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Deep Learning Approaches and its Evolution

Article contributed by Dr. Suresh Chandra Wariyal, Assistant Professor, Amrapali Institute of Technology and Sciences

The concept of Deep learning is unbeaten at dealing with different type of environment and tasks. This resource plays a vital role to accelerate the performance of deep learning. The utilization of hardware is the key aspect when number of application wants easy use of the resource. On the other way we can say that there are varieties of applications and these varieties of application may use different deep learning frameworks to utilize different amounts of resources.

Image recognition and speech recognition are the major aspect of deep learning and now the deep learning approach can be used for general purpose machine learning. The performance of machine learning is accelerated due to deep learning approach. However, it is not easy and not so cheap to deal with complex model and massive training data that occurred due to deep learning.

A number of online services nowadays rely upon machine learning to extract valuable information from data collected from various real time sources. There are different working areas of deep learning where it is implemented such as:-

  • The cultural evolution proposes a theory and experimental tests, relating difficulty of learning in deep architectures to culture and language.
  • Towards the Poisoning of Deep Learning Algorithms with Back-gradient Optimization.
  • Deep Learning Algorithm for Brain Tumor Detection and Analysis Using MR Brain Images which aims is to create deep learning algorithm to detect brain tumor using magnetic resonance brain images and analysis the performance of algorithm based on different values, accuracy, sensitivity, specificity.
  • Effective Media Traffic Classification Using Deep Learning in which Traffic classification (TC) is very important as it can provide useful information which can be used in the flexible management of the network.
  • SINGA: A Distributed Deep Learning Platform a distributed deep learning system, called SINGA, for training big models over large datasets.
  • The Business Impact of Deep Learning. This has broad implications for all organizations that rely on data analysis. It represents the latest development in a general trend towards more automated algorithms, and away from domain specific knowledge.
  • Medical image processing play great role in helping the radiologists and facility to patient’s diagnosis.
  • Deep neural networks (DNNs) are brain-inspired machine learning methods designed to recognize key patterns from raw data. State-of-the-art DNNs, even the simplest ones, require a huge amount of memory to store and retrieve data during computation.

The implication of this blog will explore the opportunity for various researcher to do their research on any area with the inference of deep learning.

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