Introduction Deep Learning and Neural Network for Engineers Training Course

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.