Improvements In Computing Power Surge The Demand For Deep Learning Technology

In recent years, digital transformation has gained momentum and businesses are moving toward digital technologies to enhance visibility and eliminate inefficiencies in their operations. Digitalization provides all the integrated features necessary to collect, store and analyze commercial data. This data is stored in a secure, digital location, where they’re accessible anywhere, anytime from an internet-connected device.

To make this process of data collection, analyzing, and interpreting large amounts of data faster and easier deep learning methods are used. Deep Learning technology is an important element of data science, which includes statistics and predictive modeling. It is utilized in several data-driven industries globally, and with this, the global deep-learning market is expected to grow exponentially.

What Is Deep Learning?

Deep Learning is defined as a subfield of machine learning associated with algorithms inspired by the structure and function of the brain called artificial neural networks. It helps to eliminate some of the data pre-processing that is typically involved with machine learning. Deep learning algorithms can ingest and process unstructured data such as text and images.

It automates feature extraction by removing some of the dependency on human experts. Deep learning can be considered a way to automate predictive analytics. In this technology data requires passing through a number of processing layers, this is what inspired the label ‘Deep’. One of the major advantages of deep learning is the program builds the feature set by itself without any supervision.

Deep learning algorithms are stacked in a hierarchy, each of these algorithms in the hierarchy applies a nonlinear transformation to its input and uses what it learns to create a statistical model as output. It performs continuous Iterations until the output has reached an acceptable level of data accuracy. As deep learning is an unsupervised learning methodology, it is faster and more accurate.

Deep Learning Vs Machine Learning

Deep learning is a subset of machine learning that differentiates itself through the way it solves data processing issues. Machine learning technology requires a domain expert to identify the most applied features. Whereas, deep learning understands features incrementally and eliminates the need for domain expertise. In machine learning, test time increases along with the size of the data.

On the other hand, Deep learning algorithms take much less time to run tests. Generally, deep learning is preferable in situations where there is a large amount of data, a lack of domain understanding for feature introspection, or complex problems, and machine learning algorithms are preferred when the data is small.

Deep Learning Methods

Deep learning models are created by using various techniques such as learning rate decay, transfer learning, training from scratch, and drop out. These methods are discussed below.

1. Learning Rate Decay

It is simply a factor that defines the system or sets conditions for its operation prior to the learning process. The learning rate is a hyper parameter that controls how much change the model experiences in response to the estimated error every time the model weights are altered. Learning rates highly impact the training processes of data. The learning rate decay method is the process of adapting the learning rate to increase performance and reduce training time.

2. Transfer Learning

This method involves perfecting a previously trained model; it requires an interface to the internals of a preexisting network. Transfer learning has the advantage of requiring much fewer data than others, which reduces computation time to minutes or hours.

3. Training from Scratch

This process requires a developer to collect a large labeled data set and configure a network architecture that can learn the features and model. Training from scratch technology is especially useful for new applications, as well as applications with a large number of output categories.

4. Dropout

The dropout technique attempts to solve the problem of overfitting in networks with large amounts of parameters by randomly dropping units and their connections from the neural network during training. This method can improve the performance of neural networks on supervised learning tasks in areas such as speech recognition, document classification, and computational biology.

Conclusion

Deep learning technology is used in all types of big data analytics applications including language translation, medical diagnosis, stock market trading signals, network security, and image recognition. IT companies are focused on advancing this technology and providing improved data learning algorithms that can be used in several industries globally.