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[Workshop] Deep Learning Kickstart


  • 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.


The workshop will be given in French.

Register at

Earlier Event: 28 February
[Lecture] Predictive Analytics
Later Event: 6 May
[Workshop] Advanced ML