Tuesday, 23 May 2017

Before ABC of Deep Learning.



Mark Cuban has rightly said above lines for deep learning.First of all, when was it coined first time? Deep Learning is used by Google in its voice and image recognition algorithms, Netflix and Amazon say that they have used in their recommendation engine, researchers at MIT say they are relying more on deep learning now.



According to Jack Rae; Google DeepMind Research Engineer, Deep learning refers to artificial neural networks that are composed of many layers. Essentially Deep Learning involves feeding a computer system a lot of data, which it can use to make decisions about other data. This data is fed through neural networks, as is the case in machine learning. These networks – logical constructions which ask a series of binary true/false questions, or extract a numerical value, of every bit of data which pass through them, and classify it according to the answers received. If we define in language of data scientists, It uses a cascade of many layers of nonlinear processing units for feature extraction and transformation. It is based on the (unsupervised) learning of multiple levels of features or representations of the data and learns multiple levels of representations that correspond to different levels of abstraction. 

According to Jack Rae; Google DeepMind Research Engineer, Deep learning refers to artificial neural networks that are composed of many layers. Essentially Deep Learning involves feeding a computer system a lot of data, which it can use to make decisions about other data. This data is fed through neural networks, as is the case in machine learning. These networks – logical constructions which ask a series of binary true/false questions, or extract a numerical value, of every bit of data which pass through them, and classify it according to the answers received. If we define in language of data scientists, It uses a cascade of many layers of nonlinear processing units for feature extraction and transformation. It is based on the (unsupervised) learning of multiple levels of features or representations of the data and learns multiple levels of representations that correspond to different levels of abstraction. 


Deep learning methods are often looked at as a black box, with most confirmations done empirically, rather than theoretically.

Deep learning is used across all industries for a number of different tasks. Commercial apps that use image recognition, open source platforms with consumer recommendation apps, navigation of self-driving cars, re-colouring black and white images, automated analysis and reporting and medical research tools that explore the possibility of reusing drugs for new ailments are the examples of deep learning.

There are many available software libraries for deep learning-

Deeplearning4j - Written in Java and algorithms can be integrated with Hadoop, Spark. It was developed mainly by a machine learning group in San Francisco led by Adam Gibson.

Torch - Open Source and written in Lua language. It is presently used by Facebook, IBM and Yandex.

Theano- Open source library for python and developed by Université de Montréal.

TensorFlow - Developed by Google Brain team and used by google in their products. It is Google's second generation machine learning system.

PaddlePaddle - Baidu's deep learning platform.

Keras- It is an open source neural network library written in Python. It is capable of running on top of Deeplearning4jTensorflow or Theano.

CNTK - It is deep learning framework developed by Microsoft Research. Also know as Microsoft's Cognitive tool-kit.


Another article on advance- machine learning by Russian Andrey Markov-

http://machinelearningstories.blogspot.in/2017/02/hidden-markov-model-session-1.html

start you first feed-forward newral network from here-




No comments:

Post a Comment