Deep

Learning

What is Deep Learning?

Deep learning is a set of machine learning algorithms that model high-level abstractions in data using architectures consisting of multiple non-linear transformations. Deep learning is used by a few companies like Google, IBM, Baidu, Microsoft, Twitter, Qubit, Intel, and Apple

Why Opt for Deep Learning with Trugo?

Lately, we have seen the development of concepts like deep learning and its usage by some large companies. Deep Learning can extract new functionality from limited sets of functionality located in the training dataset.

 

Our deep learning model performs repetitive tasks and thousands of routines in a relatively brief timeframe, in contrast with what it would take for a person to perform these tasks. Besides, the quality of the work degrades when there is raw data in the training data that does not represent the problem you are trying to solve.

We understand that labeling data can be time-consuming and expensive work; with our deep learning approach, our algorithm flourishes at learning without directives or well-labeled data.

Applications of Deep Learning

Mobile advertising

Finding the appropriate mobile audience for mobile advertising is continually put to the test, as many data points are required to be analyzed before a target segment is created and utilized in ad serving by any ad server. Deep learning has been used to decipher huge multi-dimensional advertising datasets. Tons of data are gathered during the request/serve/click internet advertising cycle. This information can shape the idea of machine learning to enhance ad selection.

Financial fraud detection

Deep learning is being successfully applied to anti-money laundering and financial fraud detection. A deep anti-money laundering detection system can spot and recognize relationships and similarities between data and ultimately learn to detect anomalies or classify and predict specific events. The answer leverages both supervised learning strategies like the classification of suspicious transactions and unsupervised learning like anomaly detection.

Customer relationship management

Deep reinforcement learning has been used to approximate the worth of possible marketing actions defined in terms of RFM variables. The estimated value function was shown to possess a natural interpretation as a customer lifetime value.

 

Benefits of Deep Learning

Creating New Features

One of the elemental advantages of deep learning over various machine learning algorithms is its capacity to supply new features from a restricted series of features located within the training dataset. Hence, deep learning calculations can make new tasks to tackle current ones. Since deep learning can produce features without a person's mediation, data scientists can spare tons of time working with big data and depend upon deep learning. It permits them to utilize more complex sets of features as compared to conventional machine learning programming.

Advanced Analysis

Because of its improved processing models, deep learning produces noteworthy outcomes when tackling data science tasks. While machine learning works only with labeled data, deep learning upholds solo learning methods that let the framework become brighter on its own. The power to make a decision the foremost significant feature permits deep learning to productively give data scientist’s brief and reliable analysis results.