Applied Deep Learning

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:
– Dr. Alejandro Correa Bahnsen http://albahnsen.com
– Prof. Dr. Fabio Gonzalez http://dis.unal.edu.co/~fgonza/

Graduate assistant:
– Sergio Angulo

Course github repository: https://github.com/albahnsen/AppliedDeepLearningClass

Schedule

Introduction to Machine Learning and Neural Networks

Date Session Notebook Exercises
June-6 Introduction to python and ML 1 – Intro to ML 2 – Intro to Python for data analysis 3 – Linear Regression 4 – Logistic Regression E1 – Python&Pandas E2 – Regression
June-8 Machine learning systems 5 – Data preparation and model evaluation 6 – Sampling 7 – Ensemble methods 8 – Model deployment E3 – Cross Validation E4 – Sampling and Ensembles
June-20 Neural networks basics 9 – Intro NN 10 – Perceptron 11 – Keras E5 – Neural Networks

Introduction to Deep Learning

Date Session Notebook Exercises
June-22 Introduction to deep learning and applications 12 – Intro to Deep Learning
July-4 Deep learning frameworks 13 – Intro to Tensorflow 14 – Deep learning frameworks E6 – TensorFlow and Keras

Deep Learning for Image Analysis

Date Session Notebook Exercises
July-5 Deep learning for image analysis & CNN 15 – CNN training in Keras P2 – Image classification with CNN
July-6 CNN and transfer learning 16 – Convolutional neural networks 17 – Transfer learning E7 – Image Captioning
July-11 Generative models 18 – Generative adversarial networks

Deep Learning for Text Analysis

Date Session Notebook Exercises
July-12 Intro to NLP and Intro to RNN 19 – Intro to natural language processing E8 – Sentiment classification
July-13 Word2vec & RNN for text analysis 20 – Recurrent neural network and LSTM 21 – Word3Vec E9 – Sequence classification using LSTM

Final Project

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