Anaconda wants to chew into the market of Google Colab
PythonAnywhere is a cloud-based Python development and hosting environment.
PythonAnywhere is a cloud-based Python development and hosting environment.
Browser extensions are software modules built on web technologies like HTML, CSS, and JavaScript.
The workshop covered advanced concepts and features of the latest SYCL specifications, including simplified code implementation for heterogeneous computing
Vertica 12 has expanded its Kubernetes to Google Cloud Storage, Azure Blob Storage and Hadoop Distributed Filesystem Storage.
This article briefs about the process of building a custom python package that can be used to scrape data from the web by using various inbuilt functions of BeautifulSoup.
RStudio Workbench is the ideal platform for code-first data science development.
The Studio Lab was launched with an aim to make ‘machine learning more accessible’ and help beginners overcome some of the persistent challenges.
PyScript allows users to build Python applications on the web using an HTML interface.
Each page in the Power BI report becomes an individual slide in PowerPoint.
InCoder is trained to maximise the likelihood of a corpus of code and can infill blocks of code conditioned on the arbitrary left and right contexts.
PyCharm has reworked Docker’s UI to make it less messier than before.
I use Google Colab or Kaggle Kernel platform to write the code from scratch.
The turning point was in 1997 when I attended my first C class, which introduced me to the elusive world of programming languages.
Orchest is a tool that can be utilized for building machine learning pipelines. Using this library we can build pipelines in a visualized manner using the Orchest provided user interface. One of the best things about this tool is it doesn’t require any third-party integration or DAGs.
XAutoML is an analytics tool that explains the AutoML optimization procedures and machine learning pipelines constructed by AutoML.
The most comprehensive Repository of Python Libraries for Data Science Recent Tutorials EDA, Visualization & Data Handling S No. Library Year Released Release Docs Tutorials
Priyanjali Gupta built an AI model that turns sign language into English in real-time and went viral with it on LinkedIn.
The free version of the Deep note enables you to share the notebook up to three other team members.
The on-prem data lake demands a tight check on resources utilisation and is cost-intensive.
Initially created for a Petfinder competition, this notebook is a tutorial about the inner workings of a convolution filter through examples and illustrations.
Kubeflow is an end-to-end machine learning stack orchestration toolset based on Kubernetes for deploying, scaling, and managing complex systems.
Kubeflow provides components for each stage in the ML lifecycle, including exploration, training and deployment.
Kubeflow is an open-source platform for scalable machine learning model training and serving.
Some of the most common cloud-based platforms for data science projects include Amazon Web Services, Google Cloud Platform, IBM Watson and Microsoft Azure.
What we refer to as coding skills for data science are in fact the ability to think logically and understand underlying data structures.
Though the demand for AI/ML roles is at an all time high, the niche talent is in short supply.
Researchers and doctors can now analyse 3D medical images with Azure Machine Learning
In the context of analytics, data visualization is critical because it allows users or clients to view large amounts of data and simultaneously extract important insights that can propel the business forward.
In this article, we pick out the top 5 IDEs useful for data scientists
ISRO, in association with IIRS, has launched courses over the last year to further boost the study of the space tech industry in India.
Join the forefront of data innovation at the Data Engineering Summit 2024 where industry leaders redefine technology 8217 s future
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