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Course Outline
The course is divided into three separate days, the third being optional.
Day 1 - Machine Learning & Deep Learning: theoretical concepts
1. Introduction IA, Machine Learning & Deep Learning
- History, basic concepts and usual applications of artificial intelligence far
Of the fantasies carried by this domain
- Collective Intelligence: aggregating knowledge shared by many virtual agents
- Genetic algorithms: to evolve a population of virtual agents by selection
- Usual Learning Machine: definition.
- Types of tasks: supervised learning, unsupervised learning, reinforcement learning
- Types of actions: classification, regression, clustering, density estimation, reduction of
dimensionality
- Examples of Machine Learning algorithms: Linear regression, Naive Bayes, Random Tree
- Machine learning VS Deep Learning: problems on which Machine Learning remains
Today the state of the art (Random Forests & XGBoosts)
2. Basic Concepts of a Neural Network (Application: multi-layer perceptron)
- Reminder of mathematical bases.
- Definition of a network of neurons: classical architecture, activation and
Weighting of previous activations, depth of a network
- Definition of the learning of a network of neurons: functions of cost, back-propagation,
Stochastic gradient descent, maximum likelihood.
- Modeling of a neural network: modeling input and output data according to
The type of problem (regression, classification ...). Curse of dimensionality. Distinction between
Multi-feature data and signal. Choice of a cost function according to the data.
- Approximation of a function by a network of neurons: presentation and examples
- Approximation of a distribution by a network of neurons: presentation and examples
- Data Augmentation: how to balance a dataset
- Generalization of the results of a network of neurons.
- Initialization and regularization of a neural network: L1 / L2 regularization, Batch
Normalization ...
- Optimization and convergence algorithms.
3. Standard ML / DL Tools
A simple presentation with advantages, disadvantages, position in the ecosystem and use is planned.
- Data management tools: Apache Spark, Apache Hadoop
- Tools Machine Learning: Numpy, Scipy, Sci-kit
- DL high level frameworks: PyTorch, Keras, Lasagne
- Low level DL frameworks: Theano, Torch, Caffe, Tensorflow
Day 2 - Convolutional and Recurrent Networks
4. Convolutional Neural Networks (CNN).
- Presentation of the CNNs: fundamental principles and applications
- Basic operation of a CNN: convolutional layer, use of a kernel,
Padding & stride, feature map generation, pooling layers. Extensions 1D, 2D and
3D.
- Presentation of the different CNN architectures that brought the state of the art in classification
Images: LeNet, VGG Networks, Network in Network, Inception, Resnet. Presentation of
Innovations brought about by each architecture and their more global applications (Convolution
1x1 or residual connections)
- Use of an attention model.
- Application to a common classification case (text or image)
- CNNs for generation: super-resolution, pixel-to-pixel segmentation. Presentation of
Main strategies for increasing feature maps for image generation.
5. Recurrent Neural Networks (RNN).
- Presentation of RNNs: fundamental principles and applications.
- Basic operation of the RNN: hidden activation, back propagation through time,
Unfolded version.
- Evolutions towards the Gated Recurrent Units (GRUs) and LSTM (Long Short Term Memory).
Presentation of the different states and the evolutions brought by these architectures
- Convergence and vanising gradient problems
- Classical architectures: Prediction of a temporal series, classification ...
- RNN Encoder Decoder type architecture. Use of an attention model.
- NLP applications: word / character encoding, translation.
- Video Applications: prediction of the next generated image of a video sequence.
Day 3 - Generational Models and Reinforcement Learning
6. Generational models: Variational AutoEncoder (VAE) and Generative Adversarial Networks (GAN).
- Presentation of the generational models, link with the CNNs seen in day 2
- Auto-encoder: reduction of dimensionality and limited generation
- Variational Auto-encoder: generational model and approximation of the distribution of a
given. Definition and use of latent space. Reparameterization trick. Applications and
Limits observed
- Generative Adversarial Networks: Fundamentals. Dual Network Architecture
(Generator and discriminator) with alternate learning, cost functions available.
- Convergence of a GAN and difficulties encountered.
- Improved convergence: Wasserstein GAN, Began. Earth Moving Distance.
- Applications for the generation of images or photographs, text generation, super-
resolution.
7. Deep Reinforcement Learning.
- Presentation of reinforcement learning: control of an agent in a defined environment
By a state and possible actions
- Use of a neural network to approximate the state function
- Deep Q Learning: experience replay, and application to the control of a video game.
- Optimization of learning policy. On-policy && off-policy. Actor critic
architecture. A3C.
- Applications: control of a single video game or a digital system.
Requirements
Engineering level
Audience: Engineers, Data-Scientists wishing to learn neural networks / Deep Learning
21 Hours
