# Difference between revisions of "Artificial Neural Networks and Deep Learning"

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This goal is pursued in the course by: | This goal is pursued in the course by: | ||

− | + | * Presenting major theoretical results underpinning NN (e.g., universal approx, vanishing/exploding gradient, etc.) | |

− | + | * Describing the most important algorithms for NN training (e.g., backpropagation, adaptive gradient algorithms, etc.) | |

− | + | * Illustrating the best practices on how to successfully train and use these models (e.g., dropout, data augmentation, etc.) | |

− | + | * Providing an overview of the most successful Deep Learning architectures (e.g., convolutional networks, sparse and dense autoencoder for embedding, long-short term memories for sequence to sequence learning, etc.) | |

− | + | * Providing an overview of the most successful applications with particular emphasis on models for solving visual recognition tasks. |

## Revision as of 20:31, 18 September 2019

The following are last minute news you should be aware of ;-)

19/09/2019: No lecture on the 20/09/2019 ... check the detailed schedule. 19/09/2019: The course starts today!

## Course Aim & Organization

Neural networks are mature, flexible, and powerful non-linear data-driven models that have successfully been applied to solve complex tasks in science and engineering. The advent of the deep learning paradigm, i.e., the use of (neural) network to simultaneously learn an optimal data representation and the corresponding model, has further boosted neural networks and the data-driven paradigm.

Nowadays, deep neural network can outperform traditional hand-crafted algorithms, achieving human performance in solving many complex tasks, such as natural language processing, text modeling, gene expression modeling, and image recognition. The course provides a broad introduction to neural networks (NN), starting from the traditional feedforward (FFNN) and recurrent (RNN) neural networks, till the most successful deep-learning models such as convolutional neural networks (CNN) and long short-term memories (LSTM).

The course major goal is to provide students with the theoretical background and the practical skills to understand and use NN, and at the same time become familiar and with Deep Learning for solving complex engineering problems.

### Teachers

The course is composed of a blending of lectures and exercises by the course teachers and a teaching assistant.

- Matteo Matteucci: the course teacher
- Giacomo Boracchi: the course teacher
- Francesco Lattari: the course teaching assistant

### Course Program

This goal is pursued in the course by:

- Presenting major theoretical results underpinning NN (e.g., universal approx, vanishing/exploding gradient, etc.)
- Describing the most important algorithms for NN training (e.g., backpropagation, adaptive gradient algorithms, etc.)
- Illustrating the best practices on how to successfully train and use these models (e.g., dropout, data augmentation, etc.)
- Providing an overview of the most successful Deep Learning architectures (e.g., convolutional networks, sparse and dense autoencoder for embedding, long-short term memories for sequence to sequence learning, etc.)
- Providing an overview of the most successful applications with particular emphasis on models for solving visual recognition tasks.