
Distributed data parallel training using Pytorch on AWS
In this post, I’ll describe how to use distributed data parallel techniques on multiple AWS GPU servers to speed up Machine Learning (ML) training. Along […]
In this post, I’ll describe how to use distributed data parallel techniques on multiple AWS GPU servers to speed up Machine Learning (ML) training. Along […]
The purpose of this post is to show how to use multi-threading to parallelize data processing with data transfer from pageable to page-locked memory. I […]
Plotting its shape helps in understanding the properties and behaviour of a function. Unfortunately since we live in a 3D world, we can’t visualize functions […]
So you developed a cool AI algorithm and want to show it off through a web service? You know a lot about AI algorithms and […]
If you follow the hardware for deep learning space, you may have heard of the term “systolic array”. A 2D systolic array forms the heart […]
The purpose of this post is to discuss my current understanding of roofline charts. Let me lay some background first. Before I got into machine […]
Over the past few days, I have been investigating how SSD (Single Shot Detector), an object detector introduced in the following paper in Dec 2016 […]
The purpose of this post is to provide math proofs and clarify some implementation details in the recently introduced reinforcement learning method called “Trust Region […]
You may have noticed that weights for convolutional and fully connected layers in a deep neural network (DNN) are initialized in a specific way. For […]
In this post, I’ll describe in detail how R-CNN (Regions with CNN features), a recently introduced deep learning based object detection and classification method works. R-CNN’s […]
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