Machine Learning and Deep Learning Applications: A Study

Artificial Intelligence (AI) is the process of making robots clever in the same way that the human brain is. In computer engineering, AI refers to the study of “expert systems,” which are devices that sense their surroundings and take activities to increase their chances of attaining their objectives. When a device can execute actions that humans identify with other human minds, like “learning” and “problem-solving,” it is referred to as “artificial intelligence.” Learning is an essential feature of machines. As a result, machine learning is a branch of AI. As a result, machines are held to higher standards. Deep learning is an example of this approach. Machine learning is a subfield of it.

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Applications of Machine Learning

Various applications of ML are — Computer vision, forecasting, text analytics, natural language processing, and information extraction are some of the various application domains.

1. Computer Vision: The vision-based domain includes sub-domains such as object classification, object recognition, and object processing.
2. Classification, evaluation, and suggestion are the several sub-domains of prediction. Machine learning has been used to effectively manage text classification, text classification, image recognition, clinical diagnosis, network intrusion detection prediction, and denial of service threat prognostication.
3. Semantic research is the procedure of linking compound sentences from paragraphs, phrases, and individuals to the quality of writing overall. How to train computers to accurately analyze natural language data is known as natural language processing. The study of looking for information in a document, exploring for documents, exploring for metadata that offers information, and researching for databases of sights and sounds is known as information recovery.

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Applications of Deep Learning

Machine learning includes deep learning as a subset. It’s a large-scale neural network with a lot of layers and parameters. Neural network designs are used in the majority of deep learning approaches. As a result, it’s also known as deep neural networks. In the examples underneath, a few of the most advanced deep learning application breakthroughs are discussed:

1. Microsoft speech recognition is an illustration of a deep learning implementation in Big Data (MAVIS). Deep learning allows audio/video data to be searched using human speech and utterances.
2. Google’s picture search service employs deep learning in a Big Data environment. They employed deep learning to comprehend images to use it for image annotation and tagging, which is important in image search engines, image retrieval, and image indexing.
3. In a Go contest in 2016, Google’s AlphaGo algorithm overcame Lee Sedol, demonstrating that deep learning has remarkable cognitive abilities.
4. Deep Dream is a technology developed by Google that can analyze photos and create odd and artificial artworks based on its own knowledge.

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Data dependencies, GPU hardware, and feature engineering are the main reasons deep learning is appropriate for expanding applications. The term “data dependencies” refers to deep learning techniques that perform effectively with large amounts of data. GPU refers to Graphics Processing Unit and is a slightly elevated processor.

Compared to machine learning, the capacity to learn high-level features from data, known as feature engineering, sets deep learning apart. As a result, many more deep learning applications could emerge in the next years.

Conclusion

With this examination of applications, we may now investigate any of the newest areas of deep learning application that will produce better results and contribute to the current study in this field. Because deep learning research is still in its early stages, there is even room for new architectures to emerge. Aside from that, improvements can be made in the sub-domains of analysis and prediction.