#

Dependencies#

docker                        5.0.3
docker-compose                1.25.5
fastapi                       0.75.2
keras                         2.8.0
matplotlib                    3.5.1
mlflow                        1.26.1
numpy                         1.22.4
optuna                        2.10.0
pandas                        1.4.2
pipenv                        2022.6.7
prefect                       2.0b5
scikit-learn                  1.0.2
seaborn                       0.11.2
tensorflow-datasets           4.5.2
tensorflow-macos              2.8.0
tensorflow-metal              0.4.0
torch                         2.0.0
torchaudio                    2.0.1
torchmetrics                  0.11.4
torchvision                   0.15.1
uvicorn                       0.17.6
xgboost                       1.6.0.dev0

Hardware#

GPU 0:                           Tesla P100-PCIE-16GB
CPU:                             Intel(R) Xeon(R) CPU @ 2.00GHz
Core:                            1
Threads per core:                2
L3 cache:                        38.5 MiB
Memory:                          15 Gb

References#

ALL18: Sanjeev Arora, Zhiyuan Li, and Kaifeng Lyu. Theoretical analysis of auto rate-tuning by batch normalization. CoRR, 2018. URL: http://arxiv.org/abs/1812.03981, arXiv:1812.03981.
AM16: Sanjeev Arora and Tengyu Ma. Back-propagation, an introduction. 12 2016. URL: http://www.offconvex.org/2016/12/20/backprop/.
Bar91: A. R. Barron. Approximation and estimation bounds for artificial neural networks. In Proceedings of the Fourth Annual Workshop on Computational Learning Theory, COLT '91, 243–249. San Francisco, CA, USA, 1991. Morgan Kaufmann Publishers Inc.
BDVJ03a: Yoshua Bengio, Réjean Ducharme, Pascal Vincent, and Christian Janvin. A neural probabilistic language model. J. Mach. Learn. Res., 3(null):1137–1155, mar 2003.
BDVJ03b: Yoshua Bengio, Réjean Ducharme, Pascal Vincent, and Christian Janvin. A neural probabilistic language model. J. Mach. Learn. Res., 3:1137–1155, March 2003. URL: http://dl.acm.org/citation.cfm?id=944919.944966.
BBBK11: J. S. Bergstra, R. Bardenet, Y. Bengio, and B. Kégl. Algorithms for Hyper-Parameter Optimization. Advances in Neural Information Processing Systems 24, 2011.
Cho21: François Chollet. Deep Learning with Python, Second Edition. Manning, 2021. ISBN 9781617296864.
CHM+15: Anna Choromanska, MIkael Henaff, Michael Mathieu, Gerard Ben Arous, and Yann LeCun. The Loss Surfaces of Multilayer Networks. In Guy Lebanon and S. V. N. Vishwanathan, editors, Proceedings of the Eighteenth International Conference on Artificial Intelligence and Statistics, volume 38 of Proceedings of Machine Learning Research, 192–204. San Diego, California, USA, 09–12 May 2015. PMLR. URL: https://proceedings.mlr.press/v38/choromanska15.html.
CUH16: Djork-Arné Clevert, Thomas Unterthiner, and Sepp Hochreiter. Fast and accurate deep network learning by exponential linear units (elus). In Yoshua Bengio and Yann LeCun, editors, ICLR (Poster). 2016. URL: http://dblp.uni-trier.de/db/conf/iclr/iclr2016.html#ClevertUH15.
DKB+20: Hadi Daneshmand, Jonas Kohler, Francis Bach, Thomas Hofmann, and Aurelien Lucchi. Batch normalization provably avoids rank collapse for randomly initialised deep networks. 2020. arXiv:2003.01652.
DPG+14: Yann N. Dauphin, Razvan Pascanu, Caglar Gulcehre, Kyunghyun Cho, Surya Ganguli, and Yoshua Bengio. Identifying and attacking the saddle point problem in high-dimensional non-convex optimization. In Proceedings of the 27th International Conference on Neural Information Processing Systems - Volume 2, NIPS'14, 2933–2941. Cambridge, MA, USA, 2014. MIT Press.
DCLT19: Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. Bert: pre-training of deep bidirectional transformers for language understanding. In Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), 4171–4186. 2019.
