# AI News, Guide to Deep Learning

- On Wednesday, October 17, 2018
- By Read More

## Guide to Deep Learning

Up till recent past, the artificial intelligence portion of data science was looked upon cautiously due to its history of booms and flops.[1] In the latest stream of events, major improvements have taken place in this field and now deep learning, the new leading front for Artificial Intelligence, presents promising prospect for overcoming problems of big data.

Deep learning has various architectures such as deep neural networks, deep belief networks, Deep Boltzmann machines and so on that are able to handle and decode complex structures that have multiple non-linear features.[3] Deep learning offers us considerable insight into the relatively unknown unstructured data which is 80% of the data that we generate as per IBM.[4] While traditional data analysis before 2005 focused on just the tip of the iceberg, the big data revolution sprang up and now deep learning offers us a better glimpse into the unconscious segment of data that we know exists, but is constrained in realizing its true potential.

Deep learning helps us in both exploring the data and identifying connections in descriptive analytics for ratemaking but these connections also help us in price forecasting what the result will likely be, given the particular combination as the machine learns from the data.

Moreover, the unique feature of deep learning is that it allows individual parts of the model to be trained independently of the other parts.[6] Deep learning models can recognize human faces with over 97% accuracy, as well as recognize arbitrary images and even moving videos.

It is becoming increasingly established that deep learning can perform exceptionally well on problems involving perceptual data like speech recognition image classification and text analytics.[7] In a single formula, this is the formula for neural networks (for hyperbolic tangent activation function)[8]

3) Spectral networks 4) noBackTrack algorithm to solve the online training of RNN (recurrent neural networks) problem 5) Neural reasoner 6) Reccurrent Neural Networks 7) Long short term memory 8) Hidden Markov Models 9) Deep belief network 10) Convolutional deep networks 11) LAMSTAR are increasingly being used in medical and financial applications.

- On Sunday, January 20, 2019

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