Bruce Lee random walk
数据科学提纲
预备知识
- 微积分⭐️
- 概率统计⭐️⭐️⭐️⭐️
- 线性代数⭐️⭐️⭐️⭐️
- 矩阵论⭐️⭐️
- 优化⭐️⭐️⭐️⭐️
- 运筹学
- 泛函分析／实分析／复分析
- 微分几何／代数几何／热带几何
- 流形／群论
- 测度论
- 信息论
书籍
- Eric Lehman, F Thomson Leighton and Albert R Meyer, Mathematics for Computer Science
- Gilbert Strang, Linear Algebra⭐️⭐️⭐️⭐️
- E. T. Jaynes, Probability Theory: The Logic of Science
- Larry Wasserman, All of Statistics⭐️⭐️⭐️
- Trevor Hastie, Robert Tibshirani and Jerome Friedman, The Elements of Statistical Learning: Data Mining, Inference, and Prediction⭐️⭐️⭐️
- Jiawei Han, Micheline Kamber and Jian Pei, Data Mining: Concepts and Techniques⭐️⭐️⭐️⭐️
- Christopher D. Manning, Prabhakar Raghavan and Hinrich Schutze, Introduction to Information Retrieval⭐️⭐️⭐️⭐️
- Avrim Blum, John Hopcroft and Ravindran Kannan, Foundations of Data Science⭐️⭐️⭐️⭐️
- Joel Grus, Data Science from Scratch First Principles with Python
- Anshul Joshi, Julia for Data Science
- Rachel Schutt and Cathy O’Neil, Doing Data Science
- Sebastian Gutierrez, Data Scientists at Work[That chapter written by Yann LeCun, especially]
- Amy N. langville and Carl D. Meyer, Google's PageRank and Beyond
- Stuart J. Russell and Peter Norvig, Artificial Intelligence: A Modern Approach⭐️⭐️
- Tom M. Mitchell, Machine Learning⭐️⭐️⭐️
- Christopher Bishop, Pattern Recognition and Machine Learning⭐️⭐️⭐️
- Kevin P. Murphy, Machine Learning: A Probabilistic Perspective⭐️⭐️⭐️⭐️
- Daphne Koller and Nir Friedman, Probalistic graphical Models: Principles and Techniques⭐️⭐️⭐️⭐️
- 周志华, 机器学习[西瓜书]⭐️⭐️⭐️
- 周志华, Ensemble Methods
- 李航, 统计机器学习⭐️⭐️⭐️
- Peter Harrington, Machine Learning in Action[中文版: 机器学习实战]⭐️⭐️⭐️
- Bradley Efron and Robert J. Tibshirani, An Introduction to the Bootstrap
- Stephen Boyd and Lieven Vandenberghe, Convex Optimization⭐️⭐️⭐️⭐️：优化领域的必读经典
- Michael Nielsen, Neural Networks and Deep Learning⭐️⭐️
- Li Deng and Dong Yu, Deep Learning: Methods and Applications
- Dong Yu and Li Deng, Automatic Speech Recognition: A Deep Learning Approach
- Li Deng and Dong Yu, Deep Learning For Signal And Information Processing
- Romain Couillet and Merouane Debbah, Random Matrix Methods for Wireless Communications
- Ian Goodfellow, Yoshua Bengio and Aaron Courville, Deep Learning⭐️⭐️⭐️⭐️⭐️：DL领域三代顶尖学者联合打造，包含basics, background, ML, optimization, regularization, practice, CNN(conv+pooling), RNN(LSTM/GRU), more research parts，内容层次适合各类研究兴趣
- Nikhil Ketkar, Deep Learning with Python⭐️⭐️:图文并茂，通俗易懂，很适合入门
- Francois Chollet, Deep Learning with Python⭐️⭐️⭐️:Keras开发者最新力作
实用教程
Python
数据科学
- Data Science Course⭐️⭐️⭐️
- Manifold Learning
- Data Vision
- 数据可视化工具Tableau
机器学习
神经网络
深度学习
-
UFLDL Tutorial⭐️⭐️⭐️⭐️
-
深度学习云平台FloydHub
-
Theories of Deep Learning(STATS 385) by David Donoho et al. ⭐️⭐️⭐️⭐️⭐️
博客汇总
视频课程
- 吴恩达，Machine Learning, Coursera⭐️⭐️⭐️⭐️⭐️
- Geoff Hinton，Neural Network
- Hugo Larochelle
- Stanford CS231n by Fei-fei Li et.al.
- Deep Learning Specialization by Andrew Ng⭐️⭐️⭐️⭐️
- Practical Deep Learning For Coders, Part1 by fast.ai
- Cutting Edge Deep Learning For Coders, Part 2 by fast.ai
MOOC
- Cousera
- edX
- Fields Institute
- Khan Academy
- MITOPENCOURSEWARE
- TED
- Udacity
- videolectures⭕️net
- 网易公开课
- 网易云课堂
会议&期刊
- AAAI
- ACL
- ACM
- AISTATS
- COLT
- CVPR
- ECCV
- ECML
- EMNLP
- ICASSP
- ICCV
- ICDE
- ICLR
- ICML
- IEEE
- IJCAI
- IJCV
- JMLR
- KDD
- Neural Computation
- Neural Networks
- NIPS
- TPAMI
- PyCon
- SciPy
- SIAM
- SIGGRAPH
- Strata
- UAI
数据集
模型
- LeNet
- AlexNet⭐️⭐️⭐️
- ZFNet
- GoogLeNet
- VGGNet
- ResNet⭐️⭐️
- WaveNet
框架
- Theano(LISA Lab)
- Caffe
- Caffe2(Facebook)
- TensorFlow(Google)
- Keras
- Lasagne
- MXNet(Amazon)
- MLlib: Spark
- DL4J: Java
- NLTK(Microsoft)
- Torch: Lua
- PyTorch
- Chainer
- PaddlePaddle(Baidu): Docker+Python/C++
- H2O
- Mocha: Julia
实例
- Detecting diabetic retinopathy in eye images
- Classification: Instant Recognition with Caffe
- Solving in Python with LeNet
- Deep learning with Julia (Flux.jl)
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