DHS11: John Duchi, Elad Hazan, and Yoram Singer. Adaptive subgradient methods for online learning and stochastic optimization. J. Mach. Learn. Res., 12(null):2121–2159, jul 2011.
Gan22: T. Ganegedara. TensorFlow in Action. Manning, 2022. ISBN 9781617298349. URL: https://books.google.com.ph/books?id=Hgh0zgEACAAJ.
Geron19: Aurélien Géron. Hands-on machine learning with Scikit-Learn and TensorFlow : concepts, tools, and techniques to build intelligent systems, Second Edition. O'Reilly Media, Sebastopol, CA, 2019. ISBN 978-1491962299.
GB10: Xavier Glorot and Yoshua Bengio. Understanding the difficulty of training deep feedforward neural networks. In Yee Whye Teh and Mike Titterington, editors, Proceedings of the Thirteenth International Conference on Artificial Intelligence and Statistics, volume 9 of Proceedings of Machine Learning Research, 249–256. Chia Laguna Resort, Sardinia, Italy, 13–15 May 2010. PMLR. URL: https://proceedings.mlr.press/v9/glorot10a.html.
GDG+17: Priya Goyal, Piotr Dollár, Ross Girshick, Pieter Noordhuis, Lukasz Wesolowski, Aapo Kyrola, Andrew Tulloch, Yangqing Jia, and Kaiming He. Accurate, large minibatch sgd: training imagenet in 1 hour. 2017. URL: https://arxiv.org/abs/1706.02677, doi:10.48550/ARXIV.1706.02677.
HZRS15: Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun. Delving deep into rectifiers: surpassing human-level performance on imagenet classification. CoRR, 2015. URL: http://arxiv.org/abs/1502.01852, arXiv:1502.01852.
HG16: Dan Hendrycks and Kevin Gimpel. Gaussian error linear units (gelus). arxiv, 2016. URL: http://arxiv.org/abs/1606.08415v3.
HS97: Sepp Hochreiter and Jürgen Schmidhuber. Flat minima. Neural Computation, 9(1):1–42, 1997. doi:10.1162/neco.1997.9.1.1.
HLW16: Gao Huang, Zhuang Liu, and Kilian Q. Weinberger. Densely connected convolutional networks. CoRR, 2016. URL: http://arxiv.org/abs/1608.06993, arXiv:1608.06993.
IS15: Sergey Ioffe and Christian Szegedy. Batch normalization: accelerating deep network training by reducing internal covariate shift. CoRR, 2015. URL: http://arxiv.org/abs/1502.03167, arXiv:1502.03167.
Kar22a: Andrej Karpathy. Building makemore part 2: mlp. 9 2022. URL: https://www.youtube.com/watch?v=TCH_1BHY58I.
Kar22b: Andrej Karpathy. The spelled-out intro to language modeling: building makemore. 9 2022. URL: https://www.youtube.com/watch?v=PaCmpygFfXo.
Kar22c: Andrej Karpathy. The spelled-out intro to neural networks and backpropagation: building micrograd. 8 2022. URL: https://www.youtube.com/watch?v=VMj-3S1tku0.
KMN+16a: Nitish Shirish Keskar, Dheevatsa Mudigere, Jorge Nocedal, Mikhail Smelyanskiy, and Ping Tak Peter Tang. On large-batch training for deep learning: generalization gap and sharp minima. CoRR, 2016. URL: http://arxiv.org/abs/1609.04836, arXiv:1609.04836.
KMN+16b: Nitish Shirish Keskar, Dheevatsa Mudigere, Jorge Nocedal, Mikhail Smelyanskiy, and Ping Tak Peter Tang. On large-batch training for deep learning: generalization gap and sharp minima. CoRR, 2016. URL: http://arxiv.org/abs/1609.04836, arXiv:1609.04836.
KB15: Diederik P. Kingma and Jimmy Ba. Adam: A method for stochastic optimization. In Yoshua Bengio and Yann LeCun, editors, 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings. 2015. URL: http://arxiv.org/abs/1412.6980.
KUMH17: Günter Klambauer, Thomas Unterthiner, Andreas Mayr, and Sepp Hochreiter. Self-normalizing neural networks. CoRR, 2017. URL: http://arxiv.org/abs/1706.02515, arXiv:1706.02515.
KSH12: Alex Krizhevsky, Ilya Sutskever, and Geoffrey E. Hinton. Imagenet classification with deep convolutional neural networks. In F. Pereira, C. J. C. Burges, L. Bottou, and K. Q. Weinberger, editors, Advances in Neural Information Processing Systems 25, pages 1097–1105. Curran Associates, Inc., 2012. URL: http://papers.nips.cc/paper/4824-imagenet-classification-with-deep-convolutional-neural-networks.pdf.
LXTG17: Hao Li, Zheng Xu, Gavin Taylor, and Tom Goldstein. Visualizing the loss landscape of neural nets. CoRR, 2017. URL: http://arxiv.org/abs/1712.09913, arXiv:1712.09913.
