Introduction to Deep Learning

Ex utamur fierent tacimates duis choro an

Lorem ipsum dolor sit amet, ius minim gubergren ad. At mei sumo sonet audiam, ad mutat elitr platonem vix. Ne nisl idque fierent vix.

Overview

Deep learning is a sub-field of machine learning, where models inspired by how our brain works are expressed mathematically, and the parameters defining the mathematical models, which can be in the order of few thousands to 100+ million, are learned automatically from the data.

Objective

Fundamental concepts of Deep Learning, including various Neural Networks for supervised and unsupervised learning.Build, train, and deploy different types of Deep Architectures, including ConvolutionalNetworks, Recurrent Networks, and Autoencoders

Prerequisites

  • Linear Algebra.
  • Calculus and Statistics.
  • Programming and Basic Machine Learning.

What you'll learn

  • The components of a deep neural network and how they work together.
  • The basic types of deep neural networks (MLP, CNN, RNN, LSTM) and the type of data each is designed for
  • A working knowledge of vocabulary, concepts, and algorithms used in deeplearning
  • How to build:
    • An end-to-end model for recognizing hand-written digit images, using a multi-class Logistic Regression and MLP (Multi-Layered Perceptron)
    • A CNN (Convolution Neural Network) model for improved digit recognition
    • An RNN (Recurrent Neural Network) model to forecast time-series data
    • An LSTM (Long Short Term Memory) model to process sequential text data

Course Outline

  • 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 Architecture and Deep Learning
  • Deep Neural network model development and interpretation – Example
  • Model validation
  • Hyper parameter tuning
  • Regularisation
  • 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)
  • Auto Encoders
  • Course Id                                             A102