Using gpus Efficiently for ML
In this blog post, we will look into how to use multiple gpus with Pytorch. We will see how to do inference on multiple gpus using DataParallel and DistributedDataParallel models of pytorch. Same methods can also be used for multi-gpu training. Pytorch provides a very convenient to use and easy to understand api for deploying/training models […]
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A quick complete tutorial to save and restore Tensorflow 2.0 models
Update: This article has been updated to show how to save and restore models in Tensorflow 2.0. If you want to learn the same with Tensorflow1.x, please go to this earlier article that explains how to save and restore Tensorflow 1.x models. In this Tensorflow 2.X tutorial, I shall explain: What is a Tensorflow-Keras Model API? […]
Continue ReadingWho’s Who of Deep Learning Eco-System
Alexnet, which started the deep learning revolution, was loosely based on a network architecture(LENet) proposed by Yann Lecun in 1998. However, back then, we didn’t have the compute or the training data to train and produce the results like Alexnet. Alex used Nvidia GPUs for training, in fact he used two GPUs to train which […]
Continue ReadingIntro to AI and Machine Learning for Technical Managers
In 2013, Google acquired a company called DeepMind for 400 Million dollars. DeepMind at the time had no product and no revenue, but what they had was a team of the best brains in the world who worked on Machine learning and Deep Learning. In fact, between 2013 and 2015, all the technology giants(Facebook, […]
Continue ReadingHuman pose estimation using Deep Learning in OpenCV
I recently came across a very interesting use-case of computer vision and AI. Runners Need is a UK based sports shoe brand. They offer automated gait analysis using computer vision before they sell you a customized shoe. Gait analysis is a method for identifying biomechanical abnormalities in the way in which you walk or run. A video […]
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