The goal of Session 3 is to understand 'why GPUs are necessary for AI' through both theory and hands-on experience. In Session 1, we observed the state where 'the GPU is visible but cannot be used,' ...
A library of open datasets for data analytics/machine learning compiled by HackerNoon. Data powers machine learning algorithms and scikit-learn or sklearn offers high quality datasets that are widely ...
This study proposes voltage-dependent-synaptic plasticity (VDSP), a novel brain-inspired unsupervised local learning rule for the online implementation of Hebb’s plasticity mechanism on neuromorphic ...
This repository contains a PyTorch implementation of the Lottery Ticket algorithm introduced by Frankle et al. in "The Lottery Ticket Hypothesis: Finding Sparse, Trainable Neural Networks" [1] and ...
Emerging two-terminal nanoscale memory devices, known as memristors, have demonstrated great potential for implementing energy-efficient neuro-inspired computing architectures over the past decade. As ...
Dr. James McCaffrey of Microsoft Research demonstrates how to fetch and prepare MNIST data for image recognition machine learning problems. Many machine learning problems fall into one of three ...
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