Amgad Muhammad

24 Abonné

Amgad has more than 11 years of experience with a professional altitude grounded in the cross-cutting knowledge and practice of both data science and engineering. With strong leadership and the capacity to work as a team player, Amgad helped teams to implement and deliver different machine learning systems utilizing stat-of-the-art deep learning architectures in different disciplines including computer vision and NLP. Amgad is also a published author focusing on helping software developers to build vision-aware and intelligent applications.

machine learning deep learning object detection cnn computer vision gpu cuda caffe dnn law of large numbers pca autoencoder best practices mobile development performance android unsupervised feature learning sparse-auto encoders gfs distributed file systems python dynamic typing

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Amgad Muhammad

Présentations

Python

Google File System

Unsupervised Feature Learning

Android Performance Best Practices

Auto-Encoders and PCA, a brief psychological background

CUDA and Caffe for deep learning

Improving region based CNN object detector using bayesian optimization

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