Introduction to

Deep Learning

Machine Learning

Paradigms

  • Supervised Learning

  • Unsupervised Learning

  • Reinforcement Learning

Supervised Learning

Supervised Learning

  • Data : (X, y)
  • Model p( y | X )
  • Unavailability of labelled data
  • Data Tagging

Unsupervised Learning

Unsupervised Learning

  • Exploit inherent latent structure in data
  • Generative Modeling
  • Model p(x)
  • Generate new samples from model

Reinforcement Learning

Reinforcement Learning

  • Unknown Environment
  • Not-so-well-defined objective
  • Learn through Self-play

Deep Learning

Deep Learning

Deep Learning

Applications

  • Computer Vision
  • Speech Recognition and Synthesis
  • Language Understanding
    • Machine Translation
    • Dialogue Systems
    • Question Answering
  • Recommendation Systems
  • Robotics

Convolutional

Neural Networks

Convolution

CNN

CNN

Recurrent Neural Networks

RNN

Learning

Linear Regression

Linear Regression

Gradient Descent

Gradient Descent

Multi-layer Perceptron

Backprop

Research

Genomics

DecaNLP

Interpretability

Interpretability

Interpretability

Question Answering

Bayesian Learning

Bayesian Learning

Bayesian Learning

Bayesian Learning

Bayesian Learning

Hyperopt

Hyperopt

dl-intro

By Suriyadeepan R

dl-intro

Introduction to Deep Learning

  • 551

More from Suriyadeepan R