Dave Waters says “What’s behind the driver-less cars? Artificial Intelligence or more specifically Deep Learning.” Deep learning is one of the most interesting topics under Machine Learning. With a huge count of applications under its umbrella, Deep Learning has become the most used machine learning field. Deep learning is also termed as the heart of Artificial Intelligence, which in itself offers huge research options among the computer science scholars.
Deep Learning enables the machines to interact and ‘learn’ like humans do. Deep learning is carried out with the generation of appropriate algorithms. More innovation means more and more sophisticated algorithms that ultimately lead to more efficient computing models. It is generally used to identify patterns in unstructured data.
To understand Deep Learning we have to sit in a time machine and travel way back to the time we were in school. We can straight land in that biology lecture when we tried to learn about the concepts of our nervous system. Our nervous system contains neurons. These neurons communicate and transfer information with the help of stimuli. Deep Learning is the concept based on the stimulus of the neurons.
The way neurons communicate with each other and the brain is used to define networks the same relationships and models are established with Deep Learning in the fields such as Natural Language Processing, Bioinformatics, speech recognition, drug recovery and much more. This article might seem approachable but in reality it is way obscure and complicated field in the computing industry. This article only discusses the basics of deep learning.
Learning Deep Learning
The main reason for using deep learning approach in machine learning is that it is an attempt to create models from large scale data with the help of multiple layered neural networks. The basic process of deep learning can be described as follows:
- Identifying and analyzing appropriate data and checking whether it is good to use Deep Learning.
- Choosing the deep learning type
- Using neural networks/machine learning algorithms on the data set
- Testing
Deep Learning Applications
Learning Deep Learning can be quite tedious to implement and apply. It is difficult to understand so needs proper knowledge of the field in which it is applied. Its applications are spread over a variety of industries and trades.
Predictive analysis
From companies like Netflix to Yelp all apply machine learning (deep learning techniques) to predict data about their users and services. This can be done by analyzing the datasets to create digital images. ANNs or Artificial Neural Networks are the most accurate models for predicting data. The whole process includes creating datasets, analyzing data, developing model relationships and visualizing. This particular application has a big influence on big data and those who want to work in this field need a deep knowledge of data analysis to follow this application.
Text fraud and Thread detection
A neural network can be used to identify and visualize an anomaly in a text data. That anomaly is further used for analytics and deep learning algorithms are applied. Analytics and data science play an important role in the protection. A dataset is chosen first. Many variables are used to find out the fraudulent activities in real time analysis that help in reducing steps and improving the speed of the action. It is a whole combo of correct algorithms and effective analysis. Deep learning security measures have dominated the market lately, improving the algorithms.
Image clustering and Facial Recognition
This is one of the most challenging applications of deep learning. Facial recognition works on the principal of biometrics wherein it makes the facial recognition. ANN architecture is used to train models based on clustering of images. This is a newer application of deep learning which is being used in social media websites like Facebook etc. This uses unique features which are picked and recognized for further steps. The final step in this application is recognition.
Healthcare
It is a vast area that is still to be researched for application of Deep Learning. The medical field is known for its non-competitive vastness. This has resulted in many Deep Learning applications in this industry. Ranging from differentiating between malignant and normal cells to medical diagnosis, all is based on the new technique of Deep Learning. Algorithms are being developed and models are being created to overcome the common diseases of today’s lifestyle.
Voice recognition and search
One of the most used and vital applications of this technology is voice recognition. This is done by following these steps: The first is to convert sounds into bits. Then we have the data sampling and converting it into data sets. Next is to analyze it and process it. Then we recognize characters from the sampled voice. This is handled using neural networks. This has been dealt with many problems like not recognizing the voices, background noise, and other issues. As voice recognition and searching are used in daily life its algorithms also become more efficient. We are still far from creating a perfect voice recognition device. Deep Learning promises a great future in the voice recognition.
Conclusion
We have come a long way since deep learning’s first successful impact in the year 2006. Deep Learning has rather introduced a newer concept of Artificial Learning and has become one of the largest topics of discussion today. Deep learning is capable of multiple tasks and features as it advances to a different and important aspect of the modern computing world. On the other hand, the neural networks are still vulnerable to cyber attacks and hacking. Henceforth we have created some efficient deep learning mechanisms but we are still very far away to keep it fully secure and efficient.
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