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What is this?
A Chinese-language deep learning tutorial — a huge curated collection of links to courses, books, lecture notes, surveys and code, taking you from math basics through CNNs, GANs, RNNs and GNNs to Kaggle practice and interview prep.
A Chinese-language deep learning tutorial — a huge curated collection of links to courses, books, lecture notes, surveys and code, taking you from math basics through CNNs, GANs, RNNs and GNNs to Kaggle practice and interview prep.Is this a course I work through, or a collection of links?It is an index, not a course itself: the README is a structured list of links to external courses, books, blog posts and repos, grouped by topic with short notes. You read it on GitHub and follow what you need.
It is an index, not a course itself: the README is a structured list of links to external courses, books, blog posts and repos, grouped by topic with short notes. You read it on GitHub and follow what you need.Is the material still current?Treat it as a snapshot: the repo was assembled in 2018 and most linked surveys and write-ups date from 2019-2020 — solid for fundamentals, but check newer sources for fast-moving areas. It also hosts links only; nothing is bundled.
Treat it as a snapshot: the repo was assembled in 2018 and most linked surveys and write-ups date from 2019-2020 — solid for fundamentals, but check newer sources for fast-moving areas. It also hosts links only; nothing is bundled.Who benefits most from it?Self-learners starting out in machine learning, students following the referenced university courses, Kaggle newcomers, and candidates preparing for algorithm-engineer interviews at internet companies.
Self-learners starting out in machine learning, students following the referenced university courses, Kaggle newcomers, and candidates preparing for algorithm-engineer interviews at internet companies.Does it include runnable code, or only articles?Both. Alongside article links there are practice sections with implementations: PyTorch ResNet, TensorFlow Object Detection API tutorials, a minimal NumPy graph-convolution implementation, PyTorch-UNet and Keras GAN demos.
Both. Alongside article links there are practice sections with implementations: PyTorch ResNet, TensorFlow Object Detection API tutorials, a minimal NumPy graph-convolution implementation, PyTorch-UNet and Keras GAN demos.What is the AiLearning project linked at the top?A companion repo the README recommends as the base of the learning route, covering classical machine learning, deep learning and NLP in Chinese.
A companion repo the README recommends as the base of the learning route, covering classical machine learning, deep learning and NLP in Chinese.Which university courses does it collect notes for?Andrew Ng's Coursera ML and sequence-model courses, Stanford CS229, CS230, CS231n, CS224n and CS234, MIT's 2019 deep learning course, and NTU professor Hung-yi Lee's lectures — many with Chinese notes or translated subtitles.
Andrew Ng's Coursera ML and sequence-model courses, Stanford CS229, CS230, CS231n, CS224n and CS234, MIT's 2019 deep learning course, and NTU professor Hung-yi Lee's lectures — many with Chinese notes or translated subtitles.How deep does the CNN section go?Very deep: history surveys, architecture walkthroughs from LeNet-5 to EfficientNet, anchor-free detectors, NMS and mAP metrics, focal loss, and lightweight networks like MobileNet and ShuffleNet.
Very deep: history surveys, architecture walkthroughs from LeNet-5 to EfficientNet, anchor-free detectors, NMS and mAP metrics, focal loss, and lightweight networks like MobileNet and ShuffleNet.What are similar projects?AI-For-Beginners, TensorFlow-Examples, pytorch-handbook, Machine-Learning-Tutorials, ailearning, d2l-en, annotated_deep_learning_paper_implementations, Ai-Learn
AI-For-Beginners, TensorFlow-Examples, pytorch-handbook, Machine-Learning-Tutorials, ailearning, d2l-en, annotated_deep_learning_paper_implementations, Ai-LearnWhich topics is it in?machinelearning, mxnet, word2vec, rnn, lstm, courses, graph-neural-networks, kaggle, deep-learning-tutorial, semantic-segmentation, attention, generative-adversarial-network
machinelearning, mxnet, word2vec, rnn, lstm, courses, graph-neural-networks, kaggle, deep-learning-tutorial, semantic-segmentation, attention, generative-adversarial-network