COURSE DETAILS

Introduction to Deep Learning (Using Pytorch)

• Introduction to Pytorch
• Introduction to Perceptron & Neural Networks
• Activation Functions
• Loss functions
• Optimizers(SGD, ADAM)
• Forward and Back propagation
• Vanishing and Exploding Gradient Problem
• Learning rate
• Multilayer Architechture and Deep Learning
• Deep Neural network model development and interpretation – Example
• Model validation
• Hyperparameter tuning
• Regulariation
• Introduction to various Deep Learning Architectures and their applications

• Convolutional Neural Networks(CNN)
• Recurrent Neural Networks(RNN)
• Long Short term Memory(LSTM)
• Gated recurrent unit(GRU)
• GANs(Generative adversarial networks)
• AutoEncoders