v2.11.0 (5518)

PA - C8 - DS-télécom-6 : Deep Learning 1


Deep Learning (machine learning based on deep artificial neural networks) has become extremely popular over the last years due to the very good results it allows for tasks such as regression, classification or genera- tion. The objective of this course is to provide a theoretical understanding and a practical usage of the three main types of networks (Multi-Layer- Perceptron, Recurrent-Neural-Network and Convolutional Neural Network). The content of this course ranges from the perceptron to the generation of adversarial images. Each theoretical lecture is followed by a practical lab on the corresponding content where student learn to implement these networks using the currently three popular frameworks: pytorch, tensorflow and keras.


Format : 6 sessions of 3.5 hours + Exam


Lectures content:

  • Multi-Layer-Perceptron (MLP): Perceptron, Logistic Regression, Chain rule, Back-propagation, Deep Neural Activation functions, Vanishing gradient, Ini- tialization, Regularization (L1,L2,DropOut), Alternative Gradient Descent, Batch-


  • Recurrent Neural Network (RNN) : Simple RNN, Forward Propagation, Backward Propagation Through Time, Vanishing/ Exploding gradients, Gated Units (LSTM, GRU), Various architectures, Sequence-to-sequence, Attention model
  • Convolutional Neural Network (CNN) : CNNs use sparse connectivity and weight sharing to reduce parameters and create more powerful networks , connections are organized in a convolution operation, CNNs now provide the state- of-the-art in a vast array of problems, we will see how CNNs work and we will implement them for classification problems


Labs content :

  • text recognition, sentiment classification 
  • music generation
  • image recognition
  • image generation


Programming language

  • Python (numpy, scikit-learn, matplotlib)
  • DL frameworks: pytorch, tensorflow, keras
  • Use Télécom computers, your own labtop or colab.research.google.com → needs a Google account → open one before the first Lab !
  • a Graphics Processing Unit (GPU) will not be required, however if you have one, this will speed up the learning process


Grading : 30% labs/project + 70% written exam


Diplôme(s) concerné(s)

Parcours de rattachement

Format des notes

Numérique sur 20

Littérale/grade réduit

Pour les étudiants du diplôme M2 Physics by Research

Le rattrapage est autorisé (Note de rattrapage conservée)
    L'UE est acquise si note finale transposée >= C
    • Crédits ECTS acquis : 3 ECTS

    Pour les étudiants du diplôme Data Sciences

    Le rattrapage est autorisé (Max entre les deux notes)
      L'UE est acquise si Note finale >= 10
      • Crédits ECTS acquis : 3 ECTS
      Veuillez patienter