{"id":21373372,"url":"https://github.com/gunhochoi/deep-learning-for-beginners","last_synced_at":"2026-01-02T20:43:20.611Z","repository":{"id":104650353,"uuid":"78887216","full_name":"GunhoChoi/Deep-Learning-For-Beginners","owner":"GunhoChoi","description":"videos, lectures, blogs for Deep Learning","archived":false,"fork":false,"pushed_at":"2018-10-16T14:32:43.000Z","size":331,"stargazers_count":96,"open_issues_count":0,"forks_count":30,"subscribers_count":10,"default_branch":"master","last_synced_at":"2025-01-22T21:16:15.846Z","etag":null,"topics":["convolutional-neural-networks","deep-learning","generative-adversarial-network","recurrent-neural-networks"],"latest_commit_sha":null,"homepage":"","language":null,"has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":null,"status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/GunhoChoi.png","metadata":{"files":{"readme":"Readme.md","changelog":null,"contributing":null,"funding":null,"license":null,"code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null,"governance":null,"roadmap":null,"authors":null,"dei":null,"publiccode":null,"codemeta":null}},"created_at":"2017-01-13T21:15:43.000Z","updated_at":"2024-12-16T08:51:04.000Z","dependencies_parsed_at":null,"dependency_job_id":"6a300706-01cd-4c0c-85fd-b89d8dbdb5fa","html_url":"https://github.com/GunhoChoi/Deep-Learning-For-Beginners","commit_stats":null,"previous_names":[],"tags_count":0,"template":false,"template_full_name":null,"repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/GunhoChoi%2FDeep-Learning-For-Beginners","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/GunhoChoi%2FDeep-Learning-For-Beginners/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/GunhoChoi%2FDeep-Learning-For-Beginners/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/GunhoChoi%2FDeep-Learning-For-Beginners/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/GunhoChoi","download_url":"https://codeload.github.com/GunhoChoi/Deep-Learning-For-Beginners/tar.gz/refs/heads/master","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":243847060,"owners_count":20357317,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2022-07-04T15:15:14.044Z","host_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub","repositories_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories","repository_names_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repository_names","owners_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners"}},"keywords":["convolutional-neural-networks","deep-learning","generative-adversarial-network","recurrent-neural-networks"],"created_at":"2024-11-22T08:27:52.586Z","updated_at":"2026-01-02T20:43:20.580Z","avatar_url":"https://github.com/GunhoChoi.png","language":null,"funding_links":[],"categories":[],"sub_categories":[],"readme":"# Deep Learning for beginners\n\n\u003cimg src=\"lion.jpg\" width=\"60%\"\u003e\n  \n2016년 8월부터 딥러닝공부를 하면서 봤던 강의영상, 동영상, 블로그들의 목록입니다.\n\n# What is Deep Learning ?