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Deep learning vs. machine learning

The article also describes how deep learning can be applied to real-world scenarios such as fraud detection, voice and facial recognition, sentiment analytics, and time series forecasting.

By using machine learning and deep learning techniques, you can build computer systems and applications that do tasks that are commonly associated with human intelligence.

In machine learning, the algorithm needs to be told how to make an accurate prediction by consuming more information (for example, by performing feature extraction).

In deep learning, the algorithm can learn how to make an accurate prediction through its own data processing, thanks to the artificial neural network structure.

The following table compares the two techniques in more detail: Because of the artificial neural network structure, deep learning excels at identifying patterns in unstructured data such as images, sound, video, and text.

Usually, image captioning applications use convolutional neural networks to identify objects in an image and then use a recurrent neural network to turn the labels into consistent sentences.

Machine translation has been around for a long time, but deep learning achieves impressive results in two specific areas: automatic translation of text (and translation of speech to text) and automatic translation of images.

Machine translation can be used to identify snippets of sound in larger audio files and transcribe the spoken word or image as text.

Text analytics based on deep learning methods involves analyzing large quantities of text data (for example, medical documents or expenses receipts), recognizing patterns, and creating organized and concise information out of it.

Another common example is insurance fraud: text analytics has often been used to analyze large amounts of documents to recognize the chances of an insurance claim being fraud.

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