ggplot2
H2O
ggplot2 | H2O | |
---|---|---|
62 | 10 | |
6,328 | 6,730 | |
0.5% | 0.7% | |
9.4 | 9.7 | |
6 days ago | 7 days ago | |
R | Jupyter Notebook | |
GNU General Public License v3.0 or later | Apache License 2.0 |
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
ggplot2
- ggplot2
- Ask HN: How do you build diagrams for the web?
-
Visualizing shapefiles in R with sf and ggplot2!
ggplot2
- Ask HN: What plotting tools should I invest in learning?
-
Relative frequency of letters in five-letter English words (Wordle aid) [OC]
I got the list of five-letter words from the words package in R, created the QWERTY keyboard grid with base R and tibble, and visualized the data with geom_tile in the ggplot2 package.
-
[OC] U.S. News & World Report Best Colleges: 2002 to 2023
Thanks, it's an interesting idea! I definitely could implement this with scale_fill_gradientn) in ggplot2.
-
Facts about Aaron Boone's Ejections as Manager
I used the ggplot2 package in R to create these figures.
-
Fueling Innovation and Collaborative Storytelling
This might not be at the top of your list, but science fiction often presents advanced data analysis and visualization technologies. Open source data analysis tools such as Python's Pandas and R's ggplot2 have revolutionized the field, making complex data manipulation and visualization accessible to all. In the science fiction novel The Martian, astronaut Mark Watney uses a variety of data analysis and visualization tools to survive on Mars. He uses Python's Pandas to clean and organize data, and he uses R's ggplot2 to create visualizations of his data. These tools allow him to make sense of the vast amounts of data and help him to make critical decisions about his survival.
-
[OC] Visualizing Financial Market Returns Across Many Asset Classes via Heatmaps
Sorry about the slow reply, but the auto-moderator seems to be deleting my comments (for some unknown reason). I will try once more: the geom_tile function in ggplot2.
-
[OC] Forbes List of Highest-Earning Musicians: 1987 to 2021
Visual cues are a much better idea, thanks! Unfortunately, I don't know how to do that in ggplot2, either (I created these figures in R).
H2O
-
Really struggling with open source models
I would use H20 if I were you. You can try out LLMs with a nice GUI. Unless you have some familiarity with the tools needed to run these projects, it can be frustrating. https://h2o.ai/
- Democratizing Large Language Models
-
Interview AI Coach - by email
Here is the transcribed portion of what you sent: Within this project, or another example, for some examples of maybe encountering resistance or someone who's just like a specific person who seemed really opposed to your ideas that you had to influence or win over, and how you approach that sort of personality-based problem. Yeah, great question. So, at Lineate, I mentioned earlier that I helped to kind of upscale the entire workforce. We're talking 200 engineers, marketing folks, sales folks, account managers. And I had just, so in an effort to kind of upscale this and identify opportunities for machine learning, I followed Andrew Ng's framework for approaching ML in the enterprise. Basically, it's like one-pagers, where I define the problem statement. Do we have access to the data? Do we have data privacy or regulation concerns? What are some risk assumptions, success criteria, all that stuff. So, I put together like 20 plus one-pagers across all the different opportunities, and I generated a successful proof of concept with the team after it was a failure, of course, at first, but we turned it into a success. And part of this Andrew Ng framework in AI in the enterprise, it's like you want to generate a center of AI excellence, where it's like you share best practices with the rest of the organization. So, nobody told me that I had to do this, but this is kind of like something that I aspire towards. And in the process of trying to be inclusive with the 200 engineers, there was one engineer who was unwilling to participate. There was a phase two of his project that had an AI component that used the same tool that we used in Google Cloud. And I opened a Slack channel with our team and himself to try to get him to share what he's working with so that my team can also share what learnings we had with that tool. He just wasn't willing to participate. I just couldn't understand. It's like, how can you not? I mean, this isn't your benefit. This is a team. You got to be a team player. So, my first reaction was like, seek to understand, what's the context here? What's the background? I asked around. I talked to engineers who worked with him. I talked to higher ups without kind of like mentioning that this person was problematic, but just to understand what the nature is. And it turns out he doesn't report to the director of Solution Architecture Engineering. Instead, he reports directly to the CEO. I was like, oh, that's interesting. It turns out he came into the company through an acquisition. He was like a startup founder. So, he's used to running the show. So, when it comes to working with a team of 200 engineers, he's a superstar in terms of performance, but maybe team play-wise, not so much. So, understanding that context really helped me understand where