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    INTRODUCTION TO DEEP LEARNING

    Like there is Google translator app is able to translate the entire paragraphs from one language to another in milliseconds and how Netflix, youtube etc. catches our taste in movies, videos etc. with perfect recommendations and more examples for deep learning is like self driving car etc. so what is deep learning actually?

    Deep learning is subset of machine learning as well as subset of artificial intelligence. As artificial intelligence is general term that refers to techniques that enables computers to copy human behavior, whereas machine learning represents set of algorithms trained on data that make all of this possible and return an output based on series of input and deep learning is type of machine learning inspired by structured of human brain i.e. the algorithms in deep learning attempt to draw similar conclusion as a human would by constant analyzing data with a given logical structure to achieve deep learning uses multi-layered structure of algorithms called neural networks.

    • For example you want to build a machine that differentiate between cat and a dog, if you want to do it by machine learning, you then need to tell features based on which the two can be differentiated features by their sound or the type of clause they have but instead with deep learning, these features are picked out by neural network without human intervention.
    Why you need deep learning?

    In case of large amount of data deep learning is better than machine learning either data is structured or unstructured.

    • So limitation of huge amount of data is avoided by deep learning.
    • Complex Problems or real world problems- Machine learning is not able to handle real life complex problems but deep learning can easily solve the complex operations or problems
    • Feature extraction- In terms of machine learning if you have a model to which you want to train it to identify the object so in terms of machine learning you have to manually feed the algorithms in it and based on it only model will work as identifier.

    But in deep learning it generate the features by itself you just have to introduce the image of the object in algorithm and you no need to add the features manually.

    WORKING OF DEEP LEARNING?

    As there is a hypothetical airplane ticket price estimation tool. You will train it by using a supervised learning method and the price estimator will predict the price using following input- Origin airport, destination airport, and departure airport as well as airline.

    As Neural Network have three ingredients.

    First the input layer which receives the input in which there are four neurons in input layer, origin airport, destination airport, departure airport and airline airport. Input layer passes the inputs to first hidden layer in the network, the hidden layer is the one which performs mathematical computation on the inputs.

    • The challenge that creating neural network is deciding the number of hidden layers and also the number of neurons for each layer.
    • You should remember that the term deep in deep learning refers to having more than one hidden layer in it and lastly the output layer returns the output data which in case is the price prediction but you must be wondering how does it compute the price prediction.
    • As each connection between neurons is associated with the weight and this weight dictates the importance of input value when predicting the price of an airplane the departure date is one of the heaviest factors than others hence the departure date neuron connection will have the higher weight.
    • A single neuron in human brain receives thousands of signals from other neurons, similarly in Artificial neural networks signals travel between nodes and assign corresponding weights to next one. Heavier weighted node will exert more effect on the next layer of the nodes.

    • In the end final layer compiles the weighted inputs to produce an output.
    SCOPE OF DEEP LEARNING
    • DATA-:

      As training a deep learning model requires huge chunks of data set to make it decently accurate.

    • COMPUTATIONAL POWER-:

      Training a deep learning system requires a high amount of computation, that’s why you generally employ using graphical processing unit that have more core than cpu and also carries a higher cost.

    • TRAINING TIME-:

      Training an average deep learning system can take weeks or even months to process. Training time is usually dependent on the amount of data and the number of layers in hidden network.

    • It can use in medical field, in case of Robotics, self-driving cars as well as translation.


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