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Introduction to Neural Networks
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Kurssisisältö

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

book
Forward Propagation

You have already implemented forward propagation for a single layer in the previous chapter. Now, the goal is to implement complete forward propagation, from inputs to outputs.

To implement the entire forward propagation process, you need to define the forward() method in the Perceptron class. This method performs forward propagation layer by layer by calling the respective method for each layer:

python

The inputs pass through the first hidden layer, with each layer's outputs serving as inputs for the next, until reaching the final layer to produce the final output.

Tehtävä

Swipe to start coding

Your goal is to implement forward propagation for the perceptron:

  1. Iterate over the layers of the perceptron.
  2. Pass x through each layer in the network sequentially.
  3. Return the final output after all layers have processed the input.

If the forward() method is implemented correctly, the perceptron should output a single number between 0 and 1 when given certain inputs (e.g, [1, 0]).

Ratkaisu

Switch to desktopVaihda työpöytään todellista harjoitusta vartenJatka siitä, missä olet käyttämällä jotakin alla olevista vaihtoehdoista
Oliko kaikki selvää?

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Kiitos palautteestasi!

Osio 2. Luku 5
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book
Forward Propagation

You have already implemented forward propagation for a single layer in the previous chapter. Now, the goal is to implement complete forward propagation, from inputs to outputs.

To implement the entire forward propagation process, you need to define the forward() method in the Perceptron class. This method performs forward propagation layer by layer by calling the respective method for each layer:

python

The inputs pass through the first hidden layer, with each layer's outputs serving as inputs for the next, until reaching the final layer to produce the final output.

Tehtävä

Swipe to start coding

Your goal is to implement forward propagation for the perceptron:

  1. Iterate over the layers of the perceptron.
  2. Pass x through each layer in the network sequentially.
  3. Return the final output after all layers have processed the input.

If the forward() method is implemented correctly, the perceptron should output a single number between 0 and 1 when given certain inputs (e.g, [1, 0]).

Ratkaisu

Switch to desktopVaihda työpöytään todellista harjoitusta vartenJatka siitä, missä olet käyttämällä jotakin alla olevista vaihtoehdoista
Oliko kaikki selvää?

Miten voimme parantaa sitä?

Kiitos palautteestasi!

Osio 2. Luku 5
Switch to desktopVaihda työpöytään todellista harjoitusta vartenJatka siitä, missä olet käyttämällä jotakin alla olevista vaihtoehdoista
Pahoittelemme, että jotain meni pieleen. Mitä tapahtui?
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