Going to the Bank: Using Deep Learning For Banking and the Financial Industry
At ODSC London 2018, Pavel Shkadzko explained to the audience how Gini GmbH, where he works as a semantics engineer, uses deep learning to automate information extraction from financial documents, such as invoices. By applying deep learning to tasks historically handled by optical character recognition and clever regular expression... Read more
Create Your First Face Detector in Minutes Using Deep Learning
Face detection is one of the most widely-demanded subfields of computer vision. Due to the advent of deep learning, computer vision has gained significant development in the last few years, and this trend is only going to increase over time. There are more and more people using computer vision... Read more
New Approaches Apply Deep Learning to Recommender Systems
Oliver Gindele is Head of Machine Learning at Datatonic. At ODSC Europe 2018, he spoke about how to apply deep learning techniques to recommender systems. He discussed how data scientists can implement some of these novel models in the TensorFlow framework, starting from a collaborative filtering approach and extending... Read more
Layer-wise Relevance Propagation Means More Interpretable Deep Learning
Wojciech Samek is head of machine learning for Fraunhofer Heinrich Hertz Institute. At ODSC Europe 2018, he spoke about an active area of research in deep learning: interpretability. Samek launched his lecture with the following preface on the rising importance of interpretability of deep learning models: “In the last number of... Read more
Mail Processing with Deep Learning: A Case Study
Businesses increasingly delegate simple, boring, and repetitive tasks to artificial intelligence. In a case study, Alexandre Hubert — lead data scientist of software company Dataiku’s U.K. operations — worked on a team of three to automate mail processing with deep learning. At ODSC Europe 2018, Hubert detailed how his team... Read more
Getting to Know Keras for New Data Scientists
For many new data scientists transitioning into AI and deep learning, the Keras framework is an efficient tool. Keras is a powerful and easy-to-use Python library for developing and evaluating deep learning models. In this article, we’ll lay out the welcome mat to the framework. You should walk away with... Read more
Data Storage Keeping Pace for AI and Deep Learning
Data is the new currency driving accelerated levels of innovation powered by AI. Enterprises require modern data storage architectures purpose-built for deep learning and designed to shorten the time to insights while simplifying complex big data pipelines. Continuing to use legacy storage systems, however, will introduce serious complications in... Read more
Overview of the YOLO Object Detection Algorithm
Let’s review the YOLO (You Only Look Once) real-time object detection algorithm, which is one of the most effective object detection algorithms that also encompasses many of the most innovative ideas coming out of the computer vision research community. Object detection is a critical capability of autonomous vehicle technology.... Read more
Deep Learning Is Going to Teach Us All the Lesson of Our Lives: Jobs Are for Machines
(An alternate version of this article was originally published in the Boston Globe) On December 2nd, 1942, a team of scientists led by Enrico Fermi came back from lunch and watched as humanity created the first self-sustaining nuclear reaction inside a pile of bricks and wood underneath a football field... Read more
Optimization for Deep Learning Highlights in 2017
Table of contents: Improving Adam Decoupling weight decay Fixing the exponential moving average Tuning the learning rate Warm restarts SGD with restarts Snapshot ensembles Adam with restarts Learning to optimize Understanding generalization Deep Learning ultimately is about finding a minimum that generalizes well — with bonus points for finding... Read more