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
Deep Learning Question-Answer Model with Demo
  How amazing would be a model that can answer questions from any paragraph by extracting word(s) from the paragraph that are most relevant. Deep learning has made this possible! See below a demo for such a question-answer super model. In this demo you can put in any context up to 300 words... Read more
Why are Convnets Often Better Than the Rest? Part II
Following on Part I of “Why are Convnets often Better than the Rest?”, we will now look at a traditional neural network’s weakness. As before, I am going to assume you understand the basics of neural network modeling already, including how images are applied to input layers. Through the... Read more
Lessons Learned and Reinforced from Writing My Own Deep Learning Package
At work, I’ve been rolling my own deep learning package to experiment with graph convolutional neural networks. I did this because in graph-centric deep learning, an idea I picked up from this paper, the inputs, convolution kernels, and much more, are being actively developed, and the standard APIs don’t... Read more
Why are Convnets Often Better Than the Rest? Part I
Introduction In this series, I will explore convolutional neural networks in comparison to standard neural networks. To begin with, the former is an evolution of the latter. Through analyzing this evolution, it is fascinating to see how particular design differences have such a great impact on performance and overall... Read more
Reel Reviews: Neural Networks for Sentiment Analysis
This is a joint article authored in collaboration between Kannan Sankaran and Win Suen. The Problem Over the past few years, there has been burgeoning interest in neural networks from data science and engineering communities. The advent of ever larger datasets, efficient commodity hardware, and powerful open source libraries... Read more
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