DEEP LEARNING
TensorFlow Essentials
– Introduction to TensorFlow
– Computational Graph
– Stochastic Gradient Descent
– Visual TensorBoard
– Keras with TensorFlow
Activation Functions
– Role of Activation functions in ANN network
– Activation Functions:
– Sigmoid (Binary)
– Softmax (Multiclass)
– ReLU (Linear)
Artificial Neural Networks
– Introduction to ANN
– Concept of Perceptron
– Perceptron Training Rule
– Gradient Descent Rule
Gradient Descent and Backpropagation
– Gradient Descent
– Stochastic Gradient Descent
– Backpropagation
– Some problems in ANN
Optimization and Regularization
– Overfitting and Capacity
– Cross Validation
– Feature Selection
– Regularization
– Hyperparameters
Introduction to Convolutional Neural Networks (CNN)
– Introduction to CNNs
– Principles behind CNNs
– Kernel and Multiple Filters
– CNN Image Classification demo
Introduction to Recurrent Neural Networks (RNN)
– Introduction to RNNs
– Unfolded RNNs
– Seq2Seq RNNs
– LSTM
– RNN applications
Deep Learning applications
– Image Processing
– Natural Language Processing
– Speech Recognition
– Video Analytics
Deep Learning Projects (Hands On)
– Image Classification with CNN – Keras
– Natural Language Processing with NKTL live project
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