Adarsha (249 Ergebnisse)
- Softcover
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- Softcover
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- Hardcover
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Zustand: New.
Time Series Algorithms Recipes 1st ed.
Kulkarni, Akshay R;shivananda, Adarsha;kulkarni, Anoosh;krishnan, V Adithya
- Softcover
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EUR 23,96
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- Softcover
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EUR 22,77
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Zustand: New. Brand New! Not Overstocks or Low Quality Book Club Editions! Direct From the Publisher! We're not a giant, faceless warehouse organization! We're a small town bookstore that loves books and loves it's customers! Buy from Lakeside Books.
- Hardcover
Anbieter: Books Puddle, New York, NY, USABooks Puddle
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EUR 24,37
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Zustand: New. First Edition 4676/21 Ansari Road, New Delhi - 110 002 NO-PA16APR2015-KAP.
Time Series Algorithms Recipes 1st ed.
Kulkarni, Akshay R;shivananda, Adarsha;kulkarni, Anoosh;krishnan, V Adithya
- Softcover
Anbieter: GreatBookPrices, Columbia, MD, USAGreatBookPrices
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EUR 25,73
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- Hardcover
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Zustand: New.
Time Series Algorithms Recipes: Implement Machine Learning and Deep Learning Techniques with Python
Kulkarni, Akshay R; Shivananda, Adarsha; Kulkarni, Anoosh; Krishnan, V Adithya
- Softcover
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Zustand: New.
Applied Recommender Systems with Python : Build Recommender Systems with Deep Learning, NLP and Graph-Based Techniques
Kulkarni, Akshay; Shivananda, Adarsha; Kulkarni, Anoosh; Krishnan, V Adithya
- Softcover
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EUR 27,13
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Zustand: New.
Applied Recommender Systems with Python
V Adithya Krishnan, Akshay Kulkarni, Anoosh Kulkarni, Adarsha Shivananda
- Softcover
Anbieter: Rarewaves.com USA, London, LONDO, Vereinigtes KönigreichRarewaves.com USA
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EUR 29,49
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Paperback. Zustand: New. This book will teach you how to build recommender systems with machine learning algorithms using Python. Recommender systems have become an essential part of every internet-based business today.You'll start by learning basic concepts of recommender systems, with an overview of different types of recommen…der engines and how they function. Next, you will see how to build recommender systems with traditional algorithms such as market basket analysis and content- and knowledge-based recommender systems with NLP. The authors then demonstrate techniques such as collaborative filtering using matrix factorization and hybrid recommender systems that incorporate both content-based and collaborative filtering techniques. This is followed by a tutorial on building machine learning-based recommender systems using clustering and classification algorithms like K-means and random forest. The last chapters cover NLP, deep learning, and graph-based techniques to build a recommender engine. Each chapter includes data preparation, multiple ways to evaluate and optimize the recommender systems, supporting examples, and illustrations.By the end of this book, you will understand and be able to build recommender systems with various tools and techniques with machine learning, deep learning, and graph-based algorithms.What You Will LearnUnderstand and implement different recommender systems techniques with PythonEmploy popular methods like content- and knowledge-based, collaborative filtering, market basket analysis, and matrix factorization Build hybrid recommender systems that incorporate both content-based and collaborative filteringLeverage machine learning, NLP, and deep learning for building recommender systemsWho This Book Is ForData scientists, machine learning engineers, and Python programmers interested in building and implementing recommender systems to solve problems.
- Softcover
Anbieter: GreatBookPrices, Columbia, MD, USAGreatBookPrices
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EUR 28,15
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Zustand: New.
- Softcover
Anbieter: Lakeside Books, Benton Harbor, MI, USALakeside Books
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EUR 27,23
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Zustand: New. Brand New! Not Overstocks or Low Quality Book Club Editions! Direct From the Publisher! We're not a giant, faceless warehouse organization! We're a small town bookstore that loves books and loves it's customers! Buy from Lakeside Books.
Time Series Algorithms Recipes
V Adithya Krishnan, Akshay R Kulkarni, Adarsha Shivananda, Anoosh Kulkarni
- Softcover
- Erstausgabe
Anbieter: Rarewaves USA, OSWEGO, IL, USARarewaves USA
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EUR 31,35
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Paperback. Zustand: New. 1st ed. This book teaches the practical implementation of various concepts for time series analysis and modeling with Python through problem-solution-style recipes, starting with data reading and preprocessing. It begins with the fundamentals of time series forecasting using statistical modeling methods…like AR (autoregressive), MA (moving-average), ARMA (autoregressive moving-average), and ARIMA (autoregressive integrated moving-average). Next, you'll learn univariate and multivariate modeling using different open-sourced packages like Fbprohet, stats model, and sklearn. You'll also gain insight into classic machine learning-based regression models like randomForest, Xgboost, and LightGBM for forecasting problems. The book concludes by demonstrating the implementation of deep learning models (LSTMs and ANN) for time series forecasting. Each chapter includes several code examples and illustrations. After finishing this book,you will have a foundational understanding of various concepts relating to time series and its implementation in Python. What You Will LearnImplement various techniques in time series analysis using Python.Utilize statistical modeling methods such as AR (autoregressive), MA (moving-average), ARMA (autoregressive moving-average) and ARIMA (autoregressive integrated moving-average) for time series forecasting Understand univariate and multivariate modeling for time series forecastingForecast using machine learning and deep learning techniques such as GBM and LSTM (long short-term memory) Who This Book Is ForData Scientists, Machine Learning Engineers, and software developers interested in time series analysis.
