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本文转载自:深度学习与计算机视觉
我们的计算机视觉学习路径框架
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目标:这个月你会学到什么?关键要点是什么?你的计算机视觉之旅将如何进行?我们会在每个月初提及此问题,以确保你知道该月底的立场以及所处的位置 -
建议时间:你每周平均应在该部分上花费多少时间 -
学习资源:该月你将学习的计算机视觉主题的顶级资源集合,其中包括文章,教程,视频,研究论文和其他类似资源
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https://discuss.analyticsvidhya.com/t/heres-your-learning-path-to-master-computer-vision-in-2020/87785
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2020年成为数据科学家和掌握机器学习的学习之路 -
https://www.analyticsvidhya.com/blog/2020/01/learning-path-data-scientist-machine-learning-2020 -
2020年掌握深度学习的学习道路 -
https://www.analyticsvidhya.com/blog/2020/01/comprehensive-learning-path-deep-learning-2020 -
自然语言处理(NLP)学习路径 -
https://www.analyticsvidhya.com/blog/2020/01/learning-path-nlp-2020
第1个月 – 涵盖基础知识:Python与统计
OpenCV中文官方教程v4.1(可选):
http://woshicver.com
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https://courses.analyticsvidhya.com/courses/introduction-to-data-science
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https://www.khanacademy.org/math/engageny-alg-1/alg1-2
第2个月 – 使用机器学习解决图像分类问题
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机器学习基础 -
https://www.analyticsvidhya.com/blog/2015/06/machine-learning-basics/ -
sklearn中文官方教程0.22.1(可选): -
http://sklearn123.com -
线性回归 -
https://www.analyticsvidhya.com/blog/2017/06/a-comprehensive-guide-for-linear-ridge-and-lasso-regression/ -
逻辑回归 -
https://www.analyticsvidhya.com/blog/2015/10/basics-logistic-regression/ -
斯坦福大学-机器学习的动机与应用 -
https://see.stanford.edu/Course/CS229/47 -
斯坦福大学的“过拟合”和“过拟合”的概念 -
https://see.stanford.edu/Course/CS229/42
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从图像中提取特征的3种技术 -
https://www.analyticsvidhya.com/blog/2019/08/3-techniques-extract-features-from-image-data-machine-learning-python/ -
HOG特征 -
https://www.analyticsvidhya.com/blog/2019/09/feature-engineering-images-introduction-hog-feature-descriptor/ -
SIFT特征 -
https://www.analyticsvidhya.com/blog/2019/10/detailed-guide-powerful-sift-technique-image-matching-python/
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使用逻辑回归进行图像分类 -
https://www.kaggle.com/gulsahdemiryurek/image-classification-with-logistic-regression -
使用Logistic回归进行图像分类 -
https://mmlind.github.io/Using_Logistic_Regression_to_solve_MNIST/
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https://datahack.analyticsvidhya.com/contest/practice-problem-identify-the-apparels/
第三个月 – Keras和神经网络简介
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Keras文档 -
https://keras.io/ -
使用Keras构建神经网络 -
https://www.analyticsvidhya.com/blog/2016/10/tutorial-optimizing-neural-networks-using-keras-with-image-recognition-case-study/
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从零开始的神经网络 -
https://www.analyticsvidhya.com/blog/2017/05/neural-network-from-scratch-in-python-and-r/ -
斯坦福大学神经网络简介 -
https://youtu.be/d14TUNcbn1k -
3Blue1Brown的神经网络: -
https://youtu.be/aircAruvnKk
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https://datahack.analyticsvidhya.com/contest/practice-problem-identify-the-apparels/
第4个月 – 了解卷积神经网络(CNN),迁移学习和参加比赛
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卷积神经网络(CNN)简化 -
https://www.analyticsvidhya.com/blog/2017/06/architecture-of-convolutional-neural-networks-simplified-demystified -
斯坦福大学的卷积神经网络: -
https://youtu.be/bNb2fEVKeEo
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掌握迁移学习 -
https://www.analyticsvidhya.com/blog/2017/06/transfer-learning-the-art-of-fine-tuning-a-pre-trained-model -
斯坦福大学实践中的ConvNets: -
https://youtu.be/dUTzeP_HTZg
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DataHack -
https://datahack.analyticsvidhya.com/contest/all -
Kaggle -
https://www.kaggle.com/competitions
第5个月 – 解决对象检测问题
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目标检测技术的分步介绍 -
https://www.analyticsvidhya.com/blog/2018/10/a-step-by-step-introduction-to-the-basic-object-detection-algorithms-part-1 -
