Jun Wu was a staff research scientist in Google who invented Google’s Chinese, Japanese, and Korean Web Search Algorithms and was responsible for many Google machine learning projects. He wrote official blogs introducing Google technologies behind its products in very simple languages for Chinese internet users from 2006-2010. The blogs had more than two million followers. He received Ph.D. in computer science from the Johns Hopkins University and had been working on speech recognition and natural language processing for more than 20 years. He was one of the earliest engineers of Google, managed many products of the company, and was awarded more than ten US patents during his ten-year tenure there. He became a full-time VC investor and co-founded Amino Capital in Palo Alto in 2014 and is the author of eight books.

"This volume originates from a series of blog articles by the author, who works as senior staff research scientist for Google China. The blog articles have been rewritten to make them more accessible to uninitiated readers. As a result, the book contains 29 chapters which may be read independently. The aim is to provide evidence for the beauty of mathematics and the wealth of its applications to the layman . . . The volume may be quite valuable for readers who want to get some insight into how enterprises like Google achieve their performance, and how much mathematics is at work in the background of many commonplace services . . . "~Dieter Riebesehl (Lüneburg), zbMath

• Words, languages vs. numbers, information. Natural language processing: from rules to statistics. Statistical language models. Chinese, Japanese, and Korean Word segmentation. Hidden Markov models. Measurement and usage of information. Fred Jelinek and modern natural language processing. Beauty of simplicity: Boolean algebra and search engines. Graph theory and web crawlers. PageRank–Google’s democratic ranking algorithm. Determing the relevance of webpages and queries. Finite state machines and dynamic programming: Core technologies of Google local search. Cosine similarity and news classification. Matrix calculation and clustering of text documents. Information fingerprints and their applications. Mathematical principles of cryptography. All that is gold does not glitter: search engine anti-SPAM. The importance of mathematical models. Don’t put all your eggs in one basket: maximum entropy modeling. The principle of (Chinese pinyin) input method editor. Bloom filter. Bayesian networks: Extensions of hidden Markov models. Conditional random field, syntactic parsing, and other applications. Viterbi and his algorithm. God algorithm: Expectation-maximization algorithms. Logistic regression and web search advertisement. Divide and conquer and Google cloud computing fundamentals. Google Brain and neural networks. The power of big data.

Jun Wu was a staff research scientist in Google who invented Google’s Chinese, Japanese, and Korean Web Search Algorithms and was responsible for many Google machine learning projects. He wrote official blogs introducing Google technologies behind its products in very simple languages for Chinese internet users from 2006-2010. The blogs had more than two million followers. He received Ph.D. in computer science from the Johns Hopkins University and had been working on speech recognition and natural language processing for more than 20 years. He was one of the earliest engineers of Google, managed many products of the company, and was awarded more than ten US patents during his ten-year tenure there. He became a full-time VC investor and co-founded Amino Capital in Palo Alto in 2014 and is the author of eight books.

"This volume originates from a series of blog articles by the author, who works as senior staff research scientist for Google China. The blog articles have been rewritten to make them more accessible to uninitiated readers. As a result, the book contains 29 chapters which may be read independently. The aim is to provide evidence for the beauty of mathematics and the wealth of its applications to the layman . . . The volume may be quite valuable for readers who want to get some insight into how enterprises like Google achieve their performance, and how much mathematics is at work in the background of many commonplace services . . . "~Dieter Riebesehl (Lüneburg), zbMath

• Words, languages vs. numbers, information. Natural language processing: from rules to statistics. Statistical language models. Chinese, Japanese, and Korean Word segmentation. Hidden Markov models. Measurement and usage of information. Fred Jelinek and modern natural language processing. Beauty of simplicity: Boolean algebra and search engines. Graph theory and web crawlers. PageRank–Google’s democratic ranking algorithm. Determing the relevance of webpages and queries. Finite state machines and dynamic programming: Core technologies of Google local search. Cosine similarity and news classification. Matrix calculation and clustering of text documents. Information fingerprints and their applications. Mathematical principles of cryptography. All that is gold does not glitter: search engine anti-SPAM. The importance of mathematical models. Don’t put all your eggs in one basket: maximum entropy modeling. The principle of (Chinese pinyin) input method editor. Bloom filter. Bayesian networks: Extensions of hidden Markov models. Conditional random field, syntactic parsing, and other applications. Viterbi and his algorithm. God algorithm: Expectation-maximization algorithms. Logistic regression and web search advertisement. Divide and conquer and Google cloud computing fundamentals. Google Brain and neural networks. The power of big data.

Jun Wu was a staff research scientist in Google who invented Google’s Chinese, Japanese, and Korean Web Search Algorithms and was responsible for many Google machine learning projects. He wrote official blogs introducing Google technologies behind its products in very simple languages for Chinese internet users from 2006-2010. The blogs had more than two million followers. He received Ph.D. in computer science from the Johns Hopkins University and had been working on speech recognition and natural language processing for more than 20 years. He was one of the earliest engineers of Google, managed many products of the company, and was awarded more than ten US patents during his ten-year tenure there. He became a full-time VC investor and co-founded Amino Capital in Palo Alto in 2014 and is the author of eight books.

"This volume originates from a series of blog articles by the author, who works as senior staff research scientist for Google China. The blog articles have been rewritten to make them more accessible to uninitiated readers. As a result, the book contains 29 chapters which may be read independently. The aim is to provide evidence for the beauty of mathematics and the wealth of its applications to the layman . . . The volume may be quite valuable for readers who want to get some insight into how enterprises like Google achieve their performance, and how much mathematics is at work in the background of many commonplace services . . . "~Dieter Riebesehl (Lüneburg), zbMath

• Words, languages vs. numbers, information. Natural language processing: from rules to statistics. Statistical language models. Chinese, Japanese, and Korean Word segmentation. Hidden Markov models. Measurement and usage of information. Fred Jelinek and modern natural language processing. Beauty of simplicity: Boolean algebra and search engines. Graph theory and web crawlers. PageRank–Google’s democratic ranking algorithm. Determing the relevance of webpages and queries. Finite state machines and dynamic programming: Core technologies of Google local search. Cosine similarity and news classification. Matrix calculation and clustering of text documents. Information fingerprints and their applications. Mathematical principles of cryptography. All that is gold does not glitter: search engine anti-SPAM. The importance of mathematical models. Don’t put all your eggs in one basket: maximum entropy modeling. The principle of (Chinese pinyin) input method editor. Bloom filter. Bayesian networks: Extensions of hidden Markov models. Conditional random field, syntactic parsing, and other applications. Viterbi and his algorithm. God algorithm: Expectation-maximization algorithms. Logistic regression and web search advertisement. Divide and conquer and Google cloud computing fundamentals. Google Brain and neural networks. The power of big data.