Ideanote is the #1 rated Idea Management solution for companies of all sizes. Its simplicity, fast onboarding and smart automation features mean you can accelerate your innovation without compromises. More than 100+ idea management features let you build your innovation funnel just the way you like.
Collect and manage ideas, engage customers and employees in your innovation, automate workflows and report on your innovation impact. Ideanote supports your business with easy idea and innovation management, open innovation challenges, continuous innovation and by lifting your employee engagement.
Use goal-driven idea collections to capture ideas from anyone in seconds - and end up with ideas that you’ll actually want to act on.
Use goal-driven idea collections to capture ideas from anyone in seconds - and end up with ideas that you’ll actually want to act on.
Use goal-driven idea collections to capture ideas from anyone in seconds - and end up with ideas that you’ll actually want to act on.
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I've been using Ideanote for less than 6 months but it really helpful with my job! I work as Project Manager, Designer for Game Development company and everyday I work with our community members, Ideanote helps me to gather ideas and innovation from community, brainstorming with them and see what they need because the members can write their ideas too!
Based on our record, NumPy seems to be a lot more popular than Ideanote. While we know about 107 links to NumPy, we've tracked only 1 mention of Ideanote. 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.
In NumPy with * or multiply(). ` or multiply()` can multiply 0D or more D arrays by element-wise multiplication. - Source: dev.to / about 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 / 6 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
From real-time whiteboards to goal-oriented idea collections with idea management. - Source: dev.to / over 2 years ago
Pandas - Pandas is an open source library providing high-performance, easy-to-use data structures and data analysis tools for the Python.
The Guide - The Guide is a two-pane outliner - a program that allows you to arrange text notes in a tree-like...
Scikit-learn - scikit-learn (formerly scikits.learn) is an open source machine learning library for the Python programming language.
Monkkee - Keep a private journal securely on the Internet – to provide a convenient user experience your...
OpenCV - OpenCV is the world's biggest computer vision library
Brightidea - With over 2 million users worldwide and $15+ billion in recorded business impact, Brightidea is ranked as the #1 Idea Management Platform globally and is the market leader in innovation management.