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Top Deep Learning Algorithms You Should Know About | Deep Learning Algorithms Explained |Simplilearn

Deep Learning algorithms use artificial neural networks to solve complex problems. This video covers the top 10 deep learning algorithms used in the industries. You will learn how these algorithms work and where they can be used. You will also look at the overall view of these neural network structures. Finally, we'll tell you how Simplilearn can help you start your career in Deep Learning.<br>

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Top Deep Learning Algorithms You Should Know About | Deep Learning Algorithms Explained |Simplilearn

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  1. Top 10 Deep Learning Algorithms

  2. Autoencoders 10 An autoencoder is a trained neural network that replicates the data from the input layer to the output layer Encoder Decoder

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  4. Restricted Boltzmann Machine 9 Restricted Boltzmann Machine (RBM) is a neural network that can learn from a probability distribution over a set of inputs. It has 2 layers Hidden units Activation function +b Visible units = a = a +b = a +b +b = a Wi…..Wn

  5. Deep Belief Networks 8 Deep Belief Networks (DBN) are a stack of Boltzmann Machines with connections between the layers and each RBM layer communicates with both the previous and subsequent layers …. Input Layer RBM1 …. Hidden Layer 1 RBM2 …. Hidden Layer 2 RBM3 …. Hidden Layer 3 …. Output Layer

  6. Self Organizing Map 7 Invented by Professor TeuvoKohonen, Self Organizing Maps (SOMs) are a data visualization technique to reduce the dimensions of data through self-organizing neural networks Input layer Computational layer Input data Fed to the SOM network

  7. Self Organizing Map 7 Invented by Professor TeuvoKohonen, Self Organizing Maps (SOMs) are a data visualization technique to reduce the dimensions of data through self-organizing neural networks RGB RGB RGB RGB RGB RGB RGB RGB Input data SOM converts the data into 2D RGB values

  8. Self Organizing Map 7 Invented by Professor TeuvoKohonen, Self Organizing Maps (SOMs) are a data visualization technique to reduce the dimensions of data through self-organizing neural networks RGB RGB RGB RGB RGB RGB RGB RGB Segregates and categorizes the different colors Input data SOM converts the data into 2D RGB values

  9. Multilayer Perceptron 6 Multilayer Perceptron (MLP) is a general feedforward neural network that consists of multiple layers of perceptron with activation functions CATS DOGS Output Layer Input Layer Hidden Layers

  10. Radial Basis Function Network 5 Radial Basis Function Networks (RBFNs) is a special type of feedforward neural networks with radial basis functions used as activation functions x1 Weights Y1 x2 Y2 xn Weighted sums Input Vector Input Layer Hidden Layer

  11. Generative Adversarial Network 4 Generative Adversarial Network (GAN) are generative models that create new data instances that resemble the training data Real Example Update model Discriminator Model Binary Classification Real/Fake Random Input Vector Generator Model Generated Example Update model

  12. Recurrent Neural Network 3 Recurrent Neural Network (RNN) have connections that form directed cycles and allows the outputs from the previous step to be fed as inputs to the current step Output at time t Hidden state at time t yt+1 yt-1 yt y ht ht-1 ht+1 Unfold w w w w h w xt xt-1 xt+1 x Input at time t

  13. Recurrent Neural Network 3 Recurrent Neural Network (RNN) have connections that form directed cycles and allows the outputs from the previous step to be fed as inputs to the current step y y y A A A Sheets How to copy a cell in Google C C C h h h Autocompletes the search Collection of vast volumes of most frequently occurring consecutive words B B B x x x Fed to the network Google search

  14. Long Short Term Memory Network 2 Long Short Term Memory Network (LSTM) are a type of recurrent neural networks capable of learning and memorizing long term dependencies ht+1 ht-1 ht x x x + + + ft ~ tanh tanh tanh ot it Ct x x x x x x tanh tanh tanh Xt Xt-1 Xt+1

  15. Convolutional Neural Network 1 Convolutional Neural Network (CNN), commonly known as ConvNets consist of multiple different layers and are mainly used image processing and identification

  16. Convolutional Neural Network 1 Convolutional Neural Network (CNN), commonly known as ConvNets consist of multiple different layers and are mainly used image processing and identification Dog Bird Fully Connected Layer Convolution + ReLU + Max Pooling Classification in the output layer Feature Extraction in multiple hidden layers

  17. How can Simplilearn help? How can Simplilearn help?

  18. Join us to learn more! simplilearn.com UNITED STATES Simplilearn Solutions Pvt. Limited 201 Spear Street, Suite 1100 San Francisco, CA 94105 Phone: (415) 741-3319 INDIA Simplilearn Solutions Pvt. Limited #53/1C, 24th Main, 2nd Sector HSR Layout, Bangalore 560102 Phone: +91 8069999471 UNITED STATES Simplilearn Solutions Pvt. Limited 801 Corporate Center Drive, Suite 138 Raleigh, NC 27607 Phone: (919) 205-5565

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