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.

REGISTRATION & PRACTICAL INFORMATION

The workshop will be given in French.

Register at https://www.humancoders.com/formations/deep-learning