Deep learning

Deep learning technology borrows  from neural structure of brain. It grew in  last decade.  With growing fascination for data science and artificial intelligence  , deep learning is a branch of machine learning  to solve complex problems.  It was first conceived after google cloud suite was born.  Deep learning is a positive sign towards more data integrity and abstraction. Google brain is a project which  deals with a machine level abstraction .

Neural network

Supervised learning and unsupervised learning is a  branch of science that learns from past mistakes. Backward ward propagation in neural network  aids in machine leaning. It is based on the fact that learning is hierarchical meaning each layer’s learnings   is achieved by   auto correction  .   Neural network  has  many hidden layers and are  subjected to weight correction. Deep learning has depth being added at very level.  In image processing every depth gets rendered . Rina Detcher who first brought deep learning to public notice.


Deep learning has a variety of applications across platforms. Automatic speech recognition is most sought-after deep learning application. It renders voice to audio files.  With advent of character recognition and image processing  deep learning  finds  a place in biometrics. Neural language development is the  branch  that  deals with  human speech. Deep learning  has contributed in human speed recognition. Minor areas like bioinformatics and image restoration  has grown because  of deep learning.


Deep learning is also susceptible to cyber-attacks.  Cyber-attacks  distorts deep learning algorithms .   Information is extracted for personal use. There is also threat of data poisoning . Deep learning is  dependent on algorithms . Platform scalability and platform independent has made deep learning   todays feat .  Convolutional algorithm and recurrent algorithm are  important for ultimate implementation for projects.

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