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