神经网络- 吴恩达Andrew Ng CNN卷积神经网络 论文等资料汇总
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课程总结
跟Andrew Ng大神学习卷积神经网络,最终用Yolo v3 实现了物体实时检测,自动驾驶会用到
笔者文章:一步一步手写实现实时监测物体YOLO v3 EASY METHOD | OpenCV Python CNN卷积神经网络
并合成了图片:
感谢老师,受益良多。
课程链接 :https://www.deeplearning.ai/program/deep-learning-specialization/
参考论文等资料汇总
Week 1:
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The Sequential model (TensorFlow Documentation)
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The Functional API (TensorFlow Documentation)
Week 2:
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Deep Residual Learning for Image Recognition (He, Zhang, Ren & Sun, 2015)
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deep-learning-models/resnet50.py/ (GitHub: fchollet)
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MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications (Howard, Zhu, Chen, Kalenichenko, Wang, Weyand, Andreetto, & Adam, 2017)
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MobileNetV2: Inverted Residuals and Linear Bottlenecks (Sandler, Howard, Zhu, Zhmoginov &Chen, 2018)
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EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks (Tan & Le, 2019)
Week 3:
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You Only Look Once: Unified, Real-Time Object Detection (Redmon, Divvala, Girshick & Farhadi, 2015)
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YOLO9000: Better, Faster, Stronger (Redmon & Farhadi, 2016)
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YAD2K (GitHub: allanzelener)
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Fully Convolutional Architectures for Multi-Class Segmentation in Chest Radiographs (Novikov, Lenis, Major, Hladůvka, Wimmer & Bühler, 2017)
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Automatic Brain Tumor Detection and Segmentation Using U-Net Based Fully Convolutional Networks (Dong, Yang, Liu, Mo & Guo, 2017)
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U-Net: Convolutional Networks for Biomedical Image Segmentation (Ronneberger, Fischer & Brox, 2015)
Week 4:
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FaceNet: A Unified Embedding for Face Recognition and Clustering (Schroff, Kalenichenko & Philbin, 2015)
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DeepFace: Closing the Gap to Human-Level Performance in Face Verification (Taigman, Yang, Ranzato & Wolf)
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facenet (GitHub: davidsandberg)
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How to Develop a Face Recognition System Using FaceNet in Keras (Jason Brownlee, 2019)
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keras-facenet/notebook/tf_to_keras.ipynb (GitHub: nyoki-mtl)
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A Neural Algorithm of Artistic Style (Gatys, Ecker & Bethge, 2015)
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TensorFlow Implementation of “A Neural Algorithm of Artistic Style”
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Very Deep Convolutional Networks For Large-Scale Image Recognition (Simonyan & Zisserman, 2015)
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Pretrained models (MatConvNet)
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