LLWT15: Ziwei Liu, Ping Luo, Xiaogang Wang, and Xiaoou Tang. Deep learning face attributes in the wild. In Proceedings of International Conference on Computer Vision (ICCV). December 2015.
Maa13: Andrew L. Maas. Rectifier nonlinearities improve neural network acoustic models. In Proceedings of the 30th International Conference on Machine Learning, volume 28. 2013.
Min69: S. Minsky, M. Papert. Perceptron: an introduction to computational geometry. The MIT Press, 1969.
Mis19: Diganta Misra. Mish: A self regularized non-monotonic neural activation function. CoRR, 2019. URL: http://arxiv.org/abs/1908.08681, arXiv:1908.08681.
MKS+15: Volodymyr Mnih, Koray Kavukcuoglu, David Silver, Andrei A. Rusu, Joel Veness, Marc G. Bellemare, Alex Graves, Martin Riedmiller, Andreas K. Fidjeland, Georg Ostrovski, and others. Human-level control through deep reinforcement learning. Nature, 518(7540):529–533, 2015.
NKB+19: Preetum Nakkiran, Gal Kaplun, Yamini Bansal, Tristan Yang, Boaz Barak, and Ilya Sutskever. Deep double descent: where bigger models and more data hurt. CoRR, 2019. URL: http://arxiv.org/abs/1912.02292, arXiv:1912.02292.
RLMD22: S. Raschka, Y. Liu, V. Mirjalili, and D. Dzhulgakov. Machine Learning with PyTorch and Scikit-Learn: Develop Machine Learning and Deep Learning Models with Python. Expert insight. Packt Publishing, 2022. ISBN 9781801819312. URL: https://books.google.com.ph/books?id=UHbNzgEACAAJ.
RM19: Sebastian Raschka and Vahid Mirjalili. Python Machine Learning, 3rd Ed. Packt Publishing, Birmingham, UK, 3rd edition, 2019. ISBN 978-1789955750.
STIM19: Shibani Santurkar, Dimitris Tsipras, Andrew Ilyas, and Aleksander Madry. How does batch normalization help optimization? 2019. arXiv:1805.11604.
SDV+16: Ramprasaath R. Selvaraju, Abhishek Das, Ramakrishna Vedantam, Michael Cogswell, Devi Parikh, and Dhruv Batra. Grad-cam: why did you say that? visual explanations from deep networks via gradient-based localization. CoRR, 2016. URL: http://arxiv.org/abs/1610.02391, arXiv:1610.02391.
SDBR14: Jost Tobias Springenberg, Alexey Dosovitskiy, Thomas Brox, and Martin Riedmiller. Striving for simplicity: the all convolutional net. 2014. URL: https://arxiv.org/abs/1412.6806, doi:10.48550/ARXIV.1412.6806.
SHK+14: Nitish Srivastava, Geoffrey E. Hinton, Alex Krizhevsky, Ilya Sutskever, and Ruslan Salakhutdinov. Dropout: a simple way to prevent neural networks from overfitting. Journal of Machine Learning Research, 15(1):1929–1958, 2014. URL: http://www.cs.toronto.edu/~rsalakhu/papers/srivastava14a.pdf.
Vie16: Tim Vieira. Evaluating ∇f(x) is as fast as f(x). 9 2016. URL: https://timvieira.github.io/blog/post/2016/09/25/evaluating-fx-is-as-fast-as-fx/.
Wai18: Elliot Waite. Pytorch autograd explained - in-depth tutorial. 11 2018. URL: https://www.youtube.com/watch?v=MswxJw-8PvE.
Wen18: Lilian Weng. From autoencoder to beta-vae. lilianweng.github.io, 2018. URL: https://lilianweng.github.io/posts/2018-08-12-vae/.
WH18: Yuxin Wu and Kaiming He. Group normalization. 2018. URL: https://arxiv.org/abs/1803.08494, doi:10.48550/ARXIV.1803.08494.
WJ21: Yuxin Wu and Justin Johnson. Rethinking "batch" in batchnorm. CoRR, 2021. URL: https://arxiv.org/abs/2105.07576, arXiv:2105.07576.
ZLLS21: Aston Zhang, Zachary C. Lipton, Mu Li, and Alexander J. Smola. Dive into deep learning. arXiv preprint arXiv:2106.11342, 2021.
ZKL+15: Bolei Zhou, Aditya Khosla, Agata Lapedriza, Aude Oliva, and Antonio Torralba. Learning deep features for discriminative localization. 2015. URL: https://arxiv.org/abs/1512.04150, doi:10.48550/ARXIV.1512.04150.
ZFM+20: Pan Zhou, Jiashi Feng, Chao Ma, Caiming Xiong, Steven Hoi, and E. Weinan. Towards theoretically understanding why sgd generalizes better than adam in deep learning. In Proceedings of the 34th International Conference on Neural Information Processing Systems, NIPS'20. Red Hook, NY, USA, 2020. Curran Associates Inc.

Contents

#

Dependencies#

Hardware#

References#