PART- 2 :ARTIFICIAL INTELLIGENCE & DEEP LEARNING
11. A Brief Introduction to Deep Learing and TensorFlow
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Deep Learning at a glance
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Artificial Neural Networks (ANN)
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Deep Learning Architectures
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Fully connected Layers
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Convolutional Layers
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Dropout Layers
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A brief introduction of Tensorflow
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A brief introduction of Keras
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From Biological to artificial neurons
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Biological Neurons
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Logical computations with neurons
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The perceptron
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Multi-Layer perceptron and Backpropagation
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Training DNN using plain TensorFlow
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Construction Phase
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Esecution Phase
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Using the neural network
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Fine-tuning neural network hyperparameter
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Number of hidden layers
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Number of neurons per hidden layer
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Activation Functions
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12. Convolutional Neural Networks (CNN) / Computer vision
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Architecture of the visual cortex
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Convolution Layer
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Filters
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Stacking Multiple feature maps
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TensorflowImplemention
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Memory Requirement
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Pooling Layer
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CNN Architectures
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LaNet – 5
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AlexNet
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GoogleNet
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ResNet
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13. Recurrent Neural Network (RNN)
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Recurrent Neurons
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Memory cells
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Input and output sequences
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Basic RNNs TensorFlow
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Static unrolling through time
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Dynamic unrolling through time
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Handling variable length input sequences
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Handling variable length output sequences
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Training RNNs
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Training a sequence classifier
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Training to predict time series
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Creative RNN
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Distributing deep RNN across multiple GPUs
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Applying Dropout
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The difficulty of training over many time steps
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LSTM Cell (Long short-term Memory)
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