ML-Reading-List

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

Machine Learning Fundamentals

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.

Deep Learning Fundamentals

• 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.

Nature Language Processing

• 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.

Speech Recognition

• 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.

Computer Vision

Software & Tools

• 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