Applied Deep Learning 2023 - Lecture 10 - Serving, Optimizing, and Practical Aspects
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Complete Playlist: https://www.youtube.com/playlist?list=PLNsFwZQ_pkE87JO3T_mvedVTlw0sjUzKh == Literature == 1. Seide et al. 1-Bit Stochastic Gradient Descent and Application to Data-Parallel Distributed Training of Speech DNNs, 2014. 2. Micikevicius, Mixed-Precision Training of Deep Neural Networks, 2017. 3. Addair, What is the difference between FP16 and FP32, 2018. 4. Tensor Processing Unit on Wikipedia. 5. Goyal et al. Accurate, Large Minibatch SGD: Training ImageNet in 1 Hour, 2018. 6. Kim et al. PyTorch Gradual Warmup LR on Github. 7. TensorFlow Data Performance Guide. 8. Prakash, Serving a deep learning model in production using tensorflow-serving, Medium, 2019. 9. Ma et al. Moving Deep Learning into Web Browser: How Far Can We Go?, 2019. 10. Gu et al. Distributed Machine Learning on Mobile Devices: A Survey, 2019. 11. Ramanujan et al. What’s Hidden in a Randomly Weighted Neural Network?, 2019. 12. Frankle et al. Stabilizing the Lottery Ticket Hypothesis, 2019. 13. Singh. Pruning Deep Neural Networks, Medium, 2019. 14. Frankle, Carbin, The Lottery Ticket Hypothesis: Finding Sparse, Trainable Neural Networks, 2019. 15. Wilkinson et al. The FAIR Guiding Principles for scientific data management and stewardship, 2016. 16. FAIR Principles website. https://www.go-fair.org/fair-principles/
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