More Machine Learning Tricks, Recipes, and Statistical Models

Source for picture: proper right here

The first part of this itemizing was printed proper right here. These are articles that I wrote in the last few years. The full sequence will attribute articles related to the following aspects of machine finding out:

• Mathematics, simulations, benchmarking algorithms based totally on synthetic info (briefly, experimental info science)
• Opinions, as an illustration regarding the value of a PhD in our topic, or the utilization of some strategies
• Methods, guidelines, pointers of thumb, recipes, suggestions
• Business analytics 
• Core Techniques 

My articles are always written in straightforward English and accessible to professionals with typically one yr of calculus or statistical teaching, on the undergraduate stage. They are geared in course of people who use info nonetheless are fascinating in gaining further smart analytical experience. Managers and willpower makers are part of my meant viewers. The kind is compact, geared in course of people who should not have quite a lot of free time. 

Despite these restrictions, state-of-the-art, of-the-beaten-path outcomes along with machine finding out commerce secrets and techniques and strategies and evaluation supplies are usually shared. References to further superior literature (from myself and totally different authors) is obtainable for a lot of who must dig deeper inside the topics talked about. 

1. Machine Learning Tricks, Recipes and Statistical Models

These articles give consideration to strategies which have massive features or which is likely to be in another case elementary or seminal in nature.

1. One Trillion Random Digits
2. New Perspective on the Central Limit Theorem and Statistical Testing
3. Simple Solution to Feature Selection Problems
4. Scale-Invariant Clustering and Regression
5. Deep Dive into Polynomial Regression and Overfitting
6. Stochastic Processes and New Tests of Randomness – Application to Cool Number Theory Problem
7. A Simple Introduction to Complex Stochastic Processes – Part 2
8. A Simple Introduction to Complex Stochastic Processes
9. High Precision Computing: Benchmark, Examples, and Tutorial
10. Logistic Map, Chaos, Randomness and Quantum Algorithms
11. Graph Theory: Six Degrees of Separation Problem
12. Interesting Problem for Serious Geeks: Self-correcting Random Walks
13. 9 Off-the-beaten-path Statistical Science Topics with Interesting Applications
14. Data Science Method to Discover Large Prime Numbers
15. Nice Generalization of the Okay-NN Clustering Algorithm –  Also Useful for Data Reduction
16. How to Detect if Numbers are Random or Not
17. How and Why: Decorrelate Time Series
18. Distribution of Arrival Times of Extreme Events
19. Why Zipf’s regulation explains so many large info and physics phenomenons

2. Free books

• Statistics: New Foundations, Toolbox, and Machine Learning Recipes

  Available proper right here. In about 300 pages and 28 chapters it covers many new topics, offering a current perspective on the subject, along with pointers of thumb and recipes which is likely to be easy to automate or mix in black-box strategies, along with new model-free, data-driven foundations to statistical science and predictive analytics. The technique focuses on sturdy strategies; it is bottom-up (from features to concept), in distinction to the conventional top-down technique.

  The supplies is accessible to practitioners with a one-year college-level publicity to statistics and probability. The compact and tutorial kind, that features many features with fairly just a few illustrations, is aimed towards practitioners, researchers, and executives in various quantitative fields.

• Applied Stochastic Processes

  Available proper right here. Full title: Applied Stochastic Processes, Chaos Modeling, and Probabilistic Properties of Numeration Systems (104 pages, 16 chapters.) This e-book is supposed for professionals in info science, laptop computer science, operations evaluation, statistics, machine finding out, large info, and arithmetic. In 100 pages, it covers many new topics, offering a current perspective on the subject.

  It is accessible to practitioners with a two-year college-level publicity to statistics and probability. The compact and tutorial kind, that features many features (Blockchain, quantum algorithms, HPC, random amount expertise, cryptography, Fintech, web crawling, statistical testing) with fairly just a few illustrations, is aimed towards practitioners, researchers and executives in various quantitative fields.

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About the creator:  Vincent Granville is a data science pioneer, mathematician, e-book creator (Wiley), patent proprietor, former post-doc at Cambridge University, former VC-funded authorities, with 20+ years of firm experience along with CNET, NBC, Visa, Wells Fargo, Microsoft, eBay. Vincent can be self-publisher at DataShaping.com, and primarily based and co-founded just some start-ups, along with one with a worthwhile exit (Data Science Central acquired by Tech Target). He recently opened Paris Restaurant, in Anacortes. You can entry Vincent’s articles and books, proper right here.