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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.

  • 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.

  • 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

  • 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

  • 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.

avatar for Louis Dorard

Louis Dorard

General Chair, PAPIs
Louis Dorard is the author of Bootstrapping Machine Learning, of the Machine Learning Canvas, General Chair of PAPIs.io (international conferences on ML applications and APIs), and Adjunct Teaching Fellow at UCL School of Management.As an independent consultant and Machine Learning... Read More →

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