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The Jupyter Seol Interwinding Earrings create a fascinating and elegant atmosphere. It has a simple yet a bold design that is wearable for any occasion as a daily item. Material: Brass Color: WG/YG Measurements: W=20mm, L=35mm Gift Ready: branded cotton gift bag included. Mystery that can’t be escaped, ‘Orbitt’. ille lan Orbitt Collection expresses a variety of repetitions and loops of orbitary lines. - Store jewelry in separate polybags to prevent scratches and oxidization. - Color may fade when exposed to salt water, sweat, spray, or perfume. Be sure to clean product before storing. - Shape of the set jewels can be altered by impact. Please handle it carefully. - Note that the internal properties of the various stones cause them to be sensitive to factors such as strong sunlight and chemicals. - 925 Silver and 14k, 18k gold are particularly sensitive to lipid and fat-based substances such as cosmetics or soapy water and exposure to these substances may lead to fading of luster.

Jupyter Notebook 101 will teach you all you need to know to create and use Notebooks effectively. You can use Jupyter Notebook to help you learn to code, create presentations, and make beautiful documentation.The Jupyter Notebook is used by the scientific community to demonstrate research in an easy-to-replicate manner.You will learn the following in Jupyter Notebook 101: - How to create and edit Notebooks - How to add styling, images, graphs, etc - How to configure Notebooks - How to export your Notebooks to other formats - Notebook extensions - Notebook widgets - Using Notebooks for presentations - Debugging Notebooks - Running tests against and inside Notebooks - An intro to JupyterLab

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Learn Docker "infrastructure as code" technology to define a system for performing standard but non-trivial data tasks on medium- to large-scale data sets, using Jupyter as the master controller.It is not uncommon for a real-world data set to fail to be easily managed. The set may not fit well into access memory or may require prohibitively long processing. These are significant challenges to skilled software engineers and they can render the standard Jupyter system unusable. As a solution to this problem, Docker for Data Science proposes using Docker. You will learn how to use existing pre-compiled public images created by the major open-source technologies―Python, Jupyter, Postgres―as well as using the Dockerfile to extend these images to suit your specific purposes. The Docker-Compose technology is examined and you will learn how it can be used to build a linked system with Python churning data behind the scenes and Jupyter managing these background tasks. Best practices in using existing images are explored as well as developing your own images to deploy state-of-the-art machine learning and optimization algorithms.What  You'll Learn Master interactive development using the Jupyter platformRun and build Docker containers from scratch and from publicly available open-source imagesWrite infrastructure as code using the docker-compose tool and its docker-compose.yml file typeDeploy a multi-service data science application across a cloud-based systemWho This Book Is ForData scientists, machine learning engineers, artificial intelligence researchers, Kagglers, and software developers

Leverage the numerical and mathematical modules in Python and its standard library as well as popular open source numerical Python packages like NumPy, SciPy, FiPy, matplotlib and more. This fully revised edition, updated with the latest details of each package and changes to Jupyter projects, demonstrates how to numerically compute solutions and mathematically model applications in big data, cloud computing, financial engineering, business management and more. Numerical Python, Second Edition, presents many brand-new case study examples of applications in data science and statistics using Python, along with extensions to many previous examples. Each of these demonstrates the power of Python for rapid development and exploratory computing due to its simple and high-level syntax and multiple options for data analysis. After reading this book, readers will be familiar with many computing techniques including array-based and symbolic computing, visualization and numerical file I/O, equation solving, optimization, interpolation and integration, and domain-specific computational problems, such as differential equation solving, data analysis, statistical modeling and machine learning.What You'll LearnWork with vectors and matrices using NumPyPlot and visualize data with MatplotlibPerform data analysis tasks with Pandas and SciPyReview statistical modeling and machine learning with statsmodels and scikit-learnOptimize Python code using Numba and CythonWho This Book Is ForDevelopers who want to understand how to use Python and its related ecosystem for numerical computing. 

Leverage the numerical and mathematical modules in Python and its standard library as well as popular open source numerical Python packages like NumPy, SciPy, FiPy, matplotlib and more. This fully revised edition, updated with the latest details of each package and changes to Jupyter projects, demonstrates how to numerically compute solutions and mathematically model applications in big data, cloud computing, financial engineering, business management and more. Numerical Python, Second Edition, presents many brand-new case study examples of applications in data science and statistics using Python, along with extensions to many previous examples. Each of these demonstrates the power of Python for rapid development and exploratory computing due to its simple and high-level syntax and multiple options for data analysis. After reading this book, readers will be familiar with many computing techniques including array-based and symbolic computing, visualization and numerical file I/O, equation solving, optimization, interpolation and integration, and domain-specific computational problems, such as differential equation solving, data analysis, statistical modeling and machine learning.What You'll LearnWork with vectors and matrices using NumPyPlot and visualize data with MatplotlibPerform data analysis tasks with Pandas and SciPyReview statistical modeling and machine learning with statsmodels and scikit-learnOptimize Python code using Numba and CythonWho This Book Is ForDevelopers who want to understand how to use Python and its related ecosystem for numerical computing.