- Softcover
Anbieter: Ria Christie Collections, Uxbridge, Vereinigtes KönigreichRia Christie Collections
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EUR 17,35
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Zustand: New. In.
- Softcover
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EUR 12,93
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PF. Zustand: New.
- Softcover
Anbieter: ThriftBooks-Dallas, Dallas, TX, USAThriftBooks-Dallas
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EUR 32,52
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Paperback. Zustand: Good. No Jacket. Pages can have notes/highlighting. Spine may show signs of wear. ~ ThriftBooks: Read More, Spend Less.
Applied Recommender Systems with Python : Build Recommender Systems with Deep Learning, NLP and Graph-Based Techniques
Kulkarni, Akshay; Shivananda, Adarsha; Kulkarni, Anoosh; Krishnan, V Adithya
- Softcover
Anbieter: GreatBookPrices, Columbia, MD, USAGreatBookPrices
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EUR 30,88
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Zustand: As New. Unread book in perfect condition.
- Hardcover
Anbieter: Vedams eBooks (P) Ltd, New Delhi, IndienVedams eBooks (P) Ltd
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EUR 15,46
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Hardcover. Zustand: As New.
- Softcover
Anbieter: California Books, Miami, FL, USACalifornia Books
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EUR 33,77
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Zustand: New.
Applied Recommender Systems with Python: Build Recommender Systems with Deep Learning, NLP and Graph-Based Techniques
Kulkarni, Akshay; Shivananda, Adarsha; Kulkarni, Anoosh; Krishnan, V Adithya
- Softcover
Anbieter: California Books, Miami, FL, USACalifornia Books
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EUR 33,77
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Zustand: New.
- Softcover
Anbieter: GreatBookPricesUK, Woodford Green, Vereinigtes KönigreichGreatBookPricesUK
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EUR 15,88
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Zustand: New.
- Softcover
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EUR 31,63
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Zustand: As New. Unread book in perfect condition.
Time Series Algorithms Recipes
V Adithya Krishnan, Akshay R Kulkarni, Adarsha Shivananda, Anoosh Kulkarni
- Softcover
- Erstausgabe
Anbieter: Rarewaves.com USA, London, LONDO, Vereinigtes KönigreichRarewaves.com USA
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EUR 34,06
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Paperback. Zustand: New. 1st ed. This book teaches the practical implementation of various concepts for time series analysis and modeling with Python through problem-solution-style recipes, starting with data reading and preprocessing. It begins with the fundamentals of time series forecasting using statistical modeling methods…like AR (autoregressive), MA (moving-average), ARMA (autoregressive moving-average), and ARIMA (autoregressive integrated moving-average). Next, you'll learn univariate and multivariate modeling using different open-sourced packages like Fbprohet, stats model, and sklearn. You'll also gain insight into classic machine learning-based regression models like randomForest, Xgboost, and LightGBM for forecasting problems. The book concludes by demonstrating the implementation of deep learning models (LSTMs and ANN) for time series forecasting. Each chapter includes several code examples and illustrations. After finishing this book,you will have a foundational understanding of various concepts relating to time series and its implementation in Python. What You Will LearnImplement various techniques in time series analysis using Python.Utilize statistical modeling methods such as AR (autoregressive), MA (moving-average), ARMA (autoregressive moving-average) and ARIMA (autoregressive integrated moving-average) for time series forecasting Understand univariate and multivariate modeling for time series forecastingForecast using machine learning and deep learning techniques such as GBM and LSTM (long short-term memory) Who This Book Is ForData Scientists, Machine Learning Engineers, and software developers interested in time series analysis.
- Softcover
Anbieter: GreatBookPricesUK, Woodford Green, Vereinigtes KönigreichGreatBookPricesUK
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EUR 17,97
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Zustand: As New. Unread book in perfect condition.
- Softcover
Anbieter: Rarewaves USA, OSWEGO, IL, USARarewaves USA
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EUR 36,70
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Paperback. Zustand: New. Gain a working knowledge of prescriptive AI, its history, and its current and future trends. This book will help you evaluate different AI-driven predictive analytics techniques and help you incorporate decision intelligence into your business workflow through real-world examples.The book kicks off with…an introduction to decision intelligence and provides insight into prescriptive AI and how it can be woven into various business strategies and frameworks. You'll then be introduced to different decision intelligence methodologies and how to implement them, along with advantages and limitations of each. Digging deeper, the authors then walk you through how to perform simulations and interpret the results. A full chapter is devoted to embedding decision intelligence processes and outcomes into your business workflow using various applications. The book concludes by exploring different cognitive biases humans are prone to, and how those biasescan be eliminated by combining machine and human intelligence.Upon completing this book, you will understand prescriptive AI, tools, and techniques and will be ready to incorporate them into your business workflow.What You Will LearnImplement full-fledged decision intelligence applications using PythonLeverage the tools, techniques, and methodologies for prescriptive AIUnderstand how prescriptive AI can be used in different domains through practical examplesInterpret results and integrate them into your decision makingWho This Book Is ForData Scientists and Machine Learning Engineers, as well as business professionals who want to understand how AI-driven decision intelligence can help grow their business.
