AI in production: The droids youre looking for
February 5, 2020
Artificial intelligence in the future, at least represented in science fiction, can learn, interpret, and take action based on data analysis. AI in production is the present, a present that feels decidedly futuristic. Jonathan Ballon explains why Intels leading portfolio of AI and computer vision edge technology will drive advances that improve how we work and live.
Enabling traditional vision on specialized deep learning hardware
February 4, 2020
In recent years, weve seen a shift from traditional vision algorithms to deep neural network algorithms. While many companies expect to move to deep learning for some or all of their algorithms, they may have a significant investment in classical vision. Paul Brasnett explains how to express and adapt a classical vision algorithm to become a trainable DNN.
Debuggable deep learning
January 13, 2020
Deep learning is often called a black box, so how do you diagnose and fix problems in a deep neural network (DNN)? Avesh Singh and Kevin Wu explain how they systematically debugged DeepHeart, a DNN that detects cardiovascular disease from heart rate data. You'll leave with an arsenal of tools for debugging DNNs, including Jacobian analysis, TensorBoard, and DNN unit tests.
Distributed deep learning in the cloud: Build an end-to-end application involving computer vision and geospatial data
January 13, 2020
High-resolution land cover maps help quantify long-term trends like deforestation and urbanization but are prohibitively costly and time intensive to produce. Mary Wahl and Banibrata De demonstrate how to use Microsofts Cognitive Toolkit and Azure cloud resources to produce land cover maps from aerial imagery by training a semantic segmentation DNNboth on single VMs and at scale on GPU clusters.
The breadth of AI applications: The ongoing expansion
January 10, 2020
In 2011, we saw a sudden increase in the abilities of computer vision systems brought about by academic researchers in deep learning. Today, Peter Norvig explains, we see continued progress in those fields, but the most exciting aspect is the diversity of applications in fields far astray from the original breakthrough areas, as well as the diversity of the people making these applications.
Deep learning 101: Apache MXNet
December 31, 2019
Simon Corston-Oliver offers an introduction to deep learning in Python using Apache MXNet. Starting with deep learning fundamentals, Simon then walks you through training and evaluating a model and explores advanced topics such as training on multiple GPUs.
Deep computer vision for manufacturing
December 11, 2019
Convolutional neural networks (CNN) can now complete many computer vision tasks with superhuman ability. This is will have a large impact on manufacturing, by improving anomaly detection, product classification, analytics, and more. Aurlien Gron details common CNN architectures, explains how they can be applied to manufacturing, and covers potential challenges along the way.
Deep learning for recommender systems
December 11, 2019
In the last few years, deep learning has achieved significant success in a wide range of domains, including computer vision, artificial intelligence, speech, NLP, and reinforcement learning. However, deep learning in recommender systems has, until recently, received relatively little attention. Nick Pentreath explores recent advances in this area in both research and practice.
Improving computer vision models at scale
December 8, 2019
Rigorous improvement of an image recognition model often requires multiple iterations of eyeballing outliers, inspecting statistics of the output labels, then modifying and retraining the model. Marton Balassi, Mirko Kmpf, and Jan Kunigk share a solution that automates the process of running the model on the testing data and populating an index of the labels so they become searchable.
Smart diagnosis in healthcare with deep learning
December 3, 2019
Deep learning with ConvNet in particular has emerged as a promising tool in medical research labs and diagnostic centers to help analyze images and scans, and systems are now surpassing human capability for manual inspection. Nishant Sahay explains how to apply deep learning to analyze high-end microscope images and X-ray scans to provide accurate diagnosis.