Deep Learning
(3 days)

Machine Learning with neural networks

Nb. days
Min. participants
Max. participants
Toulouse, Paris, etc.
2290€ HT
Deep Learning <br/> (3 days)
Artificial intelligence was created in the 1950s. After many winters, this science is currently experiencing a new boom. Machine Learning and in particular Deep Learning are at the origin of many advances in various fields (computer vision, natural language processing, etc.) applicable to all sectors of activity.
The increase in the volume of data and the computational power of the machines at our disposal now allow us to apply and improve the theories set out a few decades ago.
At the end of this training, you will know the fundamental principles of Deep Learning and you will master the different architectures of neural networks, allowing you to create models that meet your needs.


  • Understanding Deep Learning
  • Use Deep Learning frameworks: TensorFlow v2 and Keras
  • Master the different neural network architectures: dense, convolutional, recurrent, generative
  • Implement concrete cases for each type of network
  • Run calculations on CPUs, GPUs and TPUs
  • Measure the relevance of the implemented models & visualize the learning
  • Deploy a model in production
Alexia Audevart
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to adapt this program to your needs
or to obtain additional information,
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Teaching method

  • This training is 70% practical and uses illustrated and didactic exercises.
  • A daily evaluation of the acquisition of the knowledge of the day before is carried out.
  • A synthesis is proposed at the end of the training.
  • An evaluation will be proposed to the trainee at the end of the course.
  • A course support will be given to each participant including slides on the theory and exercises.
  • A sign-in sheet for each half-day of attendance is provided at the end of the course as well as a certificate of completion if the trainee has attended the entire session.
  • A follow-up and an exchange with the participants will take place a few days after the training.


Knowledge of the Python language is required as well as knowledge of scientific libraries (numpy and pandas).


  • Data Analysts
  • Data Engineers
  • Data Scientists
  • Developers


This training does not require a formal assessment of learning


Machine Learning

  • Machine Learning definition
  • The 5 tribes of Machine Learning
  • The several kinds of learning
  • Learning and Inference

Demo: Machine Learning from A to Z

First steps in neurons

  • The formal neuron
  • The perceptron
  • Activation functions
  • Gradient Descent

Demo: Neural Network Playground
Hands on: My first neural network

TensorFlow and Keras

  • TensorFlow History
  • TensorFlow v2 & Keras
  • Graph vs eager execution
  • Cloud Computing / CPU / GPU / TPU
  • TensorBoard

Demo: TensorBoard
Hands on: Number recognition (MNIST)

Convolutional Neural Network

  • CNN vs the human visual cortex
  • Convolution & pooling layers
  • Activation functions
  • CNN Architecture
  • How a network learns?
  • Some reference architectures

Demo: Number Recognition with CNN (MNIST)
Hands on: Image recognition'

Recurrent Neural Network

  • RNN
  • LSTM
  • GRU
  • Natural Language Processing: Embeddings & Word2vec
  • Transformers

Hands on: Prévision de séries temporelles
Hands on: Text Generation

Deep Generative Models

  • Unsupervised learning
  • Auto-Encoders & VAE (Variational Auto-encoder)
  • GANs

Demo: Playground GAN
Demo: Generation of realistic photos
Demo: Applying a style to a picture