Up to Functional and Sequential Models
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Luis Fernando Molina committed
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@@ -93,6 +93,14 @@
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||||
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||||
"type": "Artificial Neural Network",
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||||
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|
||||
"text": "An ANN is based on a collection of connected units or nodes called artificial neurons, which loosely model the neurons in a biological brain. Each connection, like the synapses in a biological brain, can transmit a signal to other neurons. An artificial neuron that receives a signal then processes it and can signal neurons connected to it.",
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||||
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||||
"text": "The \"signal\" at a connection is a real number, and the output of each neuron is computed by some non-linear function of the sum of its inputs. The connections are called edges. Neurons and edges typically have a weight that adjusts as learning proceeds. The weight increases or decreases the strength of the signal at a connection. Neurons may have a threshold such that a signal is sent only if the aggregate signal crosses that threshold. Typically, neurons are aggregated into layers. Different layers may perform different transformations on their inputs. Signals travel from the first layer (the input layer), to the last layer (the output layer), possibly after traversing the layers multiple times.",
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||||
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||||
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||||
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||||
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||||
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|
||||
"text": "The key difference between a Functional and Secuential model is that Functional Models allows you to create an arbitrary graph of layers, as long as they don't have cycles.",
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||||
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||||
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||||
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||||
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|
||||
"text": "In machine learning, a model is a function with learnable parameters that maps an input to an output. The optimal parameters are obtained by training the model on data. A well-trained model will provide an accurate mapping from the input to the desired output.",
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||||
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||||
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|
||||
"text": "using the Core API with lower-level ops such as tf.matMul(), tf.add(), etc."
|
||||
}
|
||||
]
|
||||
}
|
||||
@@ -0,0 +1,13 @@
|
||||
{
|
||||
"type": "Secuential Model",
|
||||
"definition": {
|
||||
"text": "The most common type of model is the Sequential model, which is a linear stack of layers. ",
|
||||
"updated": 1613201804709
|
||||
},
|
||||
"paragraphs": [
|
||||
{
|
||||
"style": "Text",
|
||||
"text": "Left click and select the pencil button to enter edit mode."
|
||||
}
|
||||
]
|
||||
}
|
||||
@@ -0,0 +1,13 @@
|
||||
{
|
||||
"type": "Self Learning",
|
||||
"definition": {
|
||||
"text": "Self-learning as a machine learning paradigm was introduced in 1982 along with a neural network capable of self-learning named crossbar adaptive array (CAA). It is a learning with no external rewards and no external teacher advice. The CAA self-learning algorithm computes, in a crossbar fashion, both decisions about actions and emotions (feelings) about consequence situations. The system is driven by the interaction between cognition and emotion.",
|
||||
"updated": 1613199403151
|
||||
},
|
||||
"paragraphs": [
|
||||
{
|
||||
"style": "Text",
|
||||
"text": "Left click and select the pencil button to enter edit mode."
|
||||
}
|
||||
]
|
||||
}
|
||||
Reference in new issue
Block a user