Applied Recommender Systems with Python
V Adithya Krishnan, Akshay Kulkarni, Anoosh Kulkarni, Adarsha Shivananda
- Softcover
Anbieter: Rarewaves USA, OSWEGO, IL, USARarewaves USA
Verkäufer/-in kontaktierenVerkäufer/-in mit 5 SternenZustand: Neu
EUR 38,17
Versand nach gratisVersand innerhalb von USAAnzahl: 8 verfügbar
Paperback. Zustand: New. This book will teach you how to build recommender systems with machine learning algorithms using Python. Recommender systems have become an essential part of every internet-based business today.You'll start by learning basic concepts of recommender systems, with an overview of different types of recommen…der engines and how they function. Next, you will see how to build recommender systems with traditional algorithms such as market basket analysis and content- and knowledge-based recommender systems with NLP. The authors then demonstrate techniques such as collaborative filtering using matrix factorization and hybrid recommender systems that incorporate both content-based and collaborative filtering techniques. This is followed by a tutorial on building machine learning-based recommender systems using clustering and classification algorithms like K-means and random forest. The last chapters cover NLP, deep learning, and graph-based techniques to build a recommender engine. Each chapter includes data preparation, multiple ways to evaluate and optimize the recommender systems, supporting examples, and illustrations.By the end of this book, you will understand and be able to build recommender systems with various tools and techniques with machine learning, deep learning, and graph-based algorithms.What You Will LearnUnderstand and implement different recommender systems techniques with PythonEmploy popular methods like content- and knowledge-based, collaborative filtering, market basket analysis, and matrix factorization Build hybrid recommender systems that incorporate both content-based and collaborative filteringLeverage machine learning, NLP, and deep learning for building recommender systemsWho This Book Is ForData scientists, machine learning engineers, and Python programmers interested in building and implementing recommender systems to solve problems.
- Softcover
Anbieter: Rarewaves.com USA, London, LONDO, Vereinigtes KönigreichRarewaves.com USA
Verkäufer/-in kontaktierenVerkäufer/-in mit 5 SternenZustand: Neu
EUR 39,77
Versand nach gratisVersand von Vereinigtes Königreich nach USAAnzahl: 8 verfügbar
Paperback. Zustand: New. Gain a working knowledge of prescriptive AI, its history, and its current and future trends. This book will help you evaluate different AI-driven predictive analytics techniques and help you incorporate decision intelligence into your business workflow through real-world examples.The book kicks off with…an introduction to decision intelligence and provides insight into prescriptive AI and how it can be woven into various business strategies and frameworks. You'll then be introduced to different decision intelligence methodologies and how to implement them, along with advantages and limitations of each. Digging deeper, the authors then walk you through how to perform simulations and interpret the results. A full chapter is devoted to embedding decision intelligence processes and outcomes into your business workflow using various applications. The book concludes by exploring different cognitive biases humans are prone to, and how those biasescan be eliminated by combining machine and human intelligence.Upon completing this book, you will understand prescriptive AI, tools, and techniques and will be ready to incorporate them into your business workflow.What You Will LearnImplement full-fledged decision intelligence applications using PythonLeverage the tools, techniques, and methodologies for prescriptive AIUnderstand how prescriptive AI can be used in different domains through practical examplesInterpret results and integrate them into your decision makingWho This Book Is ForData Scientists and Machine Learning Engineers, as well as business professionals who want to understand how AI-driven decision intelligence can help grow their business.
Applied Generative AI for Beginners: Practical Knowledge on Diffusion Models, ChatGPT, and Other LLMs
Kulkarni, Akshay,Shivananda, Adarsha,Kulkarni, Anoosh,Gudivada, Dilip
- Softcover
Anbieter: Books From California, Simi Valley, CA, USABooks From California
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EUR 37,50
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paperback. Zustand: Acceptable. The innre rhinges have come unglued. The copy shows minor external wear, but is in otherwise very good condition.
Applied Generative AI for Beginners: Practical Knowledge on Diffusion Models, ChatGPT, and Other LLMs
Kulkarni, Akshay,Shivananda, Adarsha,Kulkarni, Anoosh,Gudivada, Dilip
- Softcover
Anbieter: Books From California, Simi Valley, CA, USABooks From California
Verkäufer/-in kontaktierenVerkäufer/-in mit 4 SternenZustand: Gebraucht - Befriedigend
EUR 37,50
EUR 4,30 VersandVersand innerhalb von USAAnzahl: 1 verfügbar
paperback. Zustand: Good. Some shelf wear.
