Delve into the world of deep learning with a focus on utilizing GPUs for enhanced performance. Explore topics like neural networks, TensorFlow, PyTorch, and distributed training. Learn how deep learning algorithms process data, optimize weights and biases, and predict outcomes through training loops

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Discover the diverse range of DiCOS Apps available, from Bio-Apps like cryoSPARC and Relion to Phys-Apps such as Paraview and Ovito. Explore machine learning tools like PyTorch and Tensorflow, and learn about managing disk space for different user groups. Access examples on opening Jupyter with RTX

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Explore machine learning concepts from Python basics to advanced neural network implementations using Scikit-learn and PyTorch. This comprehensive guide provides step-by-step explanations, code examples, and practical insights for beginners in the field. Covering topics such as data visualization, N

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Exploring learning-based low-rank approximations and linear sketches in matrices, including techniques like dimensionality reduction, regression, and streaming algorithms. Discusses the use of random matrices, sparse matrices, and the concept of low-rank approximation through singular value decompos

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Adri Priadana's weekly report details recent activities like courses on machine learning, doctoral thesis research, and face recognition experiments using PyTorch. Updates on FasterNet Block, Split-based Inception Block, ResNet architecture modifications for improved face recognition accuracy are in

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Seastar presents a vertex-centric programming approach for Graph Neural Networks, showcasing better performance in graph analytic tasks compared to traditional methods. The research introduces the SEAStar computation pattern and discusses GNN programming abstractions, execution, and limitations. Dee

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