Jupyter
Looker
Databricks
Google BigQuery
Presto DB
Rakam
Informatica
Concurrent
PyTorch
TensorFlow
Keras
Scikit-learn
OpenCV
CUDA
MLKit
NumPy
Jupyter| Categories |
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| Website | jupyter.org |
| Details $ | - |
PyTorch| Categories |
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| Website | pytorch.org |
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Based on our record, Jupyter should be more popular than PyTorch. It has been mentiond 200 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.
They make it easy to launch multiple case-by-case data science projects and run your local code right from Jupyter Notebook. - Source: dev.to / 18 days ago
Talking to some colleagues and friends lately gathering some ideas of a nice Machine Learning project to build, I’ve seen that there’s a gap of knowledge in terms of how do one exactly uses a Machine Learning model trained? Just imagine yourself building a model to solve some problem, you are probably using Jupyter Notebook to perform some data clean up, perform some normalization and further tests. Then you... - Source: dev.to / about 2 months ago
This year I decided to commit to a set of tools on day 1 (Polars and Jupyter) and use them for the whole challenge. It seemed silly to do a whole new meandering walkthrough, so instead I'll highlight a few things that stuck out after finishing the challenge and sitting on it for a few days. Here we go! - Source: dev.to / 2 months ago
The resulting technical reports can be in the formats: Markdown, Pod6, or Org-mode. Or just Jupyter notebooks. Source: 4 months ago
Another effective way to use comments is through literate programming. In this programming style, comments take the spotlight: the source code contains more prose than executable code. This is useful when explaining the algorithm is more important than reading it, as in academic research and data analysis. Not surprisingly, it is the paradigm of popular tools like Jupyter Notebook and Quarto. - Source: dev.to / 5 months ago
PyTorch: An open-source deep learning framework that facilitates dynamic computational graphs, making it flexible and efficient for research and production. - Source: dev.to / 5 days ago
In this blog post, we will go through a full example and setup a data stream to PyTorch from a playground dataset on a remote database. - Source: dev.to / about 1 month ago
This post describes how I added automatic differentiation to Tensorken. Tensorken is my attempt to build a fully featured yet easy-to-understand and hackable implementation of a deep learning library in Rust. It takes inspiration from the likes of PyTorch, Tinygrad, and JAX. - Source: dev.to / 3 months ago
The design of MLX is inspired by frameworks like NumPy, PyTorch, Jax, and ArrayFire. Source: 3 months ago
If you go to https://pytorch.org/, you can choose an installation script tailored to your environment. Source: 3 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.
