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Kaoutar El Maghraoui's Projects

aihwkit icon aihwkit

IBM Analog Hardware Acceleration Kit

analog-nas icon analog-nas

Analog AI Neural Architecture Search (analog-nas) is a modular and flexible framework to facilitate implementation of Analog-aware Neural Architecture Search.

awesome-quantization-papers icon awesome-quantization-papers

List of papers related to neural network quantization in recent AI conferences and journals (please refer to README in the folder for a better view).

cifar-10 icon cifar-10

An IPython notebook demonstrating the process of Transfer Learning using pre-trained Convolutional Neural Networks with Keras on the popular CIFAR-10 Image Classification dataset.

cuda-training-series icon cuda-training-series

Training materials associated with NVIDIA's CUDA Training Series (www.olcf.ornl.gov/cuda-training-series/)

dlaas-getting-startedtutorial icon dlaas-getting-startedtutorial

A Getting Started Tutorial to get you quick-started on using the IBM Watson Studio DLaaS (Deep Learning as a Service) via the Command Line Interface (CLI). Covers basic pre-requisites, creating a Machine Learning Instance, Cloud Object Storage, etc. and a simple demo.

dlaas-getting-startedtutorial-1 icon dlaas-getting-startedtutorial-1

This repo is for the IBM MIT AI Lab to use DLaaS in IBM Watson Studio. The demo will use Pytorch to train VGG for CIFAR10. The code is forked from kuangliu (https://github.com/kuangliu/pytorch-cifar) and adapted for submitting the model to IBM Watson Machine Learning on Watson Studio for training. It is meant to get you quick-started. We hope you have some fun running your first models in IBM Cloud

livelossplot icon livelossplot

Live training loss plot in Jupyter Notebook for Keras, PyTorch and others

product-line-prediction icon product-line-prediction

Created for toolchain: https://console.ng.bluemix.net/devops/toolchains/d86a270e-42fb-487a-bf25-8a015bbd5b98?env_id=ibm%3Ayp%3Aus-south

transformers icon transformers

🤗Transformers: State-of-the-art Natural Language Processing for Pytorch and TensorFlow 2.0.

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