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Introduction to Neural Networks
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Contenido del Curso

Introduction to Neural Networks

Introduction to Neural Networks

1. Concept of Neural Network
What is a Neural Network?Neural Network StructureWhat is a Neuron?Activation FunctionsForward and Backward PropagationApplications of Deep Learning in the Real World
2. Neural Network from Scratch
Single Neuron ImplementationPerceptron LayersForward PropagationBackward PropagationData PreprocessingModel Training and EvaluationNeural Network with scikit-learn
3. Conclusion
Neural Networks or Traditional ModelsOther types of Neural NetworksChallenge: Manual Hyperparameter TuningChallenge: Automatic Hyperparameter TuningQuiz: ConclusionSummary

bookPerceptron Layers

Perceptron is the name of the simplest neural network, consisting of only one hidden layer. However, in order to be able to solve more complex problems, we will create a variation of perceptron called multilayer perceptron (MLP). A multilayer perceptron consists of multiple hidden layers. The structure of a multilayer perceptron looks like this:

  1. An input layer: It receives the input data;
  2. Hidden layers: These layers process the data and extract patterns. We have two hidden layers in our model;
  3. Output layer: Produces the final prediction or classifications.

In general, each layer consists of multiple neurons, and the output from one layer becomes the input for the next layer.

Tarea

Set up the basic structure of the perceptron by implementing its layers.

  1. Create neurons and specify number of their inputs.
  2. Active neurons for forward propagation.
  3. Define three layers: 2 hidden layers and 1 output layer.

Switch to desktopCambia al escritorio para practicar en el mundo realContinúe desde donde se encuentra utilizando una de las siguientes opciones
¿Todo estuvo claro?

¿Cómo podemos mejorarlo?

¡Gracias por tus comentarios!

Sección 2. Capítulo 2
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bookPerceptron Layers

Perceptron is the name of the simplest neural network, consisting of only one hidden layer. However, in order to be able to solve more complex problems, we will create a variation of perceptron called multilayer perceptron (MLP). A multilayer perceptron consists of multiple hidden layers. The structure of a multilayer perceptron looks like this:

  1. An input layer: It receives the input data;
  2. Hidden layers: These layers process the data and extract patterns. We have two hidden layers in our model;
  3. Output layer: Produces the final prediction or classifications.

In general, each layer consists of multiple neurons, and the output from one layer becomes the input for the next layer.

Tarea

Set up the basic structure of the perceptron by implementing its layers.

  1. Create neurons and specify number of their inputs.
  2. Active neurons for forward propagation.
  3. Define three layers: 2 hidden layers and 1 output layer.

Switch to desktopCambia al escritorio para practicar en el mundo realContinúe desde donde se encuentra utilizando una de las siguientes opciones
¿Todo estuvo claro?

¿Cómo podemos mejorarlo?

¡Gracias por tus comentarios!

Sección 2. Capítulo 2
toggle bottom row

bookPerceptron Layers

Perceptron is the name of the simplest neural network, consisting of only one hidden layer. However, in order to be able to solve more complex problems, we will create a variation of perceptron called multilayer perceptron (MLP). A multilayer perceptron consists of multiple hidden layers. The structure of a multilayer perceptron looks like this:

  1. An input layer: It receives the input data;
  2. Hidden layers: These layers process the data and extract patterns. We have two hidden layers in our model;
  3. Output layer: Produces the final prediction or classifications.

In general, each layer consists of multiple neurons, and the output from one layer becomes the input for the next layer.

Tarea

Set up the basic structure of the perceptron by implementing its layers.

  1. Create neurons and specify number of their inputs.
  2. Active neurons for forward propagation.
  3. Define three layers: 2 hidden layers and 1 output layer.

Switch to desktopCambia al escritorio para practicar en el mundo realContinúe desde donde se encuentra utilizando una de las siguientes opciones
¿Todo estuvo claro?

¿Cómo podemos mejorarlo?

¡Gracias por tus comentarios!

Perceptron is the name of the simplest neural network, consisting of only one hidden layer. However, in order to be able to solve more complex problems, we will create a variation of perceptron called multilayer perceptron (MLP). A multilayer perceptron consists of multiple hidden layers. The structure of a multilayer perceptron looks like this:

  1. An input layer: It receives the input data;
  2. Hidden layers: These layers process the data and extract patterns. We have two hidden layers in our model;
  3. Output layer: Produces the final prediction or classifications.

In general, each layer consists of multiple neurons, and the output from one layer becomes the input for the next layer.

Tarea

Set up the basic structure of the perceptron by implementing its layers.

  1. Create neurons and specify number of their inputs.
  2. Active neurons for forward propagation.
  3. Define three layers: 2 hidden layers and 1 output layer.

Switch to desktopCambia al escritorio para practicar en el mundo realContinúe desde donde se encuentra utilizando una de las siguientes opciones
Sección 2. Capítulo 2
Switch to desktopCambia al escritorio para practicar en el mundo realContinúe desde donde se encuentra utilizando una de las siguientes opciones
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