What is a Neural Network ?
From Perceptron to MLP, how does it work ?
Implementation of a CNN using Torch7
What is a convolution and how to use them in Convolutional Neural Networks ?
The current trend in object detection and localization is to learn predictions with high capacity deep neural networks. In this talk, the main state of the art techniques will be presented (RCNN, Fast RCNN, Faster RCNN, MultiBox, YOLO et SSD) along with our technique designed for the detection of many objects from fewer examples. We will emphasize on the similarities and differences between these techniques and we will try to list their pros and cons.
Natural Language Processing (NLP) is a subfield of artificial intelligence and computational linguistics. The main objective is to teach the machine the human language. To learn the language and solve some NLP tasks, machines need a representation for words. My seminar will mainly focus on how to learn a good word representation that carry both semantic and syntactic information by using neural networks. I will then present the method I developed during my internship to improve the semantic similarity between words, by using online lexical dictionaries.
Sponsored by Solstice ANR Project
|Introduction to deep learning||Download|
|Code Torch7 for the introduction||Download|
|Likelihood-based and Likelihood-free Unsupervised Learning||Lien github|
Building Institut Optique Graduate School, Room D03, ground floor, Campus Manufacture, Saint-Etienne