Record Detail Back
Deep learning innovations and their convergence with big data
- Share to:
n recent years, there’s been a resurgence in the field of Artificial Intelligence and deep learning is gaining a lot of attention. Deep learning is a branch of machine learning based on a set of algorithms that can be used to model high-level abstractions in data by using multiple processing layers with complex structures, or otherwise composed of multiple non-linear transformations. Estimation of depth in a Neural. Network (NN) or Artificial Neural Network (ANN) is an integral as well as complicated process. These methods have dramatically improved the state-of-the-art in speech recognition, visual object recognition, object detection and many other domains such as drug discovery and genomics. This chapter describes the motivations for deep architecture, problem with large networks, the need for deep architecture
and new implementation techniques for deep learning. At the end, there is also an
algorithm to implement the deep architecture using the recursive nature of functions and transforming them to get the desired output.
006.31 KAR d
006.31
Advances in Data Mining and Database Management (ADMDM) Book Series
Text
ENGLISH
IGI Global
2018
New York
LOADING LIST...
LOADING LIST...
APA Citation
Karthik, S. (2018).Deep learning innovations and their convergence with big data.(Electronic Thesis or Dissertation). Retrieved from https://localhost/etd