This is a reading list to record daily study and reading about ML and related data analysis & tools.
- Machine Learning Fundamentals
- Deep Learning Fundamentals
- Nature Language Processing
- Speech Recognition
- Computer Vision
- Software & Tools
Please make sure that you understand the fundamental concepts and knowledge before you go deeper into specific topics.
- Zhou Zhihua. 2016. Machine Learning (Chinese Edition). Tsinghua University Press. Github repo for formula derivation.
- 南瓜书（PumpkinBook）. 周志华西瓜书的开源教辅。
- Christopher M. Bishop. 2006. Pattern Recognition and Machine Learning. Springer. Official Github Page and another repo for python code.
- 李航. 2012. 统计学习方法. Tsinghua University Press.
- Pedro Domingos. 2012. A Few Useful Things to Know about Machine Learning. University of Washington.
- Tianqi Chen, Carlos Guestrin. 2016. XGBoost: A Scalable Tree Boosting System. University of Washington. KDD 2016.
- Ian Goodfellow, Yoshua Bengio, Aaron Courville. 2016. Deep Learning. MIT Press.
- Jurgen Schmidhuber. 2014. Deep Learning in Neural Networks: An Overview. University of Lugano & SUPSI.
- Daniel Jurafsky, James H. Martin. 2018 (3rd edition). Speech and Language Processing. Standford University.
- Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N. Gomez, Lukasz Kaiser, and Illia Polosukhin. 2017. Attention is All You Need. NIPS 2017.
- Lawrence Rabiner, Biing-Hwang Juang. 1993. Fundamentals of Speech Recognition. Prentice Hall.
- Xuedong Huang, Alex Acero, Hslao-Wuen Hon. 2001. Spoken Language Processing: A Guide to Theory, Algorithm and System Development. Prentice Hall.
- Dong Yu, Li Deng. 2015. Automatic Speech Recogniton. Springer. 语音识别实践. 电子工业出版社.
- Alex Graves, Santiago Fernandez, Fautino Gomez, Juergen Schmidhuber. 2006. Connectionist Temporal Classification: Labelling Unsegmented Sequence Data with Recurrent Neural Networks. ICML 2006.
- Alex Graves. 2012. Supervised Sequence Labelling with Recurrent Neural Networks. Springer Berlin Heidelberg.
- Steve Young, Gunnar Evermann, Mark Gales, etc. 2015. The HTK Book (3.5alpha). Machine Intelligence Laboratory of the Cambridge University Engineering Department (CUED).
- Lawrence Rabiner. 1989. A Tutorial on Hidden Markov Models and Selected Applications in Speech Recognition. IEEE.
- Jeff Bilmes. 1998. A Gentle Tutorial of the EM Algorithm and its Application to Parameter Estimation for Gaussian Mixture and Hidden Markov Models. International Computer Science Institute.
- Rafal Jozefowicz, Oriol Vinyals, Mike Schuster, Noam Shazeer, Yonghui Wu. 2016. Exploring the Limits of Language Modeling. Google Brain.
- Naoyuki Kanda, Xugang Lu, Hisashi Kawai. 2016. Maximum A Posteriori based Decoding for CTC Acoustic Models. INTERSPEECH 2016.
- George E. Dahl, Dong Yu, Li Deng, and Alex Acero. 2012. Context-Dependent Pre-Trained Deep Neural Networks for Large-Vocabulary Speech Recognition. IEEE Transactions on Audio, Speech and Language Processing, Vol 20, 2012.
- Alex Graves , Navdeep Jaitly. 2014. Towards End-to-End Speech Recognition with Recurrent Neural Networks. Google Deepmind.
- Aaron van den Oord, Sander Dieleman, Heiga Zen, Karen Simonyan, Oriol Vinyals, Alex Graves, Nal Kalchbrenner, Andrew Senior, Koray Kavukcuoglu. 2016. WaveNet: A Generative Model for Raw Audio. Google.
- Geoffrey Hinton, Li Deng, Dong Yu, George E. Dahl, Abdel-rahman Mohamed, Navdeep Jaitly, Andrew Senior, Vincent Vanhoucke, Patrick Nguyen, Tara N. Sainath, and Brian Kingsbury. 2012. Deep Neural Networks for Acoustic Modeling in Speech Recognition. IEEE Singal Processing Magazine.
- Aurelien Geron. 2017. Hands-on Machine Learning with Scikit-Learn, Keras, and TensorFlow. O'Reilly.
- Martin Kleppmann. 2017. Designing Data-Intensive Applications. O'Reilly. Review