Convolutional of neurons where each neuron is fully

   ConvolutionalNeural NetworksIt is very similar toordinary neural networks. These are actually made of neurons which consists of learnable weights and biases and whereeach neuron get some inputs , performs a dot product operation of these inputsand conditionally follows it with non-linearity.This is usually explained in the architecture of this model where eachneuron when receiving inputs make it to transform through a series of hiddenlayers. Now each hidden layer consists of neurons where each neuron is fullyconnected to all the previous neurons and these neurons in a single layerfunction independently and thus making them not to share connections with others.The finally connected layer is the “output layer” and it represents classscores in classification system.

a single fully-connected neuron in a firsthidden layer of a regular Neural Network would have 32*32*3 = 3072 weightsThere are three main parameters that control the output volume of  the convolution layer. They are:1. Depth2. Stride3. Zero padding    {displaystyle W} {displaystyle K} {displaystyle S}{displaystyle P}{displaystyle(W-K+2P)/S+1}The main advantage of convolution neuralnetworks is the inputs are represented in a image format and this system is amore sensible way of neural networks.   The applications of convolution neuralnetworks are    1. Image recognition    2.

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Video analysis    3. Checkers    4. Go    5. Fine-tuning  DeepBelief Networks  They are generally a problem type generative models whichcontains many layers of hidden variables.

Now each layer is performing theoperation of capturing high order correlations between the hidden features inthe layer mentioned below in two characterstic points: 1. thetwo main top layers of the deep belief networks form a undirected bipartitiongraph which will result ina machine called Restriction Boltzman Machine. 2.Wheras the lower layers results in the directed sigmoid belief graph.  The boltzman machine is a representation of  network of symmetrically coupled random binary units denoted or having variables as{0,1}. The restricted boltzman machine is like a extension of  boltzman machine where the condition is nohidden to hidden and no visible to visible connections.

The top layer is a random binary hidden units hwheras the bottom layer is a vector of random binary visible variables w.The exact calculations of restricted boltzmanmachine is very difficult to find and conclude because of the expectationoperator in E_P MODEL .The training of deep belief learning is that ityields much better results by pre training each layer with a algorithm namedunsupervised algorithm which the superposition of one layer after another layerstarting mainly with the first layer always. After initializing a number oflayers, the whole neural networks can be fine tuned with respect to thesupervised training criterion.Global strategiesDeep learning provides two main improvementsover the traditional machines. They are:1.

They simply reduce the need for hand craftedand engineered feature set to be used exclusively for training purpose.  2.They increase the accuracy of the prediction modelfor larger amounts of dat3. Back-Propagation4. Now in today’s generation most of thecompanies making employed deep learning for various particular applications.

 Now someof the strategies that are applied in various big international companies arelisted below: 1. Facebook’sartificial intelligence lab adopted this deep learning strategy and performstasks such as automatically tagging uploaded pictures with the names of thespecified people in them.2. Google’s DeepMind Technologies developed anew system which is capable of learning how to play Atari video games whichuses the pixels as input data. And Google  translation system uses an LSTM method totranslate between more than 100 languages.3.

In 2015, a company named Blippardemonstrated a augmented reality version which uses deep learning methods andconcepts to identify objects in real time.Automotive Deep LearningIn deep learning there is a concept of automotiveuse cases which can be applied in automotive industry and it is listed below:  1. Visual inspection in manufacturing2.

Social media analytics 3. Autonomous driving 4. Robots and Smart machines5. Conversational user interface


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