Machine Learning Workshop

Anthill Inside 2017

Shubham Dokania

@shubhamdokania

About Me

  • Recent Graduate from Delhi Technological University
    • B.Tech in Mathematics and Computing Engineering
  • Machine Learning Instructor at Coding Blocks.
  • Research Intern at IIIT-D
    • Supervised by Dr. Ganesh Bagler
    • Working on drug-side-efffect predictions
  • A few papers (accepted and under-review) on Evolutionary Optimization, Vision and ML.
  • Independent Study on Deep Learning, Reinforcement Learning.

Workshop Overview

  • Introduction to Machine Learning
  • Four Sessions (~90 mins each)
  • Mathematical Conceptualisation
  • Project Implementation

Session Information

  • Introduction and warm-up
  • CNN architectures and applications
  • Transfer Learning & Sequence Processing
  • Reinforcement Learning

Session 01: Introduction

  • History and Introduction to DL
  • Applications (Industrial and daily)
  • Convex Optimization
  • Hands-on with code

Deep Learning

  • Introduction and some History
  • The Gartner Hype Cycle
  • Applications
    • Image classification/recognnition
    • Image captioning/VQA
    • Sequence processing
    • Speech Recognition
    • ¬†

Neural Networks

A brief discussion

Foundation

Perceptron model

Relation with Logistic Regression

Neurons and ANN

Artificial Neural Network

Interactive Example

Tensorflow Playground

Gradient Descent

Optimization

Backpropagation

(Chain rule for neural nets)

What's going on inside the black box?

Neural Nets as Feature extractors

It's all about Manifolds

Every layer is a transformation operation

Some examples of transformation operations

Build your own neural network

Libraries: Numpy, Matplotlib

Some Popular Frameworks

  • Caffe
  • Torch
  • PyTorch
  • Chainer
  • Lasagne
  • Tensorflow
  • Theano
  • Keras
  • And more...

Small Break

~10-15 mins

Session 02:Convolutions

And CNN

What is Convolution?

Some parameters...

Padding, Strides etc...

Application to Neural Networks

Advantages and Disadvantages over ANN

Hands-on with CNNs

Toy example

The MNIST dataset

How it works?

Some visualizations of filters 

ImageNet datasets

models like VGG, AlexNet, Inception-v3 etc.

Using Pre-trained models

Use VGG-19 from Keras

Small Break

Session 03: Transfer Learning

  • What is it?
  • Applications

Small Demo with MNIST

Knowledge transfer

with VGG-16/19 Net

Results and Discussion

  • Accuracy and Loss
  • Training Time
  • Improvements

Exciting Applications!

Deep Dream and Style Transfer

Let's build both!

Sequence Processing

Introduction to Recurrent Architectures

The Recurrent Neural Network

Training of RNN

Backprop through time

Improvements over RNN

Long Short-Term Memory (LSTM)

Encoder-Decoder models

and thought-vector representations

Hands-On

Music generation with LSTMs

Applications of Previous Concepts

  • AutoEncoder models
  • Sequence to sequence learning
    • Neural Machine Translation
    • Sentiment classification
  • And more...

Small Break

~10-15 mins

Session 04: Reinforcement Learning

  • Supervised, Unsupervised and RL
  • Learning through Rewards
  • The algorithm powering AlphaGo

Q-Learning

  • A Dynamic Programming approach
  • Update function
  • Toy task with simple graph

Approximate Q-Learning

  • Feature based learning
  • Approximate rewards
  • Basis for Deep Q-Networks

Deep Q-Network

and solving Atari games

Problem Statement

  • Game-play understanding
  • The Reward function
  • DQN on Atari Ms-Pacman
  • Hands-on project

Final Project

Train Flappy bird with RL

Final Wrap-up

  • Conclusions
  • Some more examples
  • What to do after the workshop
  • Resources and papers to follow

Thank You!

Deep Learning Applications: A hands-on approach

By Shubham Dokania

Deep Learning Applications: A hands-on approach

Presentation for Anthill Inside 2017 workshop

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