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Update index.md with grammatical, spelling fixes (#28874)

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2019-02-23 23:04:40 -08:00
committed by Randell Dawson
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guide/english/machine-learning/neural-networks/index.md
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@@ -13,9 +13,11 @@ The basic principle which underlies the remarkable success of neural networks is
Neural networks initially became popular in the 1980s, but limitations in computational power prohibited their widespread acceptance until the past decade.
Innovations in CPU size and power allow for neural network implementation at scale, though other machine learning paradigms still outrank neural networks in terms of efficiency.
The most basic element of a neural network is a neuron. Its input is a vector, say `x`, and its output is a real valued variable, say `y`. Thus, we can conclude that the neuron acts as a mapping between the vector `x` and a real number `y`.
Neural networks perform regression iteratively across multiple layers, resulting in a more nuanced prediction model.
A single node in a neural network computes the exact same function as [logistic regression](../logistic-regression/index.md).
All these layers, aside from the input and output, are hidden, that is, the specific traits represented by these layers are not chosen or modified by the programmer.
All these layers, aside from the input and output, are hidden. That is, the specific traits represented by these layers are not chosen or modified by the programmer.
![Four Layered Neural Network](http://cs231n.github.io/assets/nn1/neural_net2.jpeg)
@@ -23,11 +25,11 @@ In any given layer, each node takes all values stored in the previous layer as i
The power of neural networks lies in their ability to "discover" patterns and traits unseen by programmers.
As mentioned earlier, the middle layers are "hidden," meaning the weights given to the transitions is determined exclusively by the training of the algorithm.
Neural networks are used on a variety of tasks. These include computer vision, speech recognition, translation, social network filtering, playing video games, and medical diagnosis among other things.
Neural networks are used on a variety of tasks. These can include but are not limited to: computer vision, speech recognition, translation, social network filtering, playing video games, and medical diagnosis.
### Visualization
There's an awesome tool to help you grasp the idea of neural networks without any hard math: <a href='http://playground.tensorflow.org' target='_blank' rel='nofollow'>TensorFlow Playground</a>, a web app that lets you play with a real neural network running in your browser and click buttons and tweak parameters to see how it works.
There's an awesome tool to help you grasp the idea of neural networks without any hard math: <a href='http://playground.tensorflow.org' target='_blank' rel='nofollow'>TensorFlow Playground</a>, a web app that lets you play with a real neural network running in your browser, click buttons and tweak parameters to see how it works.
### Problems solved using Neural Networks
- Classification