Deep Learning-What is it and why is it important? | Infogen Labs
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Deep learning is a sort of machine learning in which a computer is taught to perform human-like tasks such as speech recognition, image identification, and prediction. Deep learning sets up basic parameters about the data and educates the computer to learn on its own by spotting patterns utilizing several layers of processing, rather than structuring data to run through predefined equations.
The Evolution of Deep Learning:
Deep learning is one of the cornerstones of artificial intelligence (AI), and its present popularity stems in part from the hype surrounding AI. Deep learning approaches have enhanced the ability to classify, recognize, detect, and characterize – in a nutshell, to comprehend.
Deep learning is used to categorize photos, identify voice, detect objects, and describe information, for example. Deep learning is used in several systems, such as Siri and Cortana.
Deep learning is now being advanced by a number of developments:
Deep learning approaches have seen an increase in performance due to algorithmic advances. Model accuracy has improved thanks to new machine learning algorithms. New neural network classes have been developed that are ideally suited to applications such as text translation and image categorization.
We now have access to a lot more data, such as streaming data from the Internet of Things, textual data from social media, physicians' notes, and investigative transcripts, to develop neural networks with many deep layers.
The advancements in distributed cloud computing and graphics processing units have given us access to amazing computing capacity. Deep algorithms require this degree of processing power to be trained.
Human-to-machine interfaces, on the other hand, have progressed significantly. Gesture, swipe, touch, and natural language are replacing the mouse and keyboard, sparking increased interest in AI and deep learning.
Deep Learning Opportunities and Applications:
Because of the iterative nature of deep learning algorithms, their complexity as the number of layers increases, and the massive volumes of data required to train the networks, a lot of processing power is required to tackle deep learning problems.
Deep learning methods' dynamic character – their capacity to develop and react to changes in the underlying information pattern – offers a fantastic chance to bring more dynamic behavior into analytics.
One possibility is to personalize customer analytics more. Another excellent possibility is to increase accuracy and performance in applications that have long employed neural networks. We can add additional depth by using better algorithms and more computational power.
While deep learning techniques are now focused on cognitive computing applications, they also have a lot of potential in classic analytics applications like time series analysis.
Another option is to make existing analytical activities more efficient and simplified. SAS recently conducted research into deep neural networks for speech-to-text transcription. When deep neural networks were used instead of traditional approaches, the word-error rate was reduced by more than 10%. They also cut off roughly ten data pre-treatment, feature engineering, and modeling procedures. When compared to feature engineering, the significant performance advantages and time savings signal a paradigm change.
What are the applications of deep learning?
Deep learning may appear to be at a research phase to the untrained eye, as computer scientists and data scientists continue to explore its potential. Deep learning, on the other hand, has a wide range of practical applications that organizations are already utilizing and will continue to utilize as research progresses. Today's popular applications include:
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?Speech Recognition:
Deep learning for speech recognition has gained traction in both the industry and academic realms. To recognize human speech and voice patterns, Xbox, Skype, Google Now, and Apple's Siri?, to name a few, are already using deep learning algorithms in their systems.
Image Recognition:
Automatic image captioning and scene description are two practical applications of image recognition. This could be critical in law enforcement investigations to identify criminal activities among thousands of images taken by bystanders in a crowded place where a crime has occurred. The adoption of 360-degree camera technology in self-driving automobiles will also help picture recognition.
Natural Language Processing:
For many years, neural networks, a key component of deep learning, have been utilized to process and interpret textual text. This technique, which is a subset of text mining, can be used to find patterns in a variety of sources, including consumer complaints, physician notes, and news stories, to name a few.
Recommendation Systems
Amazon and Netflix popularised the idea of a recommendation system that can predict what you might be interested in next based on your previous activity. Deep learning may be used to improve suggestions across various platforms in complex situations like music tastes or apparel preferences.
How deep learning works?
Deep learning alters your perspective on how you describe the problems you're solving with analytics. It progresses from instructing the computer on how to solve a problem to actually teaching the machine how to solve the problem.
The typical method for analytics is to design characteristics from the data to extract new variables, then choose an analytic model and estimate the parameters (or unknowns) of that model. Because completeness and correctness are dependent on the quality of the model and its features, these strategies can result in predictive systems that do not generalize effectively. When you use feature engineering to create a fraud model, for example, you start with a set of variables and most likely build a model from those variables using data transformations. You may wind up with 30,000 variables on which your model is based, after which you must mould the model, determine which variables are meaningful and which are not, and so on. You'll have to start afresh if you want to add more data.
Deep learning's new strategy is to replace the model's formulation and specification with hierarchical characterizations (or layers) that learn to recognize latent data features from the regularities in the layers. With deep learning, the paradigm shift is from feature engineering to feature representation.
Deep learning holds the potential of producing prediction systems that generalize well, adapt effectively, improve continuously as new data comes, and are more dynamic than predictive systems based on hard business rules. You are no longer a model. Rather, you practice the task.
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