\n\n1. Deep Learning introduced by Nvidia (https://www.youtube.com/watch?v=C2FS9WVm7j4)\n2. Deep Learrning Roadmap (https://github.com/songrotek/Deep-Learning-Papers-Reading-Roadmap)\n3. What is deep learning (http://machinelearningmastery.com/what-is-deep-learning/)\n\n# Installation\n\n1. Azure server NV series install (https://docs.microsoft.com/en-us/azure/virtual-machines/linux/n-series-driver-setup)\n\n# Libraries\n\n1. Tensorflow (https://www.tensorflow.org/)\n2. Tensorflow Cookbook (https://github.com/nfmcclure/tensorflow_cookbook)\n3. CNTK (https://github.com/Microsoft/CNTK, https://www.microsoft.com/en-us/research/product/cognitive-toolkit/)\n4. CNTK Tutorial (https://notebooks.azure.com/library/cntkbeta2)\n5. Keras Pretrained Models (https://github.com/fchollet/keras/blob/master/docs/templates/applications.md)\n6. Keras Blog (https://blog.keras.io/index.html)\n7. Python Torch tutorial (https://github.com/yunjey/pytorch-tutorial)\n8. Incredible Pytorch (https://github.com/ritchieng/the-incredible-pytorch)\n9. Caffe2 (https://caffe2.ai/)\n\n# Machine Learning Basics\n\n1. 딥러닝과 관련된 개념들 (https://www.youtube.com/playlist?list=PLjJh1vlSEYgvGod9wWiydumYl8hOXixNu)\n2. Andrew NG 교수님의 Coursera 강의 (https://www.coursera.org/learn/machine-learning)\n3. Ian goodfellow의 책 (https://github.com/HFTrader/DeepLearningBook)\n4. Numpy-100 Tutorial (https://github.com/rougier/numpy-100)\n5. Numpy tutorial (http://www.dataquest.io/blog/numpy-tutorial-python/?utm_source=mybridge\u0026utm_medium=blog\u0026utm_campaign=read_more)\n6. Kaggle 1st place for 2 years (http://course.fast.ai/lessons/lesson1.html)\n7. 아니 이 많은걸 언제 다 정리하셨대 (https://handong1587.github.io/index.html)\n8. Experiments about ReLU/LeakyReLu/PReLU (https://arxiv.org/pdf/1505.00853.pdf)\n9. Hyperparameter optimization (https://arimo.com/data-science/2016/bayesian-optimization-hyperparameter-tuning/)\n10. FastAI Linear Algebra (https://github.com/fastai/numerical-linear-algebra)\n\n# General Neural Networks\n\n1. 열한줄로 뉴럴넷 짜보기 (https://iamtrask.github.io/2015/07/12/basic-python-network/)\n2. 한단계 한단계 Back propagation에 대한 친절한 설명 (https://mattmazur.com/2015/03/17/a-step-by-step-backpropagation-example/)\n3. Batch Normalization (https://kratzert.github.io/2016/02/12/understanding-the-gradient-flow-through-the-batch-normalization-layer.html)\n4. Gradient Descent Optimization Algorithm 비교 (http://sebastianruder.com/optimizing-gradient-descent/)\n5. Adagrad, Adadelta,RMSProp,Adam (http://prinsphield.github.io/2016/02/04/An%20Overview%20on%20Optimization%20Algorithms%20in%20Deep%20Learning%20(II)/)\n\n# Convolutional Neural Networks\n\n1. CNN을 쉽게 이해하도록 도와준 영상 (https://youtu.be/FmpDIaiMIeA, https://brohrer.github.io/how_convolutional_neural_networks_work.html)\n2. 그 유명한 cs231n 강의 (https://www.youtube.com/playlist?list=PLkt2uSq6rBVctENoVBg1TpCC7OQi31AlC)\n3. 그 유명한 cs231n 강의노트 (http://cs231n.github.io/)\n4. 한글로 설명이 잘되어있는 라온피플 블로그 (http://laonple.blog.me/220463627091) \n5. 시각화된 Convolution의 작동 (https://github.com/vdumoulin/conv_arithmetic)\n6. 강의자 Andrej Kaparthy의 볼게 많은 블로그 (http://cs.stanford.edu/people/karpathy/)\n7. 명화의 화풍을 따라 그리는 Neural Style (http://www.anishathalye.com/2015/12/19/an-ai-that-can-mimic-any-artist/, https://github.com/cysmith/neural-style-tf, https://www.youtube.com/watch?v=N14_w2RG1A8)\n8. 레이어별로 뉴런의 Activation 및 반응을 볼 수 있는 자료 (https://github.com/yosinski/deep-visualization-toolbox)\n9. Google Deepdream (https://github.com/google/deepdream)\n10. 2016 No.1 ResNet (https://github.com/KaimingHe/deep-residual-networks)\n11. Transposed Convoultion의 문제점과 해결방안 (http://distill.pub/2016/deconv-checkerboard/) \n12. 