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Class Applied Deep Learning - Summer 2018
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README.md

README.md

Applied Deep Learning Class

Uniandes - Summer 2018

The use of statistical models in computer algorithms allows computers to make decisions and predictions, and to perform tasks that traditionally require human cognitive abilities. Deep learning is the interdisciplinary field at the intersection of statistics and computer science which develops such algorithnms and interweaves them with computer systems. It underpins many modern technologies, such as speech recognition, internet search, bioinformatics, computer vision, Amazon’s recommender system, Google’s driverless car and the most recent imaging systems for cancer diagnosis are all based on Deep Learning technology.

This course on Deep Learning will explain how to build systems that learn and adapt using real-world applications. Some of the topics to be covered include deep learning frameworks, convolutional neural networks, generative models nadrecurrent models. The course will be project-oriented, with emphasis placed on writing software implementations of learning algorithms applied to real-world problems, in particular, image analysis, image captioning, natural language pocessing, sentiment detection, among others.

Instructors:

Graduate assistant:

Resources

Schedule

Introduction to Machine Learning and Neural Networks

Date Session Notebooks/Presentations Exercises
June-6 Introduction to python and ML
June-8 Machine learning systems
June-20 Neural networks basics

Introduction to Deep Learning

Date Session Notebooks/Presentations Exercises
June-22 Introduction to deep learning and applications
July-4 Deep learning frameworks

Deep Learning for Image Analysis

Date Session Notebook Exercises
July-5 Deep learning for image analyssis & CNN
July-6 CNN and transfer learning
July-11 Generative models

Deep Learning for Text Analysis

Date Session Notebook Exercises
July-12 Intro to NLP & Intro to RNN
July-13 Word2vec & RNN for text analysis

Final Project

Date Session Readme Kaggle
July-23 Final project presentations Description Link Kaggle
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