“if you plan to use deep learning extensively (>150 hrs/mo), building your own deep learning workstation might be the right move.” [medium]
Baidu AI Studio (only for PaddlePaddle) Paperspace (cooperating with fast.ai) Google Colab (cooperating with fast.ai) vast.ai (C2C/P2P sharing, very cheap, a lot of time to init/load/unload) Kaggle (max 6h, good GPU but complex steps to use) MS Azure Amazon FloydHub (special CLI interface) ref:
CN intro: Paperspace vs. Colab, 2019 Best Deals in Deep Learning Cloud Providers, 2018 比较云GPU平台
see also: /dl-theory
Data # 不得不赞！一个国内（可能）最好的海量 CV 数据集获取网站 Tutorials # 图像分类任务中的 tricks 总结 (bak) 邱锡鹏 教授 2020 神经网络与深度学习
Keywords: CNN, RNN, Attention, Gaussian Mix., RBM, DBN, GAN, RL, TF, PyTorch: equation, pseudocode, exercise …
PDF, PPT, code, exercise & solution, etc. A collection of various DL deep learning architectures, models, and tips for TensorFlow and PyTorch in Jupyter Notebooks 2019 《TensorFlow 2.0 深度学习算法实战 2019》 (pdf version via Python 数据科学) ICCV 2019 教程: DL SOTA by MXNET & GluonCV, [github] [cn intor] CS294-158 Deep Unsupervised Learning, 14 weeks, slides & youtube, Spring 2019 (overview in CN) fast.
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see also: /dl-do-it
ML Interpratation, Comprehensibility & Causality MIXED TUTORIAL + TRIKCS # Google: TF, DL, Tutorial, Tricks, etc The Ultimate Course and Book list to be an expert in Mathematics and Programming (Content: Discrete Mathematics, Linear Algebra, Calculus, Probability, Cryptography, Geometry and Statistics. 45 Mathematics Courses) Deep Learning and Reinforcement Learning Summer School 2018 - Yoshua Bengio etc. 深度学习课程笔记 by Tess Ferrandez, recommended by Andrew Ng PAPERS, TERMS & DEFINATIONS # 一文读懂自注意力机制：8 大步骤图解+代码 ref: Illustrated: Self-Attention – Step-by-step guide to self-attention with illustrations and code [典型/经典 DL/NLP 模型] 读完这 45 篇论文，“没人比我更懂 AI 了” 如何开启深度学习之旅？这三大类 125 篇论文为你导航 Github DL papers DeepLearningBook/DeepLearningPapers.
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