자료들이 모여있는 Awesome Deep vision (https://github.com/kjw0612/awesome-deep-vision)\n13. ResNet in Tensorflow (https://github.com/ry/tensorflow-resnet)\n14. ResNet, DenseNet (https://chatbotslife.com/resnets-highwaynets-and-densenets-oh-my-9bb15918ee32#.rbzbvof9l)\n15. Spatial Transformer Network (https://github.com/fxia22/stn.pytorch)\n16. Filtered image after convolution (http://setosa.io/ev/image-kernels/)\n17. Convolution Transposed (https://arxiv.org/pdf/1603.07285.pdf)\n18. LeNet to ResNet (http://slazebni.cs.illinois.edu/spring17/lec01_cnn_architectures.pdf,http://vision.stanford.edu/teaching/cs231b_spring1415/slides/alexnet_tugce_kyunghee.pdf)\n19. 2017 cs21n (http://cs231n.stanford.edu/)\n20. Convolution function as matrix multiplication (https://nrupatunga.github.io/convolution-2/)\n21. Depth-wise Seperable Convolution (https://www.youtube.com/watch?v=T7o3xvJLuHk)\n\n# Detection \u0026 Semantic Segmentation\n\n1. Fully Convolutional Network for Semantic Segmentation (https://github.com/shekkizh/FCN.tensorflow)\n2. Faster R-CNN (https://github.com/rbgirshick/py-faster-rcnn)\n3. Semantic Flow segmentation (https://ps.is.tuebingen.mpg.de/research_projects/semantic-optical-flow, https://ps.is.tuebingen.mpg.de/uploads_file/attachment/attachment/261/semanticflow.pdf)\n4. Image Segmentation (http://warmspringwinds.github.io/tensorflow/tf-slim/2016/12/18/image-segmentation-with-tensorflow-using-cnns-and-conditional-random-fields/)\n5. Localization \u0026 Detection gitbook (https://leonardoaraujosantos.gitbooks.io/artificial-inteligence/content/object_localization_and_detection.html)\n6. Image Processing in classical ways(?)(https://www.giassa.net/?page_id=118) \n7. All about segmentation (https://github.com/mrgloom/Semantic-Segmentation-Evaluation)\n8. Tensorflow Faster R-CNN (https://github.com/endernewton/tf-faster-rcnn)\n9. Deeplab Resnet Tensorflow (https://github.com/DrSleep/tensorflow-deeplab-resnet)\n10. Segmentation Overview (https://meetshah1995.github.io/semantic-segmentation/deep-learning/pytorch/visdom/2017/06/01/semantic-segmentation-over-the-years.html)\n\n# Unsupervised Learning\n\n1.  Semi-supervised Learning (http://rinuboney.github.io/2016/01/19/ladder-network.html, https://github.com/CuriousAI/ladder)\n\n# Autoencoder\n\n1. 김범준씨의 Variational Autoencoder의 번역 (http://nolsigan.com/blog/what-is-variational-autoencoder/)\n1. Generating Large Images from Latent Vectors (http://blog.otoro.net/2016/04/01/generating-large-images-from-latent-vectors/, https://arxiv.org/pdf/1512.09300.pdf)\n2. Variational Autoencoder (https://www.youtube.com/watch?v=BiWRaES2WN0\u0026t=991s, http://blog.fastforwardlabs.com/2016/08/12/introducing-variational-autoencoders-in-prose-and.html, https://github.com/kvfrans/variational-autoencoder)\n\n\n# Generative Adversarial Networks\n\n1. Adversarial Nets papers (https://github.com/zhangqianhui/AdversarialNetsPapers)\n2. Generative Adversarial Networks by OpenAI (https://openai.com/blog/generative-models/)\n3. 김태훈씨의 쉽게 설명한 DCGAN in Tensorflow (http://www.slideshare.net/carpedm20/pycon-korea-2016, https://github.com/carpedm20/DCGAN-tensorflow)\n4. 간단한 GAN 설명과 동영상 예시 (http://keunwoochoi.blogspot.kr/)\n5. 이미지의 빈부분을 채우는 GAN (http://bamos.github.io/2016/08/09/deep-completion/, https://github.com/bamos/dcgan-completion.tensorflow)\n6. 