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Dr K Chaudhry is First Author of Jaypee Brothers, Number One Medical Publishers in India. First book of Dr K Chaudhry, as also of Jaypee Brothers, was published during the year 1968. In addition, Dr K Chaudhry is Youtube Celebrity with fans in all Countries. He is Famous for his English Versions of Bollywood and Pakistani Songs. Patrick French's India A Portrait has three pages on Dr K Chaudhry. His versatility shows up in his Horoscope software, Global Malls Yellow Pages, BMI Registered lyrics. Google DOCTORKC to view Abhishek Bachhan tweet, Patrich French interactions, and huge number of songs.
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Dr K Chaudhry is First Author of Jaypee Brothers, Number One Medical Publishers in India. First book of Dr K Chaudhry, as also of Jaypee Brothers, was published during the year 1968. In addition, Dr K Chaudhry is Youtube Celebrity with fans in all Countries. He is Famous for his English Versions of Bollywood and Pakistani Songs. Patrick French's India A Portrait has three pages on Dr K Chaudhry. His versatility shows up in his Horoscope software, Global Malls Yellow Pages, BMI Registered lyrics. Google DOCTORKC to view Abhishek Bachhan tweet, Patrich French interactions, and huge number of songs.
Development
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Data Science with Python and Dask
Summary Dask is a native parallel analytics tool designed to integrate seamlessly with the libraries you're already using, including Pandas, NumPy, and Scikit-Learn. With Dask you can crunch and work with huge datasets, using the tools you already have. And Data Science with Python and Dask is your guide to using Dask for your data projects without changing the way you work! Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications. You'll find registration instructions inside the print book. About the Technology An efficient data pipeline means everything for the success of a data science project. Dask is a flexible library for parallel comp...
Artificial Intelligence Applications and Innovations
This two-volume set of IFIP-AICT 675 and 676 constitutes the refereed proceedings of the 19th IFIP WG 12.5 International Conference on Artificial Intelligence Applications and Innovations, AIAI 2023, held in León, Spain, during June 14–17, 2023. This event was held in hybrid mode. The 75 regular papers and 17 short papers presented in this two-volume set were carefully reviewed and selected from 185 submissions. The papers cover the following topics: Deep Learning (Reinforcement/Recurrent Gradient Boosting/Adversarial); Agents/Case Based Reasoning/Sentiment Analysis; Biomedical - Image Analysis; CNN - Convolutional Neural Networks YOLO CNN; Cyber Security/Anomaly Detection; Explainable AI/Social Impact of AI; Graph Neural Networks/Constraint Programming; IoT/Fuzzy Modeling/Augmented Reality; LEARNING (Active-AutoEncoders-Federated); Machine Learning; Natural Language; Optimization-Genetic Programming; Robotics; Spiking NN; and Text Mining /Transfer Learning.
Breaking the Language Barrier: Demystifying Language Models with OpenAI
Breaking the Language Barrier: Demystifying Language Models with OpenAI is an informative guide that covers practical NLP use cases, from machine translation to vector search, in a clear and accessible manner. In addition to providing insights into the latest technology that powers ChatGPT and other OpenAI language models, including GPT-3 and DALL-E, this book also showcases how to use OpenAI on the cloud, specifically on Microsoft Azure, to create scalable and efficient solutions.
Development of a modular Knowledge-Discovery Framework based on Machine Learning for the interdisciplinary analysis of complex phenomena in the context of GDI combustion processes
In this work, a novel knowledge discovery framework able to analyze data produced in the Gasoline Direct Injection (GDI) context through machine learning is presented and validated. This approach is able to explore and exploit the investigated design spaces based on a limited number of observations, discovering and visualizing connections and correlations in complex phenomena. The extracted knowledge is then validated with domain expertise, revealing potential and limitations of this method.
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Annual Report
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Python and HDF5
Gain hands-on experience with HDF5 for storing scientific data in Python. This practical guide quickly gets you up to speed on the details, best practices, and pitfalls of using HDF5 to archive and share numerical datasets ranging in size from gigabytes to terabytes. Through real-world examples and practical exercises, you’ll explore topics such as scientific datasets, hierarchically organized groups, user-defined metadata, and interoperable files. Examples are applicable for users of both Python 2 and Python 3. If you’re familiar with the basics of Python data analysis, this is an ideal introduction to HDF5. Get set up with HDF5 tools and create your first HDF5 file Work with datasets by learning the HDF5 Dataset object Understand advanced features like dataset chunking and compression Learn how to work with HDF5’s hierarchical structure, using groups Create self-describing files by adding metadata with HDF5 attributes Take advantage of HDF5’s type system to create interoperable files Express relationships among data with references, named types, and dimension scales Discover how Python mechanisms for writing parallel code interact with HDF5
