Deep Learning

Running Deep Learning models in OpenCV

by Ankit Sachan • July 12, 2018

OpenCV: The open source computer vision library for everyone: OpenCV has been the go-to library for computer vision for more than a decade. Released under BSD license, OpenCV code is free to be used for academic as well as commercial purposes. Millions of developers/academics/companies have been using OpenCV to build some really cool stuff. Deep […]

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Choosing a Deep Learning Framework in 2018: Tensorflow or Pytorch?

by Ankit Sachan • May 29, 2018

One of my friends is the founder and Chief data scientist at a very successful deep learning startup. 2017 was a good year for his startup with funding and increasing adoption. However, on a Thursday evening last year, my friend was very frustrated and disappointed. The framework on which they had built everything in last […]

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Object Detection using Single Shot Multibox Detector

by koustubh • January 2, 2018

In a previous post, we covered various methods of object detection using deep learning. In this blog, I will cover Single Shot Multibox Detector in more details. SSD is one of the most popular object detection algorithms due to its ease of implementation and good accuracy vs computation required ratio. Work proposed by Christian Szegedy […]

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Keras tensorflow tutorial

Keras tutorial: Practical guide from getting started to developing complex deep neural network

by Ankit Sachan • May 16, 2017

Keras is a high-level python API which can be used to quickly build and train neural networks using either Tensorflow or Theano as back-end. This tutorial assumes that you are slightly familiar convolutional neural networks. You can follow the first part of convolutional neural network tutorial to learn more about them. In this quick tutorial, we shall […]

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Deep Learning on Raspberry Pi

Accelerating Convolutional Neural Networks on Raspberry Pi

by koustubh • April 5, 2017

Unless you have been living under the rock, you must have heard of the revolution that deep learning and convolutional neural networks have brought in computer vision. Computers have achieved near-human level accuracy for most of the tasks. However, most of the top performing neural networks for state of the art image recognition problem suffer […]

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