter the number of inputs of the neuron.
Use the uniform function to generate a random bias for every neuron.
Enter the activation function of the neuron.

_{Once you've completed this task, click the button below the code to check your solution.}

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 2. Chapter 1

Single Neuron Implementation

Here's what happens step by step:

Each input is multiplied by a corresponding weight. The weights are learnable parameters and they determine the importance of the corresponding input;
All the weighted inputs are summed together;
In our implementation, we will also add an additional parameter called bias to the input sum. The bias allows the neuron to shift its output up or down, adding flexibility to the modeling capability;
Then the input sum is passed through an activation function. We are using the sigmoid function, which squashes values into the range (0, 1).

Note
Bias of the neuron is also a trainable parameter.

Task

Implement the basic structure of a neuron. Complete the missing parts of the neuron class:

Enter the number of inputs of the neuron.
Use the uniform function to generate a random bias for every neuron.
Enter the activation function of the neuron.

_{Once you've completed this task, click the button below the code to check your solution.}

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

Everything was clear?

Thanks for your feedback!

Here's what happens step by step:

Each input is multiplied by a corresponding weight. The weights are learnable parameters and they determine the importance of the corresponding input;
All the weighted inputs are summed together;
In our implementation, we will also add an additional parameter called bias to the input sum. The bias allows the neuron to shift its output up or down, adding flexibility to the modeling capability;
Then the input sum is passed through an activation function. We are using the sigmoid function, which squashes values into the range (0, 1).

Note
Bias of the neuron is also a trainable parameter.

Task

Implement the basic structure of a neuron. Complete the missing parts of the neuron class:

Enter the number of inputs of the neuron.
Use the uniform function to generate a random bias for every neuron.
Enter the activation function of the neuron.

_{Once you've completed this task, click the button below the code to check your solution.}

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

Section 2. Chapter 1

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