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Introduction to Neural Networks
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Kursinnehåll

Introduction to Neural Networks

Introduction to Neural Networks

1. Concept of Neural Network
What is a Neural Network?Applications of Deep Learning in the Real WorldNeural Networks or Traditional ModelsNeural Network StructureWhat is a Neuron?Activation FunctionsForward and Backward Propagation
2. Neural Network from Scratch
Single Neuron ImplementationChallenge: Creating a NeuronPerceptron LayersChallenge: Creating a PerceptronForward PropagationLoss FunctionBackward PropagationBackpropagation ImplementationModel TrainingChallenge: Training the PerceptronModel EvaluationChallenge: Evaluating the PerceptronNeural Network with scikit-learn
3. Conclusion
Other types of Neural NetworksHyperparameter TuningChallenge: Automatic Hyperparameter TuningQuiz: Neural Networks EssentialsSummary

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Challenge: Creating a Perceptron

Since our goal is to implement a multilayer perceptron, creating a Perceptron class will simplify model initialization. Its only attribute, layers is essentially a list of the Layer objects that define the structure of the network:

python

The variables used to initialize the layers are the following:

  • input_size: the number of input features;
  • hidden_size: the number of neurons in each hidden layer (both hidden layers will have the same number of neurons in this case);
  • output_size: the number of neurons in the output layer.

The structure of the resulting perceptron should be as follows:

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Swipe to start coding

Your goal is to set up the basic structure of the perceptron by implementing its layers:

  1. Initialize the weights (a matrix) and biases (a vector) with random values from a uniform distribution in range [-1, 1) using NumPy.
  2. Compute the raw output values of the neurons in the forward() method of the Layer class.
  3. Apply the activation function to the raw outputs in the forward() method of the Layer class and return the result.
  4. Define three layers in the Perceptron class: two hidden layers with the same number of neurons and one output layer. Both hidden layers should use the relu activation function, while the output layer should use sigmoid.

Lösning

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Var allt tydligt?

Hur kan vi förbättra det?

Tack för dina kommentarer!

Avsnitt 2. Kapitel 4
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book
Challenge: Creating a Perceptron

Since our goal is to implement a multilayer perceptron, creating a Perceptron class will simplify model initialization. Its only attribute, layers is essentially a list of the Layer objects that define the structure of the network:

python

The variables used to initialize the layers are the following:

  • input_size: the number of input features;
  • hidden_size: the number of neurons in each hidden layer (both hidden layers will have the same number of neurons in this case);
  • output_size: the number of neurons in the output layer.

The structure of the resulting perceptron should be as follows:

Uppgift

Swipe to start coding

Your goal is to set up the basic structure of the perceptron by implementing its layers:

  1. Initialize the weights (a matrix) and biases (a vector) with random values from a uniform distribution in range [-1, 1) using NumPy.
  2. Compute the raw output values of the neurons in the forward() method of the Layer class.
  3. Apply the activation function to the raw outputs in the forward() method of the Layer class and return the result.
  4. Define three layers in the Perceptron class: two hidden layers with the same number of neurons and one output layer. Both hidden layers should use the relu activation function, while the output layer should use sigmoid.

Lösning

Switch to desktopByt till skrivbordet för praktisk övningFortsätt där du är med ett av alternativen nedan
Var allt tydligt?

Hur kan vi förbättra det?

Tack för dina kommentarer!

Avsnitt 2. Kapitel 4
Switch to desktopByt till skrivbordet för praktisk övningFortsätt där du är med ett av alternativen nedan
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