Ram Vijay (508 results)

Condition: New. pp. [x] + 81 + [i].

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Condition: New. pp. [6] + 74.

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Paperback or Softback. Condition: New. Soil carbon sequestration potential of different forest types of aric Kutch, Gujarat with special emphasis on climate change: Soil carbon sequestratio. Book.

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Paperback or Softback. Condition: New. Development and validation of HPLC method for simultaneous quantitative determination of Azilsartan medoxomil potassium and Chlorthalidone in human pl. Book.

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paperback. Condition: New. Brand new book, sourced directly from publisher. Dispatch time is 7-8 days from our warehouse. Book will be sent in robust, secure packaging to ensure it reaches you securely.

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Taschenbuch. Condition: Neu. Druck auf Anfrage Neuware - Printed after ordering - Document from the year 2019 in the subject Chemistry - General, , course: Ph.D, language: English, abstract: This book is an approach for compilation and making a handbook in very simple and easy language to very commonly used terms in chemistry for higher secondary, under-graduate and post graduate students.Many times we are familiar with some terms but at certain times like competitive examinations, interviews, and so on it just need a glimpse to all these so as to revise it. This book provides a highlight to almost all basic terms used in chemistry from higher secondary education to post graduation. It tries to cover all basic definitions and basic laws important for interviews, college exams, competitive exams and other such events.This has been designed in simple language and colorful way so as to help students during end-time revision and reducing their quality time to be wasted on searching such basic terms from various sources.…

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Paperback. Condition: New. "Time Series Forecasting Using Generative AI introduces readers to Generative Artificial Intelligence (Gen AI) in time series analysis, offering an essential exploration of cutting-edge forecasting methodologies."The book covers a wide range of topics, starting with an overview of Generative AI, where readers gain insights into the history and fundamentals of Gen AI with a brief introduction to large language models. The subsequent chapter explains practical applications, guiding readers through the implementation of diverse neural network architectures for time series analysis such as Multi-Layer Perceptrons (MLP), WaveNet, Temporal Convolutional Network (TCN), Bidirectional Temporal Convolutional Network (BiTCN), Recurrent Neural Networks (RNN), Long Short-Term Memory (LSTM), Deep AutoRegressive(DeepAR), and Neural Basis Expansion Analysis(NBEATS) using modern tools. Building on this foundation, the book introduces the power of Transformer architecture, exploring its variants such as Vanilla Transformers, Inverted Transformer (iTransformer), DLinear, NLinear, and Patch Time Series Transformer (PatchTST). Finally, The book delves into foundation models such as Time-LLM, Chronos, TimeGPT, Moirai, and TimesFM enabling readers to implement sophisticated forecasting models tailored to their specific needs.This book empowers readers with the knowledge and skills needed to leverage Gen AI for accurate and efficient time series forecasting. By providing a detailed exploration of advanced forecasting models and methodologies, this book enables practitioners to make informed decisions and drive business growth through data-driven insights.?       Understand the core history and applications of Gen AI and its potential to revolutionize time series forecasting.?       Learn to implement different neural network architectures such as MLP, WaveNet, TCN, BiTCN, RNN, LSTM, DeepAR, and NBEATS for time series forecasting.?       Discover the potential of Transformer architecture and its variants, such as Vanilla Transformers, iTransformer, DLinear, NLinear, and PatchTST, for time series forecasting.?       Explore complex foundation models like Time-LLM, Chronos, TimeGPT, Moirai, and TimesFM.?       Gain practical knowledge on how to apply Gen AI techniques to real-world time series forecasting challenges and make data-driven decisions.Who this book is for:Data Scientists, Machine learning engineers, Business Aanalysts, Statisticians, Economists, Financial Analysts, Operations Research Analysts, Data Analysts, Students.…

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