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Introduction to Neural Networks
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Kursinhalt

Introduction to Neural Networks

Introduction to Neural Networks

1. Concept of Neural Network
What is a Neural Network?Neural Networks or Traditional ModelsApplications of Deep Learning in the Real WorldNeural 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:

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:

Aufgabe

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.

Lösung

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Abschnitt 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:

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:

Aufgabe

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.

Lösung

Switch to desktopWechseln Sie zum Desktop, um in der realen Welt zu übenFahren Sie dort fort, wo Sie sind, indem Sie eine der folgenden Optionen verwenden
War alles klar?

Wie können wir es verbessern?

Danke für Ihr Feedback!

Abschnitt 2. Kapitel 4
Switch to desktopWechseln Sie zum Desktop, um in der realen Welt zu übenFahren Sie dort fort, wo Sie sind, indem Sie eine der folgenden Optionen verwenden
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