Cambridge
-
Machine Learning for Speaker Recognition (Hardcover)$3,340$3,273 -
90折
$2,322Foundations of Probabilistic Programming -
VIP 95折
Computational Approaches to the Network Science of Teams$2,330$2,214 -
VIP 95折
Data-Driven Computational Neuroscience: Machine Learning and Statistical Models$3,240$3,078 -
A First Course in Random Matrix Theory: For Physicists, Engineers and Data Scientists (Hardcover)$1,700$1,666 -
VIP 95折
Linear Algebra for Everyone (Hardcover)$2,800$2,660 -
90折
$2,340Essentials of Pattern Recognition: An Accessible Approach -
VIP 95折
Small Summaries for Big Data$2,490$2,366 -
VIP 95折
Learning Scientific Programming with Python 2/e$2,070$1,967 -
VIP 95折
European Societies, Migration, and the Law: The 'Others' Amongst 'Us'$5,040$4,788 -
VIP 95折
Sentiment Analysis: Mining Opinions, Sentiments, and Emotions, 2/e (Hardcover)$3,480$3,306 -
VIP 95折
Potential Theory and Geometry on Lie Groups$7,580$7,201 -
Processing Networks: Fluid Models and Stability (Hardcover)$1,260$1,235 -
VIP 95折
Numerical Methods in Physics with Python (Hardcover)$4,570$4,342 -
VIP 95折
The Kestrel: Ecology, Behaviour and Conservation of an Open-Land Predator$2,880$2,736 -
VIP 95折
Matrix Positivity$5,280$5,016 -
VIP 95折
Analysis and Geometry on Graphs and Manifolds$4,400$4,180 -
VIP 95折
Electronic Structure: Basic Theory and Practical Methods, 2/e (dhl)$3,660$3,477 -
VIP 95折
About Shakespeare: Bodies, Spaces and Texts$1,130$1,074 -
VIP 95折
Smartphones Within Psychological Science$3,690$3,506 -
$1,813Modern Quantum Mechanics, 3/e (Hardcover) -
VIP 95折
How to Write Good Programs: A Guide for Students$900$855 -
VIP 95折
Bandit Algorithms$2,200$2,090 -
VIP 95折
Regression and Other Stories (Paperback)$2,100$1,995 -
Combinatorial Mathematics (Hardcover)$1,680$1,646 -
VIP 95折
Structured Dependence Between Stochastic Processes$5,400$5,130 -
Foundations of Astrophysics (Hardcover)$1,520$1,490 -
VIP 95折
Algorithm Design with Haskell$2,600$2,470 -
Multimedia Learning, 3/e (Paperback)$1,980$1,940 -
90折
$1,764The Art of Feature Engineering: Essentials for Machine Learning