Category Archives: Thoughts
November 3, 2013Posted by on
- Linear Algebra and Its Applications by Gilbert Strang (Cengage Learning)
- Convex Optimization by Stephen Boyd and Lieven Venden‐berghe (Cambridge University Press)
- A First Course in Probability (Pearson) and Introduction to Probability Models (Academic Press) by Sheldon Ross
- R in a Nutshell by Joseph Adler (O’Reilly)
- Learning Python by Mark Lutz and David Ascher (O’Reilly)
- R for Everyone: Advanced Analytics and Graphics by Jared Lander (Addison-Wesley)
- The Art of R Programming: A Tour of Statistical Software Design by Norman Matloff (No Starch Press)
- Python for Data Analysis by Wes McKinney (O’Reilly) Data Analysis and Statistical Inference
- Statistical Inference by George Casella and Roger L. Berger (Cengage Learning)
- Bayesian Data Analysis by Andrew Gelman, et al. (Chapman & Hall)
- Data Analysis Using Regression and Multilevel/Hierarchical Models by Andrew Gelman and Jennifer Hill (Cambridge University Press)
- Advanced Data Analysis from an Elementary Point of View by Cosma Shalizi (under contract with Cambridge University Press)
- The Elements of Statistical Learning: Data Mining, Inference and Prediction by Trevor Hastie, Robert Tibshirani, and Jerome Friedman (Springer)
Artificial Intelligence and Machine Learning
- Pattern Recognition and Machine Learning by Christopher Bishop (Springer)
- Bayesian Reasoning and Machine Learning by David Barber (Cambridge University Press)
- Programming Collective Intelligence by Toby Segaran (O’Reilly)
- Artificial Intelligence: A Modern Approach by Stuart Russell and Peter Norvig (Prentice Hall)
- Foundations of Machine Learning by Mehryar Mohri, Afshin Rostamizadeh, and Ameet Talwalkar (MIT Press)
- Introduction to Machine Learning (Adaptive Computation and Machine Learning) by Ethem Alpaydim (MIT Press)
- Field Experiments by Alan S. Gerber and Donald P. Green (Norton)
- Statistics for Experimenters: Design, Innovation, and Discovery by George E. P. Box, et al. (Wiley-Interscience)
- The Elements of Graphing Data by William Cleveland (Hobart Press)
- Visualize This: The FlowingData Guide to Design, Visualization, and Statistics by Nathan Yau (Wiley)
Pinterst Screenshot http://www.pinterest.com/dangleebits/books/
November 1, 2013Posted by on
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September 26, 2013Posted by on
September 14, 2013Posted by on
August 14, 2013Posted by on
I been reading and wishing I could start using “Deep Learning” to classify network traffic. There’s been a lot of talk over deep learning in the last year in the ML community. I recently watched Peter Norvig on the latest updates and how Google in using deep neural networks and how they are beating conventional algorithms in their voice, video and image classification applications.
I’m excited to hear that malware detection, speech recognition, computer vision, and molecular activity prediction are all early adopters. I’m excited to start see network security vendors join the gaggle. The idea of neural networks is hardly new but today’s neural networks can efficiently process many more neurons, with many more layers, than before. Thanks to improvements in CPU and GPU technologies.
Geoff Hinton introduced a new algorithm which allows for efficiently training larger and deeper neural networks than in the past.
I’m hoping that NN will provide an upper hand for InfoSec.
August 10, 2013Posted by on
July 11, 2013Posted by on
Prescriptive analytics is the third phase of business analytics, a decision-modelling system for industry. The first stage is descriptive analytics, which looks at past issues and describes them; it tells you what happened and why after the fact. The second stage is predictive analytics, which combines historical data with predictive algorithms to guess the probability of future events; it tells you what will happen. But prescriptive analytics claims to go even further. It applies a multitude of business rule algorithms, multiple mathematical and computational modeling systems to automatically synthesize hybrid data sets and answer not only what will happen but what also needs to be done about it. Put another way, prescriptive analytics continuously and automatically tries to anticipates the what, when, and why of unknown future events. And it has the potential to be scary accurate.
June 28, 2013Posted by on