텍스트를 이미지로 바꾸는 GAN text-to-image (https://github.com/reedscot/icml2016)\n7. GAN video generation (http://web.mit.edu/vondrick/tinyvideo/)\n8. DCGAN Tutorial (https://medium.com/@awjuliani/generative-adversarial-networks-explained-with-a-classic-spongebob-squarepants-episode-54deab2fce39#.gdxkk32d7)\n9. InfoGAN Tutorial (https://medium.com/emergent-future/learning-interpretable-latent-representations-with-infogan-dd710852db46#.9iaqd4it5)\n10. DiscoGAN in Pytorch (https://github.com/carpedm20/DiscoGAN-pytorch)\n11. Wiseodd GANs (https://github.com/wiseodd/generative-models)\n12. DiscoGAN official (https://github.com/SKTBrain/DiscoGAN)\n13. CycleGAN tutorial (https://hardikbansal.github.io/CycleGANBlog/)\n\n# Recurrent Neural Networks\n\n1. RNN에 대한 친절한 설명 (https://iamtrask.github.io/2015/11/15/anyone-can-code-lstm/)\n2. Andrej Kaparthy RNN의 활용가능성 (http://karpathy.github.io/2015/05/21/rnn-effectiveness/)\n3. Image caption generator in Tensorflow (https://github.com/tensorflow/models/tree/master/im2txt)\n4. Awesome RNN (https://github.com/kjw0612/awesome-rnn)\n5. Pytorch RNN (https://github.com/spro/practical-pytorch)\n6. LSTM experiments (http://blog.echen.me/2017/05/30/exploring-lstms/)\n7. Attention Mechanism in RNN (https://www.youtube.com/watch?v=QuvRWevJMZ4)\n8. Stanford CS224d(https://github.com/DSKSD/DeepNLP-models-Pytorch)\n\n# NLP\n\n1. CS224d for NLP (https://youtu.be/Qy0oEkCZkBI?list=PLlJy-eBtNFt4CSVWYqscHDdP58M3zFHIG)\n2. Oxford Deep NLP (https://github.com/oxford-cs-deepnlp-2017/lectures)\n3. Seq2seq TF1.0 code (https://github.com/ematvey/tensorflow-seq2seq-tutorials)\n4. Denny Britz Seq2seq (https://github.com/google/seq2seq)\n5. Pytorch for NLP tutorial (https://github.com/rguthrie3/DeepLearningForNLPInPytorch) \n6. Practical Pytorch for NLP (https://github.com/spro/practical-pytorch)\n\n# Word2vec\n\n1. Word2vec이 필요한 이유와 코드 공식사이트 번역본 (http://khanrc.tistory.com/entry/TensorFlow-6-word2vec-Theory, http://khanrc.tistory.com/entry/TensorFlow-7-word2vec-Implementation)\n2. Chris Mccormick의 Word2vec 설명 (http://mccormickml.com/tutorials/)\n3. 한국어와 NLTK, Gensim에 대한 박은정씨의 발표 (https://www.lucypark.kr/slides/2015-pyconkr/#1)\n4. Genism tutorial (https://radimrehurek.com/gensim/models/word2vec.html)\n5. Kaggle word2vec tutorial (https://www.kaggle.com/c/word2vec-nlp-tutorial/details/part-1-for-beginners-bag-of-words)\n6. Word2vec의 역사(http://sebastianruder.com/word-embeddings-1/)\n\n# Reinforcement Learning\n\n1. Simple Reinforcement Learning with Tensorflow by Arthur Juliani (https://medium.com/emergent-future/simple-reinforcement-learning-with-tensorflow-part-0-q-learning-with-tables-and-neural-networks-d195264329d0#.hegtvglmg)\n2. Udacity Self Driving Car Simulator (https://github.com/udacity/self-driving-car-sim)\n3. UC Berkeley RL (http://rll.berkeley.edu/deeprlcourse/)\n4. Denny Britz RL (http://www.wildml.com/2016/10/learning-reinforcement-learning/, https://github.com/dennybritz/reinforcement-learning)\n5. RL Derivatives (http://www.alexirpan.com/rl-derivations/)\n\n# Visualization\n\n1. t-SNE (https://www.analyticsvidhya.com/blog/2017/01/t-sne-implementation-r-python/, http://distill.pub/2016/misread-tsne/)\n2. t-SNE 저자 설명 (https://www.youtube.com/watch?v=EMD106bB2vY)\n3. MNIST 시각화 (http://colah.github.io/posts/2014-10-Visualizing-MNIST/)\n4. Tensorboard 예시 (https://github.com/normanheckscher/mnist-tensorboard-embeddings)\n5. How to use t-SNE effectively (http://distill.pub/2016/misread-tsne/)\n6. CAM:Class Activation Map (http://cnnlocalization.csail.mit.edu/)\n7. CAM:Class Activation Map 한글설명 (http://tmmse.xyz/2016/04/10/object-localization-with-weakly-supervised-learning/)\n8. Grad-CAM