实现faster RCNN用于目标检测 -
https://www.analyticsvidhya.com/blog/2018/11/implementation-faster-r-cnn-python-object-detection -
使用YOLO进行物体检测 -
https://www.analyticsvidhya.com/blog/2018/12/practical-guide-object-detection-yolo-framewor-python -
斯坦福大学的物体检测: -
https://youtu.be/nDPWywWRIRo -
YOLO论文 -
https://arxiv.org/pdf/1506.02640.pdf -
YOLO预训练模型 -
https://pjreddie.com/darknet/yolo/
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数脸挑战 -
https://datahack.analyticsvidhya.com/contest/vista-codefest-computer-vision-1 -
COCO物体检测挑战 -
http://cocodataset.org/#download
第6个月 – 了解图像分割和注意力模型
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图像分割技术的分步介绍 -
https://www.analyticsvidhya.com/blog/2019/04/introduction-image-segmentation-techniques-python -
实现Mask R-CNN进行图像分割 -
https://www.analyticsvidhya.com/blog/2019/07/computer-vision-implementing-mask-r-cnn-image-segmentation -
Mask R-CNN论文 -
https://arxiv.org/pdf/1703.06870.pdf -
Mask R-CNN GitHub存储库 -
https://github.com/matterport/Mask_RCNN
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http://cocodataset.org/#download
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Sequence-to-Sequence Modeling with Attention -
https://www.analyticsvidhya.com/blog/2018/03/essentials-of-deep-learning-sequence-to-sequence-modelling-with-attention-part-i -
Sequence-to-Sequence Models by Stanford -
https://nlp.stanford.edu/~johnhew/public/14-seq2seq.pdf
第7个月 – 探索深度学习工具
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PyTorch教程 -
https://pytorch.org/tutorials/ -
PyTorch的初学者友好指南 -
https://www.analyticsvidhya.com/blog/2019/09/introduction-to-pytorch-from-scratch
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PyTorch中文官方教程 (可选) -
http://pytorch123.com
TensorFlow:
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TensorFlow教程 -
https://www.tensorflow.org/tutorials -
TensorFlow简介 -
https://www.analyticsvidhya.com/blog/2016/10/an-introduction-to-implementing-neural-networks-using-tensorflow
第8个月 – 了解NLP和图像字幕的基础
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斯坦福-词嵌入: -
https://youtu.be/ERibwqs9p38 -
递归神经网络(RNN)简介: -
https://youtu.be/UNmqTiOnRfg -
RNN教程 -
http://www.wildml.com/2015/10/recurrent-neural-networks-tutorial-part-3-backpropagation-through-time-and-vanishing-gradients/
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自动图像字幕 -
https://cs.stanford.edu/people/karpathy/sfmltalk.pdf -
使用深度学习的图像字幕 -
https://www.analyticsvidhya.com/blog/2018/04/solving-an-image-captioning-task-using-deep-learning
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http://cocodataset.org/#download
第9个月 – 熟悉生成对抗网络(GAN)
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Ian Goodfellow的生成对抗网络(GAN): -
https://youtu.be/HGYYEUSm-0Q -
GAN 论文 -
https://arxiv.org/pdf/1406.2661.pdf -
生成对抗网络的最新进展 -
https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=8667290 -
Keras-GAN -
https://github.com/eriklindernoren/Keras-GAN
第10个月 – 视频分析简介
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计算视频中演员的放映时间 -
https://www.analyticsvidhya.com/blog/2018/09/deep-learning-video-classification-python -
建立视频分类模型 -
https://www.analyticsvidhya.com/blog/2019/09/step-by-step-deep-learning-tutorial-video-classification-python -
通过视频进行人脸检测 -
https://www.analyticsvidhya.com/blog/2018/12/introduction-face-detection-video-deep-learning-python
第11个月和第12个月 – 解决项目并参加竞赛
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数字识别器 -
https://www.kaggle.com/c/digit-recognizer -
ImageNet对象定位挑战 -
https://www.kaggle.com/c/imagenet-object-localization-challenge -
年龄检测 -
https://datahack.analyticsvidhya.com/contest/practice-problem-age-detection -
空中仙人掌鉴定 -
https://www.kaggle.com/c/aerial-cactus-identification -
超声神经分割 -
https://www.kaggle.com/c/ultrasound-nerve-segmentation -
对抗性攻击防御 -
https://www.kaggle.com/c/nips-2017-defense-against-adversarial-attack/overview
信息图– 2020年计算机视觉学习之路
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