The concept of Artificial Learning originated with very narrow notions originally which were very limited in their applicability. With the passage of time and the continuous developments in the field, this concept has becomeinfinitely broad in its scope and applications. Artificial Intelligence has given birth to Machine Learning and Machine Learning has been further developed to arrive at the recent concept of deep learning.

Deep Learning can be rightly said to be the grandchild of Artificial Learning. It is a subset of Machine Learning which is extensively based on Artificial Neural Networks (ANN). Its function is meant to imitate the functioning of the human brain while processing data, analyzing the inputs and creating structured outputs for use in decision making.

Dilemma of Difference- Deep Learning vs. Machine Learning

People often consider Deep Learning and Machine Learning as one and the same thing. But this is not the case. Infact, it is just a part of a broader family of machine learning methods based on artificial neural networks. Deep Learning is different from Machine Learning in the sense that machine learning almost always requires structured data to process and analyze, whereas Deep Learning relies on Artificial Neural Networks and uses the layers of networks to process the data.

Deep Learning Applications at a Glance

• Computer Vision – This machine learning method assists the interdisciplinary scientific field of Computer Vision is helping computers gain high-level understanding through the use of images and videos. For example, object detection and video tracking.
• Speech Recognition –– Translation of spoken words into text is a challenging task but with the advent of era of this method, the effectiveness of speech recognition programs has increased manifold. Programs like Google Assistant and Siri use speech recognition to translate our speech into commands which are then completed automatically by the system.
• Natural Language Processing – It involves the use of deep learning to improve the interaction between computers and human (natural) languages through speech recognition, speech segmentation, text to speech, etc. Automatic machine translation is done through natural language processing only.

Some of the recent applications that are commonly used around us include:

• Automatic colorization of black and white images
• Predicting soundtrack for silent movies
• Object classification and detection in photographs
• Automatic image caption generation

Advantages of Deep Learning

• Cost and time – Deep learning mechanisms can effectively replace humans in doing several tasks which reduces the cost of hiring employees. Moreover, the faster speed of processing data can save some valuable time.
• Creating new features – It has the ability to generate new features from a small set of features by creating new tasks to solve current ones. This ability allows for the use of complex features by data scientists in a reliable manner.
• Unsupervised learning – Deep learning algorithms allows the system to learn and become smarter on its own without the need of constant supervision. This reduces human efforts and also helps the system to become more efficient.

The technological developments of the modern world are revolving extensively around the deep learning algorithms. It is working as a mechanism that is giving a major boost to AI technologies around the globe. With the advent of deep learning, we can finally look forward to building a smart world of smart technology through the use of Artificial Intelligence. This methodology will play an essential role in the future to simplify and enrich the experience of automatic systems and technologies available in every sphere of our lives.

At IDS Software Solutions we aim to deliver end to end software solutions in technologies like Deep Learning, Machine learning and AI , providing a fusion of business and technological expertise.

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