IPFS
Dropbox
Google Drive
FileCoin
Syncthing
Sia
Mega
ZeroNet
NumPy
Pandas
Scikit-learn
OpenCV
Dataiku
Exploratory
htm.java
WEKA
Based on our record, IPFS should be more popular than NumPy. It has been mentiond 288 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.
When I click on https://synapsemedia.io/ I get redirected to a link like https://ipfs.io/ipns/synapsemedia.io (to use ipfs.io instead of my local node). Source: about 1 year ago
You may already be aware that the Interplanetary File System or IPFS is a distributed storage network where computers from all over the world form nodes to share data. Source: about 1 year ago
In case of you don't trust them, it gets harder. Especially if you need to have it hosted without any trace to yourself. I'd probably pay a service to store my data on ipfs. You can pay with crypto. But I'm this case there's the question, how will you be able to access it. My thought would be to have a [tails][tails] USB with the necessary software. Source: over 1 year ago
Https://ipfs.io is the only acceptable file host. Source: over 1 year ago
I never click GET button, don't even know what it does tbh XD Those four buttons are for choosing which IPFS gateway you want to use. By default I use ipfs.io, if ipfs.io is down then I click the Cloudflare one. General rule is that you pick one gateway if it does not work then another one and so on. Source: over 1 year ago
In NumPy with * or multiply(). ` or multiply()` can multiply 0D or more D arrays by element-wise multiplication. - Source: dev.to / 2 months ago
Data science projects often use numpy. However, numpy objects are not JSON-serializable and therefore require conversion to standard python objects in order to be saved:. - Source: dev.to / 2 months ago
Numpy: A library for scientific computing in Python. - Source: dev.to / 5 months ago
Python has become a preferred language for data analysis due to its simplicity and robust library ecosystem. Among these, NumPy stands out with its efficient handling of numerical data. Let’s say you’re working with numbers for large data sets—something Python’s native data structures may find challenging. That’s where NumPy arrays come into play, making numerical computations seamless and speedy. - Source: dev.to / 7 months ago
A majority of software in the modern world is built upon various third party packages. These packages help offload work that would otherwise be rather tedious. This includes interacting with cloud APIs, developing scientific applications, or even creating web applications. As you gain experience in python you'll be using more and more of these packages developed by others to power your own code. In this example... - Source: dev.to / 7 months ago
Dropbox - Online Sync and File Sharing
Pandas - Pandas is an open source library providing high-performance, easy-to-use data structures and data analysis tools for the Python.
Google Drive - Access and sync your files anywhere
Scikit-learn - scikit-learn (formerly scikits.learn) is an open source machine learning library for the Python programming language.
FileCoin - Filecoin is a data storage network and electronic currency based on Bitcoin.
OpenCV - OpenCV is the world's biggest computer vision library
