Using neural networks for enhancing video call quality

One of the things our tech team is always fascinated by is the way in which neural networks can be deployed to solve a variety of use cases. And even though we work with conversational text and chatbots to improve customer service for our customers, we are inherently curious and tackle various challenges through our experimental work. So it is no wonder that a few months ago, during one of our casual Friday afternoon brainstorming sessions, our talk drifted to the poor quality of video calls with an iPhone. It is well known that the popularity in various entertainment and

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3 Elements That Make Bots Work In Facebook Messenger

The brave new world of bots is marching onward with each passing day to the steady drumbeat of growth in messaging apps worldwide. People love to text and messaging has emerged as the preferred communication mode for many. With the growth in Facebook Messenger and its platform, talking to companies and brands through messaging is slowly starting to gain ground. As with any breakout thing, there are opinions on both sides of the spectrum of whether it is hype or whether it is something tangible that will stick around. Whatever the case may be (I’ll leave my crystal ball post

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Deep learning networks benchmarked.

As we have made a good number of improvements in our visual recommendation technology we decided to benchmark it against other popular deep learning networks. The aim was to compare our recommendation against AlexNet (Caffe implementation) and ResNet (Torch implementation) and to outline errors in product similarity identification. The setup for the comparison was such that with each network we fed in 3 different product categories and identified visually similar products based on a single thumbnail image. The errors are outlined in the images below in red circles. For the product categories we used 3260 different bags, 7028 different shoes

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