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
At NVIDIA, Deep Learning Gets Deeper
Alison Lowndes and her team at NVIDIA are finding exciting new ways to handle the demands deep learning places on machines — and even more exciting ways to use the new technology. At ODSC London 2018, Alison Lowndes of NVIDIA gave a talk on how advances in graphics processing... 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
Efficient, Simplistic Training Pipelines for GANs in the Cloud with Paperspace
Generative adversarial networks — GANs for short — are making waves in the world of machine learning. Yann LeCun, a legend in the deep learning community, said in a Quora post “ the most interesting idea in the last 10 years in .” GANs (and, more generally,... Read more
Why are Convnets Often Better Than the Rest? Part III
This is the third part in a series on Convnets. Read the first part here and the second part here. Shared weights The shared weight design is exactly why convolutional nets (Convnets for short) are good at detecting the same features in different parts of an image. When I mentioned shared... 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
Using machine learning to play games has always been an excellent way to understand and evolve learning models. In the last ten years, Google DeepMind managed to train a convolutional neural network to play Go, and IBM’s chess computer Deep Blue beat the best chess player in the world.... 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