{"id":110,"date":"2019-08-04T16:13:51","date_gmt":"2019-08-04T16:13:51","guid":{"rendered":"https:\/\/machinelearningindaba.com\/2017\/?page_id=110"},"modified":"2019-08-14T13:47:11","modified_gmt":"2019-08-14T13:47:11","slug":"background-topics","status":"publish","type":"page","link":"https:\/\/deeplearningindaba.com\/2017\/indaba\/programme\/background-topics\/","title":{"rendered":"Background Topics"},"content":{"rendered":"

Deep Learning is a branch of Machine Learning that applies deep neural networks to modelling data such as language<\/strong> (labelling sentences as having a positive or negative sentiment, finding the main topics in documents, translating between languages), speech<\/strong> (transcribing speech data to text), and images<\/strong> (detecting and labelling objects within images). We expect (and indeed we hope!) to attract attendees from a very wide range of backgrounds, e.g., statistics, information engineering, statistical physics, computational neuroscience, econometrics, computational biology, etc. So if you\u2019re interested, you should apply.<\/strong><\/p>\n

The lectures will range from more introductory to more advanced topics, and will cover topics from across machine learning. We will aim throughout to solidify concepts and fill in gaps through discussions, forming study groups, and having practical sessions. If you\u2019ve completed any previous machine learning course (at a university or an online course), you will have covered most of the necessary background concepts.<\/p>\n

You should be able to follow most of the lectures if you have a basic knowledge of these topics (we will also offer refreshers on most of these):<\/p>\n

Linear algebra:<\/h4>\n

If you know what vectors and matrices are, and you can multiply a matrix with a vector, you\u2019ll be fine. If not, see the following:<\/p>\n