Зміст курсу
Introduction to Neural Networks
Introduction to Neural Networks
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.
Swipe to show code editor
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.
Перейдіть на комп'ютер для реальної практикиПродовжуйте з того місця, де ви зупинились, використовуючи один з наведених нижче варіантівДякуємо за ваш відгук!
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.
Swipe to show code editor
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.
Перейдіть на комп'ютер для реальної практикиПродовжуйте з того місця, де ви зупинились, використовуючи один з наведених нижче варіантівДякуємо за ваш відгук!
Перейдіть на комп'ютер для реальної практикиПродовжуйте з того місця, де ви зупинились, використовуючи один з наведених нижче варіантів