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Monday, October 15 • 9:00am - 5:30pm
Deep Learning Kickstart with Keras — Training Workshop

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This training workshop will take place before the main conference. It will be given in a classroom of up to 20 persons only, to maximize interaction and so you can ask even more questions than in a conference setting.

IMPORTANT:
  • A specific ticket is required to get access to the workshop ("Training (10/15) + Whole Conference (10/16-17)").
  • The venue is different from that of the main conference. The workshop will be held at Nanigans — many thanks to them for providing the space!
  • Students should bring their own laptops, for practical work. They will be given access to GPU-equipped machines in the cloud, for hands-on experiments with deep learning.


LEARNING OBJECTIVES
  • Understand the possibilities and limitations of Deep Learning
  • Understand how single and multi-layered Neural Networks are trained on data
  • Create, evaluate and optimize Neural Networks with Keras
  • Tackle image recognition tasks with Convolutional Neural Networks
  • Leverage Transfer Learning to speed up training and increase accuracy


PROGRAM
  • Introduction to Machine/Deep Learning and its possibilities:
    • Fundamental concepts
    • Formalizing supervised learning problems: classification and regression
    • Example use cases
    • Revisions of Python basics; usage of Jupyter notebooks
  • Linear and logistic regression:
    • Performance metrics: MSE (regression), accuracy and log-loss (classification)
    • Creating a single-layer network with Keras: defining input and output layers, optimizer, compilation, training
    • Logistic and softmax functions for classification
    • Data preparation
  • Multi-layered neural networks:
    • Structure of fully-connected, multi-layered networks
    • Activation functions
    • Adding layers in Keras
    • Exporting trained networks/models for deployment
  • Evaluating, optimizing and comparing models:
    • Evaluation procedure
    • Plotting and interpreting learning curves
    • Detecting overfitting
    • Reducing training time via efficient GPU utilization
    • Application to structured datasets
  • Convolutional Neural Networks and their application to image recognition:
    • Convolution layers, pooling layers, and “dropout” regularization
    • Application to MNIST (handwritten digit recognition)
  • Introduction to Transfer Learning:
    • Reusing trained deep nets to extract high-level features and tackle new problems efficiently
    • Application to an image classification challenge on Kaggle
  • Going further with Deep Learning:
    • Recap
    • Limitations of Deep Learning
    • Practical tips for using Deep Learning in your applications
    • Other types of Neural Networks
    • Resources


STUDENT REQUIREMENTS
  • Programming experience and basic knowledge of the Python syntax. Code will be provided for students to replicate what will be shown during hands-on demos. Please consult Codeacademy's Learn Python and Robert Johansson's Introduction to Python programming (in particular the following sections: Python program files, Modules, Assignment, Fundamental types, Control Flow and Functions) to learn or revise Python's basics.
  • Basic maths knowledge (undergraduate level) will be useful to better understand some of the theory behind learning algorithms, but it isn’t a hard requirement.
  • Own laptop to bring for hands-on practical work.

Speakers
avatar for Louis Dorard

Louis Dorard

General Chair, PAPIs



Monday October 15, 2018 9:00am - 5:30pm
Nanigans 100 Summer Street, 31st Floor, Boston MA, 02110

Attendees (22)