Data-Science-Notes 数据科学的笔记以及资料搜集,目前尚在更新,部分内容来源于github搜集。 0.math (数学基础) 1.python-basic(python基础) 2.numpy(numpy基础) 3.pandas(pandas基础) 4.scipy(scipy基础) 5.data-visualization(数据可视化基础,包含matplotlib和seaborn) 6.scikit-learn(scikit-learn基础) 7.machine...
:memo: An awesome Data Science repository to learn and apply for real world problems. - academic/awesome-datascience
DSVM-Ubuntu-22.04 image is a tailored Data Science Virtual Machine built on the robust Ubuntu 22.04 platform. It comes pre-installed with a comprehensive suite of popular data science and AI tools, including deep learning frameworks, Jupyter notebooks, and visualization libraries. Optimized for sea...
It is a valuable tool to get your rendering code right and improve it. Just start your Vulkan, D3D11, D3D12 or OpenGL application via RenderDoc, take a snapshot and start the investigation. Check out the in-depth release notes on github. Post navigation 1 2 Next » ...
Data Science Dojo blog features the most recent, and relevant articles about data science, analytics, generative AI, large language models, machine learning, and data visualization.
There’s hardly a better way to simplify big data than by visual means. What would you rather do: read through flat data and take notes or use a dedicated data visualization software and grasp the essence of large data sets in seconds?
The same intuition is applied to other materials science use cases with features that are long in one or two dimensions; for example, delamination in carbon fiber composites, pore space in gas-bearing shale, thin films in power structures, layer-wise metrology of semicondu...
Data scientists might also use web-based data science notebooks, such as Zeppelin Notebooks, throughout the much of the process for data ingestion, discovery, analytics, visualization, and collaboration. Data science methods Data scientists use statistical methods such as hypothesis testing, factor an...
Review Reports Versions Notes Abstract In traditional production plants, current technologies do not provide sufficient context to support information integration and interpretation. Digital transformation technologies have the potential to support contextualization, but it is unclear how this can be achieved...
Data mining is employed to extract useful information and to detect patterns from often large data sets, closely related to knowledge discovery in databases and data science. In this investigation, we formulate models based on machine learning algorithms