강의 참고 자료 모음 (논문, 깃헙 레포, 블로그 글 등)

[RyanKor]

Lecture 1 Reference

W​eek 2:

• Implementing a Neural Network from Scratch in Python – An Introduction (Denny Britz, 2015)

• Why normalize images by subtracting dataset's image mean, instead of the current image mean in deep learning? (Stack Exchange)

W​eek 3:

• Demystifying Deep Convolutional Neural Networks (Adam Harley)

• CS231n: Convolutional Neural Networks for Visual Recognition (Stanford University)

W​eek 4:

• Autoreload of modules in IPython (Stack Overflow)

---

Lecture 2 Reference

W​eek 3:

• Introduction to gradients and automatic differentiation (TensorFlow Documentation)

• tf.GradientTape (TensorFlow Documentation)

---

Lecture 4 Reference

W​eek 1:

• The Sequential model (TensorFlow Documentation)

• The Functional API (TensorFlow Documentation)

W​eek 2:

• Deep Residual Learning for Image Recognition (He, Zhang, Ren & Sun, 2015)

• d​eep-learning-models/resnet50.py/ (GitHub: fchollet)

• MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications (Howard, Zhu, Chen, Kalenichenko, Wang, Weyand, Andreetto, ​& Adam, 2017)

• MobileNetV2: Inverted Residuals and Linear Bottlenecks (Sandler, Howard, Zhu, Zhmoginov &Chen, 2018)

• EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks (Tan & Le, 2019)

W​eek 3:

• You Only Look Once: Unified, Real-Time Object Detection (Redmon, Divvala, Girshick & Farhadi, 2015)

• YOLO9000: Better, Faster, Stronger (Redmon & Farhadi, 2016)

• YAD2K (GitHub: allanzelener)

• YOLO: Real-Time Object Detection

• Fully Convolutional Architectures for Multi-Class Segmentation in Chest Radiographs (Novikov, Lenis, Major, Hladůvka, Wimmer & Bühler, 2017)

• Automatic Brain Tumor Detection and Segmentation Using U-Net Based Fully Convolutional Networks (Dong, Yang, Liu, Mo & Guo, 2017)

• U-Net: Convolutional Networks for Biomedical Image Segmentation (Ronneberger, Fischer & Brox, 2015)

W​eek 4:

• FaceNet: A Unified Embedding for Face Recognition and Clustering (Schroff, Kalenichenko & Philbin, 2015)

• DeepFace: Closing the Gap to Human-Level Performance in Face Verification (Taigman, Yang, Ranzato & Wolf)

• f​acenet (GitHub: davidsandberg)

• How to Develop a Face Recognition System Using FaceNet in Keras (Jason Brownlee, 2019)

• k​eras-facenet/notebook/tf_to_keras.ipynb (GitHub: nyoki-mtl)

• A Neural Algorithm of Artistic Style (Gatys, Ecker & Bethge, 2015)

• Convolutional neural networks for artistic style transfer

• TensorFlow Implementation of "A Neural Algorithm of Artistic Style"

• V​ery Deep Convolutional Networks For Large-Scale Image Recognition (Simonyan & Zisserman, 2015)

• Pretrained models (MatConvNet)

---

Lecture 5 Reference

W​eek 1:

• Minimal character-level language model with a Vanilla Recurrent Neural Network, in Python/numpy (GitHub: karpathy)

• The Unreasonable Effectiveness of Recurrent Neural Networks (Andrej Karpathy blog, 2015)

• d​eepjazz (GitHub: jisungk)

• Learning Jazz Grammars (Gillick, Tang & Keller, 2010)

• A Grammatical Approach to Automatic Improvisation (Keller & Morrison, 2007)

• Surprising Harmonies (Pachet, 1999)

W​eek 2:

• Man is to Computer Programmer as Woman is to Homemaker? Debiasing Word Embeddings (Bolukbasi, Chang, Zou, Saligrama​ & Kalai, 2016)

• GloVe: Global Vectors for Word Representation (Pennington, Socher & Manning, 2014)

• W​oebot

W​eek 4:

• Natural Language Processing Specialization (by DeepLearning.AI)

• Attention Is All You Need (Vaswani, Shazeer, Parmar, Uszkoreit, Jones, Gomez, Kaiser​ & Polosukhin, 2017)