Difference between revisions of "Artificial Neural Networks and Deep Learning"
(Created page with "Work in progress, coming soon ...") |
|||
| Line 1: | Line 1: | ||
| − | + | __FORCETOC__ | |
| + | |||
| + | 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. | ||
| + | |||
| + | * [https://www.deib.polimi.it/ita/personale/dettagli/267262 Matteo Matteucci]: the course teacher | ||
| + | * [https://www.deib.polimi.it/ita/personale/dettagli/549640 Giacomo Boracchi]: the course teacher | ||
| + | * [https://www.deib.polimi.it/ita/personale/dettagli/846174 Francesco Lattari]: the course teaching assistant | ||
Revision as of 19:28, 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!
Contents
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
