“Advanced member reading list”版本间的差异

2015年7月10日 (五) 02:36的版本

Machine Learning

• Deep learning tutotials, LISA lab, University of Montreal
• Deep learning, methods and applications, Li Deng, MS
• Introduction to ML, Nils J. Nilsson, Stanford
• Introduction to Machine Learning, Ethem Alpaydın
• Machine Learning course given by Tom Mitchell
• Neural Networks, A comprehensive foundation, Simon Haykin
• Neumerical optimization, Jorge Nocedal, Stephen J. Wright
• Conjugate Gradient Algorithms in Nonconvex Optimization, Radosaw Pytlak
• Bayesian Reasoning and Machine Learning, David Barber
• Machine Learning, an algorithm perspective, Stephen Marsland
• Neural Petworks for Pattern Recognition, Christopher M. Bishop
• Learning Deep Architectures for AI, Yoshua Bengio

Speech Processing

General

• Spring Handbook of Speech Processing, Ed. Jacob Benesty, M. Mohan Sondhi, Yiteng Huang
• Spoken Language Processing, X.D. Huang,

Acoustics

• Introduction to Digital Signal Processing, Lawrence R. Rabiner and Ronald W. Schafer
• Foundation of acoustics
• Spring Topics in Signal Processing, Microphone Array Processing, Benesty, J.; Chen, J.; Huang, Y
• Signal and System

ASR

• The Application of Hidden Markov Models in Speech Recognition
• Deeplearninginneuralnetworks:Anoverview

TTS

SID

Language Processing

• Speech and Language Processing
• Foundation of Natural Language Processing

Finance Processing

• Applied Time Series Modeling and Forecasting