Based on our record, Jupyter should be more popular than PyTorch. It has been mentiond 206 times since March 2021. We are tracking product recommendations and mentions on various public social media platforms and blogs. They can help you identify which product is more popular and what people think of it.
Interesting, I would have guessed you had used something jupyter-like: https://jupyter.org/ https://explorabl.es/all/. - Source: Hacker News / 5 days ago
JupyterLab: JupyterLab is an interactive development environment that allows you to create and share documents containing live code, equations, visualizations, and narrative text. It's particularly well-suited for data science and research-oriented projects. - Source: dev.to / 26 days ago
Jupyter Lab web-based interactive development environment. - Source: dev.to / about 1 month ago
Choosing IDE: Selecting a suitable Integrated Development Environment (IDE) is crucial for efficient coding. Consider popular options such as PyCharm, Visual Studio Code, or Jupyter Notebook. Install your preferred IDE and ensure it's configured to work with Python. - Source: dev.to / about 1 month ago
Jupyter Notebooks is very popular among data people specially Python users. So, I tried to find a way to run the Groovy kernel inside a Jupyter Notebook, and to my surprise, I found a way, BeakerX! - Source: dev.to / 3 months ago
TensorFlow, developed by Google, and PyTorch, developed by Facebook, are two of the most popular frameworks for building and training complex machine learning models. TensorFlow is known for its flexibility and robust scalability, making it suitable for both research prototypes and production deployments. PyTorch is praised for its ease of use, simplicity, and dynamic computational graph that allows for more... - Source: dev.to / 27 days ago
*My post explains Dot, Matrix and Element-wise multiplication in PyTorch. - Source: dev.to / 2 months ago
Import torch # we use PyTorch: https://pytorch.org Data = torch.tensor(encode(text), dtype=torch.long) Print(data.shape, data.dtype) Print(data[:1000]) # the 1000 characters we looked at earlier will to the GPT look like this. - Source: dev.to / 3 months ago
AI's Open Embrace Artificial intelligence (AI) and machine learning (ML) are increasingly leveraging open-source frameworks like TensorFlow [https://www.tensorflow.org/] and PyTorch [https://pytorch.org/]. This democratization of AI tools is driving innovation and lowering entry barriers across industries. - Source: dev.to / 2 months ago
Which label applies to a tool sometimes depends on what you do with it. For example, PyTorch or TensorFlow can be called a library, a toolkit, or a machine-learning framework. - Source: dev.to / 2 months ago
Looker - Looker makes it easy for analysts to create and curate custom data experiences—so everyone in the business can explore the data that matters to them, in the context that makes it truly meaningful.
TensorFlow - TensorFlow is an open-source machine learning framework designed and published by Google. It tracks data flow graphs over time. Nodes in the data flow graphs represent machine learning algorithms. Read more about TensorFlow.
Databricks - Databricks provides a Unified Analytics Platform that accelerates innovation by unifying data science, engineering and business.What is Apache Spark?
Keras - Keras is a minimalist, modular neural networks library, written in Python and capable of running on top of either TensorFlow or Theano.
Google BigQuery - A fully managed data warehouse for large-scale data analytics.
Scikit-learn - scikit-learn (formerly scikits.learn) is an open source machine learning library for the Python programming language.