Deep Learning with python

                                                 Deep Learning

Why we are Choosing  Python for Deep Learning?

Python is a widely used high-level programming language for general-purpose programming. Apart from being open source programming language, python is a great object-oriented, interpreted, and interactive programming language. Python combines remarkable power with very clear syntax. It has modules, classes, exceptions, very high level dynamic data types, and dynamic typing. There are interfaces to many system calls and libraries, as well as to various windowing systems.  Python is also usable as an extension language for applications written in other languages that need easy-to-use scripting or automation interfaces.


What is Deep Learning?

Deep structured learning or hierarchical learning or deep learning in short is part of the family of machine learning methods which are themselves a subset of the broader field of Artificial Intelligence.
Deep learning is a class of machine learning algorithms that use several layers of nonlinear processing units for feature extraction and transformation. Each successive layer uses the output from the previous layer as input.

  



Why do we need Deep Learning


  • Process huge amount of data : Deep Learning Algorithms work with enormous amount of structured and unstructured data.
  • Perform Complex algorithms : Machine Learning Algorithms cannot perform complex operations, to do that we need Deep Learning Algorithms. 
  • To achieve the best performance with large amount of data : As the amount of data increases, the performance of Machine Learning Algorithms decreases, to make sure the performance of a model is good, we need Deep Learning .
  • Feature Extractions: while Deep Learning Algorithms take large volumes of data as input, analyze the input, analyze the input to extract features out of an object and identifies similar objects.


Application of Deep Learning

Deep neural networks, deep belief networks and recurrent neural networks have been applied to fields such as computer vision, speech recognition, natural language processing, audio recognition, social network filtering, machine translation, and bioinformatics where they produced results comparable to and in some cases better than human experts have.
Deep learning has produced good results for a few applications such as computer vision, language translation, image captioning, audio transcription, molecular biology, speech recognition, natural language processing, self-driving cars, brain tumour detection, real-time speech translation, music composition, automatic game playing and so on.
Deep Learning Algorithms and Networks −
  •  based on the unsupervised learning of multiple levels of features or representations of the data. Higher-level features are derived from lower level features to form a hierarchical representation.
  • use some form of gradient descent for training.


Let us now learn about the different deep learning models/ algorithms.Some of the popular models within deep learning are as follows −
  • Convolutional neural networks
  • Recurrent neural networks
  • Deep belief networks
  • Generative adversarial networks
  • Auto-encoders and so on



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