Jain Mayank Kumar (25 results)

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Soft cover. Condition: New. Contents: 1. Basics of Compiler. 2. Phases of Writing a Compiler. 3. Lexical Analyzer. 4. Syntax Analyzer. 5. Syntax Directed Translation. 6. Type Checking. 7. Run Time Environment. 8. Intermediate Code Generators. 9. Code Generations. 10. Code Optimization. 11. Pass Compiler. Index. This book divided in eleven chapters, in the first chapter describes basics of a compiler, its definition and its types. It also includes the need of a compiler. The second chapter deals with phases of compiler, frontend and book end of compiler, single pass and multiphase compiler; Chapter three covers role of logical analyzer, description of tokens, automata, the fourth chapter presents syntax analyzer, grammar, LMD, RMD, passing techniques. Fifth chapter gives syntax directed translation, syntax tree, attributes such as synthesis and inherited. Chapter six deals with type checking, its definition, dynamic type checking and equivalence of it, function overloading and parameter passing. Chapter seven covers run time environment storage allocation techniques, symbol table. Chapter eight presents intermediate code generators, techniques of ICG, conversion. Chapter nine deals with code generation, basic blocks, flow graph, peephole optimization while chapter ten is on code optimization, that contains optimization of basic blocks, reducible flow graph, data flow analysis and global analysis. Chapter eleven one-pass compiler, compiler, its structure, STD rules and passing are described.…

Paperback. Condition: New.

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Condition: As New. Unread book in perfect condition.

Condition: New. In.

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Condition: New. 2020. Paperback. . . . . .

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Paperback. Condition: Brand New. 152 pages. 9.25x6.10x0.40 inches. In Stock.

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Condition: New. 2020. Paperback. . . . . . Books ship from the US and Ireland.

Paperback. Condition: New. 1st ed. Use ensemble learning techniques and models to improve your machine learning results.Ensemble Learning for AI Developers starts you at the beginning with an historical overview and explains key ensemble techniques and why they are needed. You then will learn how to change training data using bagging, bootstrap aggregating, random forest models, and cross-validation methods. Authors Kumar and Jain provide best practices to guide you in combining models and using tools to boost performance of your machine learning projects. They teach you how to effectively implement ensemble concepts such as stacking and boosting and to utilize popular libraries such as Keras, Scikit Learn, TensorFlow, PyTorch, and Microsoft LightGBM. Tips are presented to apply ensemble learning in different data science problems, including time series data, imaging data, and NLP. Recent advances in ensemble learning are discussed. Sample code is provided in the form of scripts and the IPython notebook.What You Will LearnUnderstand the techniques and methods utilized in ensemble learningUse bagging, stacking, and boosting to improve performance of your machine learning projects by combining models to decrease variance, improve predictions, and reduce biasEnhance your machine learning architecture with ensemble learningWho This Book Is ForData scientists and machine learning engineers keen on exploring ensemble learning.…

Condition: New. In English.

Condition: New. pp. 384.

Condition: New. pp. 155.

Condition: New. pp. 384.

Condition: New. pp. 384.

Paperback. Condition: New.

Paperback. Condition: New. 1st ed. Use ensemble learning techniques and models to improve your machine learning results.Ensemble Learning for AI Developers starts you at the beginning with an historical overview and explains key ensemble techniques and why they are needed. You then will learn how to change training data using bagging, bootstrap aggregating, random forest models, and cross-validation methods. Authors Kumar and Jain provide best practices to guide you in combining models and using tools to boost performance of your machine learning projects. They teach you how to effectively implement ensemble concepts such as stacking and boosting and to utilize popular libraries such as Keras, Scikit Learn, TensorFlow, PyTorch, and Microsoft LightGBM. Tips are presented to apply ensemble learning in different data science problems, including time series data, imaging data, and NLP. Recent advances in ensemble learning are discussed. Sample code is provided in the form of scripts and the IPython notebook.What You Will LearnUnderstand the techniques and methods utilized in ensemble learningUse bagging, stacking, and boosting to improve performance of your machine learning projects by combining models to decrease variance, improve predictions, and reduce biasEnhance your machine learning architecture with ensemble learningWho This Book Is ForData scientists and machine learning engineers keen on exploring ensemble learning.…

Condition: New. Print on Demand pp. 155.

Condition: New. PRINT ON DEMAND pp. 155.

Condition: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Beginning-Intermediate user level|Explains ensemble learning with less math and more programming-friendly abstractions than presented in other books so it is easier for you to learnDiscusses the competitive edge that you can achieve by u.…