“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:
see also: /dl-do-it
MIXED TUTORIAL + TRIKCS
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- 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
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ToC:
一 ~ 三、概述, 背景, 人脑视觉机理
四、关于特征: 特征表示的粒度; 初级（浅层）特征表示; 结构性特征表示; 需要有多少个特征？
五 ~ 七、Deep Learning 的基本思想 (vs. Shallow Learning), Neural Network
八、Deep learning 训练过程: 传统神经网络 vs. deep learning
九、Deep Learning 的常用模型或者方法: AutoEncoder 自动编码器; Sparse Coding 稀疏编码; Restricted Boltzmann Machine(RBM) 受限波尔兹曼机; Deep BeliefNetworks 深信度网络; Convolutional Neural Networks 卷积神经网络
十、总结与展望
十一、参考文献和 Deep Learning 学习资源
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