Applied Deep Learning 2024 - Lecture 10 - Serving, Optimizing, and Practical Aspects

Описание: What happens after you've trained a machine learning model? It's pretty worthless, unless you can run in and serve it properly to some users. This is what we're looking at today. How we can take our amazing models and bring it all the way to our customers through releases, docker images, or platforms that do almost all the work for us. Apart from this, we're also discussing a few more practical aspects and how we can optimize our models even further.

Complete Playlist:    • Applied Deep Learning 2024 - TU Wien  

== 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/