Scikit-learn might be a bit more popular than Prezi. We know about 28 links to it since March 2021 and only 22 links to Prezi. 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.
Hello fellow privacy enthusiasts, a very long time ago used Prezi for creating slides for a school presentations. I am able to find back to these as they contain my name. I would very much like to have these deleted, but I do not know the account that was used to create this as it was back in 2014. Source: about 1 year ago
If the speaker is able to use notes that aren't the slide (they're not relying on the slides being shown to the audience to be their own speaker notes), then I use the theory that the slides should provide "context, not content", except for specific details that someone might want to take down in their notes or have access to later, such as a citation. Otherwise, it's all about context, which of course includes... Source: about 1 year ago
Use the notes area of a slide to provide the details. If you share the deck or look back on it later the details of what was covered is there but it will help you keep the main presentation clean. There are also tools like highnote.io and prezi.com that can help you structure your presentations very well. Source: about 1 year ago
I have heard that platforms like canva, highnote.io and prezi.com presentations are pretty good. They have really modern outlooks and they have a large library of free content. Their licensing terms are relatively generous as well. What do you use? Source: about 1 year ago
If you want a really flashy presentation, Prezi is another one that no one's mentioned yet. Source: about 1 year ago
Online Courses: Coursera: "Machine Learning" by Andrew Ng EdX: "Introduction to Machine Learning" by MIT Tutorials: Scikit-learn documentation: https://scikit-learn.org/ Kaggle Learn: https://www.kaggle.com/learn Books: "Hands-On Machine Learning with Scikit-Learn, Keras & TensorFlow" by Aurélien Géron "The Elements of Statistical Learning" by Trevor Hastie, Robert Tibshirani, and Jerome Friedman By... - Source: dev.to / 2 months ago
Firstly, we need a connection to Memgraph so we can get edges, split them into two parts (train set and test set). For edge splitting, we will use scikit-learn. In order to make a connection towards Memgraph, we will use gqlalchemy. - Source: dev.to / 11 months ago
The ML component is based on scikit-learn which differentiates it from purely list-based filters. It couples this with a full-featured wireless router (RaspAP) in a single device, so it fulfills the needs of a use case not entirely addressed by Pi-hole. Source: about 1 year ago
Finally, when it comes to building models and making predictions, Python and R have a plethora of options available. Libraries like scikit-learn, statsmodels, and TensorFlowin Python, or caret, randomForest, and xgboostin R, provide powerful machine learning algorithms and statistical models that can be applied to a wide range of problems. What's more, these libraries are open-source and have extensive... Source: about 1 year ago
Scikit-learn is a machine learning library that comes with a number of pre-built machine learning models, which can then be used as python wrappers. Source: about 1 year ago
Microsoft PowerPoint - Microsoft PowerPoint empowers you to create clean slideshow presentations and intricate pitch decks and gives you a powerful presentation maker to tell your story.
Pandas - Pandas is an open source library providing high-performance, easy-to-use data structures and data analysis tools for the Python.
Keynote - Keynote for Mac, iOS, and iCloud lets you make dazzling presentations. Anyone can collaborate — even on a PC. And it’s compatible with Apple Pencil.
OpenCV - OpenCV is the world's biggest computer vision library
Google Slides - Create a new presentation and edit it with others at the same time — from your computer, phone or tablet. Free with a Google account.
NumPy - NumPy is the fundamental package for scientific computing with Python