Pattern Recognition Algorithms Data (47 results)

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Taschenbuch. Condition: Neu. Neuware - You studied the algorithms. You solved a hundred problems. Then the interviewer hands you one you have never seen, and your mind goes blank. That freeze is not a knowledge gap. It is a recognition gap - and almost no one trains it. Most interview prep teaches you to memorize solutions to labeled problems. You practice the sliding window chapter, so every problem in front of you is a sliding window problem. The label is handed to you. But a real interview quietly removes the label. It describes a delivery route, a stream of numbers, a game board - and your job is to decide, alone, which pattern it belongs to. That is a different skill from solving, and it is the one this book trains. Think Before You Code is built around a three-question diagnosis you run before writing a single line: which pattern is this, why, and what is the one guiding question that cracks it open. Each chapter builds a pattern from its fundamentals, walks through a problem the way an experienced engineer actually thinks out loud, then gives you a graded ladder of practice problems with the label hidden - so you train recognition instead of recall. Inside, you will find: - Every core pattern that shows up in technical interviews - sliding window, two pointers, hashing, binary search, trees and graphs, backtracking, dynamic programming, greedy, intervals, and more- A repeatable diagnosis method for reading an unfamiliar prompt and naming its pattern before you code- Practice ladders with the pattern hidden, so you rehearse the exact moment that breaks candidates in real interviews- A master decision tree tying every pattern together, plus mixed-diagnosis drills and full mock walkthroughs- The interviewer's-eye view: what separates a candidate who freezes from one who calmly narrates their way to a solution, written by an engineering leader who has sat on both sides of the table This is not another list of five hundred problems to memorize. It is the missing habit that turns everything you already know into an answer you can produce under pressure - the difference between a candidate who has studied and one who is actually ready. If you have done the practice and still freeze on anything new, this book teaches you the one skill that fixes it: naming the problem before you solve it.…

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Condition: New. 224.

Condition: New. 2019. Second. paperback. . . . . .

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Paperback. Condition: Brand New. 2nd revised edition edition. 229 pages. 10.00x7.05x0.63 inches. In Stock.

Paperback. Condition: Brand New. 2014 edition. 210 pages. 9.00x6.00x0.50 inches. In Stock.

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Condition: New. pp. 280.

Condition: New. 2019. Second. paperback. . . . . . Books ship from the US and Ireland.

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Condition: New. pp. 280.

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Hardcover. Condition: Good. No Dust Jacket. First Edition. An ex-library copy in original laminated pictorial hard covers. The usual ex-libris markings. The binding is sound, the text is clean/unmarked, and there is little cover wear. No dust jacket, apparently as issued. Book.

Condition: New. pp. 280.

paperback. Condition: New. In shrink wrap. Looks like an interesting title.

Paperback. Condition: Brand New. 280 pages. 9.21x6.14x0.60 inches. In Stock.

Condition: Sehr gut. Zustand: Sehr gut | Seiten: 280 | Sprache: Englisch | Produktart: Bücher | This valuable text addresses different pattern recognition (PR) tasks in a unified framework with both theoretical and experimental results. Tasks covered include data condensation, feature selection, case generation, clustering/classification, and rule generation and evaluation. Organized into eight chapters, the book begins by introducing PR, data mining, and knowledge discovery concepts. The authors proceed to analyze the tasks of multi-scale data condensation and dimensionality reduction. Then they explore the problem of learning with support vector machine (SVM), and conclude by highlighting the significance of granular computing for different mining tasks in a soft paradigm. …