Pytorch(https://github.com/jacobgil/pytorch-grad-cam)\n9. Grad-CAM Visualization(https://ramprs.github.io/2017/01/21/Grad-CAM-Making-Off-the-Shelf-Deep-Models-Transparent-through-Visual-Explanations.html)\n10. Optimizer Visualization(https://github.com/wassname/viz_torch_optim)\n\n# Data Augmentation\n\n1. Data Augmentation with Keras api (http://machinelearningmastery.com/image-augmentation-deep-learning-keras/)\n2. Winner of Galaxy zoo (http://benanne.github.io/2014/04/05/galaxy-zoo.html)\n3. Elastic Deformation (https://gist.github.com/chsasank/4d8f68caf01f041a6453e67fb30f8f5a)\n4. Elastic Deformation2 (https://www.kaggle.com/bguberfain/ultrasound-nerve-segmentation/elastic-transform-for-data-augmentation)\n5. Image Data Augmentations (https://github.com/aleju/imgaug) \n6. Scipy Lectures (http://www.scipy-lectures.org/index.html#)\n\n# Ensemble\n\n1. Snapshot Ensembles: Train 1, get M for free (https://arxiv.org/abs/1704.00109)\n\n\n# Attention + Classification\n\n1. Residual Attention Network for Image Classification (http://arxiv.org/abs/1704.06904)\n2. Learn To Pay Attention (http://arxiv.org/abs/1804.02391)\n3. Tell Me Where to Look: Guided Attention Inference Network (https://arxiv.org/abs/1802.10171)\n\n# Blogs \u0026 Gist\n\n1. Fast Forward Labs (http://blog.fastforwardlabs.com/)\n2. Variational Autoencoder (http://oduerr.github.io/talks/)\n3. Google Experiments (https://aiexperiments.withgoogle.com/)\n4. Deep learning 2016 summary(https://tryolabs.com/blog/2016/12/06/major-advancements-deep-learning-2016/)\n5. Brandon Amos Blog (https://bamos.github.io/)\n6. Hvass_lab_tutorials (https://github.com/Hvass-Labs/TensorFlow-Tutorials)\n7. Tensorflow Queue and Threads (https://blog.metaflow.fr/tensorflow-how-to-optimise-your-input-pipeline-with-queues-and-multi-threading-e7c3874157e0#.fbfqfygsm)\n8. How to read images using tf.queue (https://gist.github.com/eerwitt/518b0c9564e500b4b50f)\n9. Sungjoon choi's blog (http://enginius.tistory.com/)\n10. Openresearch.ai(http://openresearch.ai/)\n11. Why Denoising?(https://thecuriousaicompany.com/another-test-learning-by-denoising-part-1-what-and-why-of-denoising/)\n\n# Awesome Series\n\n1. Awesome2vec (https://github.com/MaxwellRebo/awesome-2vec)\n2. Awesome Bayesian Deep Learning (https://github.com/robi56/awesome-bayesian-deep-learning)\n\n# Mathematics for Deep Learning\n\n1. Essence of Linear Algebra (https://www.youtube.com/playlist?list=PLZHQObOWTQDPD3MizzM2xVFitgF8hE_ab)\n2. 공돌이의 수학정리노트 (https://wikidocs.net/book/563)\n3. Brilliant.org (https://brilliant.org/)\n4. Cross Entropy Loss \u0026 KL divergence (http://rdipietro.github.io/friendly-intro-to-cross-entropy-loss/)\n5. PRML by Bishop in Korean (http://norman3.github.io/prml/)\n6. Mathematical Tour in Python (http://www.numerical-tours.com/python/)\n7. Statistical Distributions (http://hamelg.blogspot.kr/2015/11/python-for-data-analysis-part-22.html)\n8. PRML algorithms implemented in Python (https://github.com/ctgk/PRML)\n9. Bloomberg Foundation of Machine Learning (https://bloomberg.github.io/foml/#lectures)\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fgunhochoi%2Fdeep-learning-for-beginners","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fgunhochoi%2Fdeep-learning-for-beginners","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fgunhochoi%2Fdeep-learning-for-beginners/lists"}