Types of and Applications for Neural Nets - [Resource]
Jay Shah, a machine learning enthusiast, wrote a piece on Medium about the popular types and applications of neural networks. This is a good resource for beginners to read so that they can get a flavor of the field. At their company they apply neural nets for anomaly detection, time-series predictions, as well as natural language understanding.
In his own words, Shah explains neural nets in just a few lines:
"Essentially, neural networks are composed of layers of computational units called neurons, with connections in different layers. These networks transform data until they can classify it as an output. Each neuron multiplies an initial value by some weight, sums results with other values coming into the same neuron, adjusts the resulting number by the neuron’s bias, and then normalizes the output with an activation function." [source]
Shah also goes into the iterative process of learning, which is a key feature of the neural network; and he also discusses the challenges (for beginners and not only) present in learning neural networks.
The types of neural networks discussed are:
- autoencoders and deep belief networks
- convolutional neural networks - mostly used in image processing (object detection, recognition, etc)
- recurrent neural networks - often used on time series data
Shah also discusses how neural nets are applied in fields like marketing, retail and sales, banking, finance, and medicine. And finally, he recommends a few resources that beginners can use to deepen their knowledge and understanding of neural networks. Read it all here:
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Cristi Vlad Self-Experimenter and Author
This is a Great!
This Initiative is very helpful for beginners making them able to understands and learn the basics.
Thank you for sharing this informative applications with us.
Good stuff , like your post.