he's coming from. And the next thing I did was I tried to anticipate, what are some of his needs? What can I do to help him reach his goals? And he wanted to, of course, do well on his project because he's a high performer. He wants to be aware of any risks early on. So, what I did was I got a hold of a sample dataset from the work that he was doing. And since I had access to some tools that he did not have, like h2o.ai, DataRobot, I took some of his data samples, put it into these tools, ran different algorithms on them, like GBM, different neural networks, to get a sense of what does a confusion matrix look like? What is this two by two matrix of true positive, false positive, and stuff like that. So, I was able to deliver some of these confusion matrices to him so that he's aware of it. And another thing is, I said, the tool that you're using is the same tool that we used. Well, guess what? It doesn't do so well in a sub-10 millisecond environment, which is one of the needs of your project. You might want to consider SageMaker endpoint where you can deploy artifact there so that this latency requirement is not a problem. So, I kind of anticipated where his needs are, being proactive to help him, offer advice where I anticipated that he needed help and extra guidance. He started kind of like more open up. And guess where I shared some of these insights? I shared it in the channel that he originally did not want to participate in. And I said, I'm going to share it in this channel. So, then he takes a look there and he starts replying to that. So, now I kind of like, kind of guided him to take one step into like this channel. So, now whatever reply he says, then my engineers can see that reply. And now it's like we have a team spirit going on now. So, that's like how I kind of got him from not wanting to participate to now participating. And on top of that, I also did like these company wide webinars where I showcase our teams. I put their profile pictures on the front slide. So, when everybody dials in, then they could see like, these are the people on my team. Here's what we're working on. And I asked him, you're really good at what you do. I would love to include you in this team in the next meeting. Are you okay with it if I put your profile picture on the front page? And he said, yes, right away. So, like helping to kind of like, because it's not like I need the credit. I just distribute some of the visibility to some of these star engineers and kind of like in exchange, you get like better collaboration. And that goal of the AI Center for Excellence for better kind of sharing best practices and learnings. So, I think by doing that, I was able to kind of like turn an icky situation into something that became a team effort. That's awesome. Love it.
-
Top 10+ OpenAI Alternatives
H2O.ai
-
Best machine learning framework(s) for production
Thanks for the input. To clarify, I am more focused on choosing the modeling framework(s) that makes the most sense to use for future production. For example, is h2o.ai a good framework for training models for later deployment (through something like elastic beanstalk, Flask API's etc.)? I came across a number of mentions of Tensorflow, however it is focused on neural nets while I also want to use classic models such as random forests, etc.
-
Time Series Analysis - Too Narrow a Dataset / Feature Set?
I've also initialised an instance of H2O.ai, so I can parse into the server each product, by store, segmented. It can then train the models, determine which model is the most performant, and then save it. Because the variability of different product SKU, at different hospitals, is substantial.
- A Tiny Grammar of Graphics
-
20+ Free Tools & Resources for Machine Learning
H2O.ai H2O is a deep learning tool built in Java. It supports most widely used machine learning algorithms and is a fast, scalable machine learning application interface used for deep learning, elastic net, logistic regression, and gradient boosting.
-
Data Science Competition
H20
-
[PAID] Looking for Phaser.js game developer
Built and founded various web3 projects for last 2 years such as OpenArt and 8RealmDojo for last 2 years as well as being high performing student in CTU in Prague and SeoulTech. Was offered internships in Amazon and H2O.ai. Created robots assistants using robots from SoftBank.
What are some alternatives?
Altair - Declarative statistical visualization library for Python
MLflow - Open source platform for the machine learning lifecycle
tmap - R package for thematic maps
scikit-learn - scikit-learn: machine learning in Python
vega - A visualization grammar.
pycaret - An open-source, low-code machine learning library in Python
dplyr - dplyr: A grammar of data manipulation
LightGBM - A fast, distributed, high performance gradient boosting (GBT, GBDT, GBRT, GBM or MART) framework based on decision tree algorithms, used for ranking, classification and many other machine learning tasks.
worldfootballR - A wrapper for extracting world football (soccer) data from FBref, Transfermark, Understat and fotmob
Prophet - Tool for producing high quality forecasts for time series data that has multiple seasonality with linear or non-linear growth.
glue - Glue strings to data in R. Small, fast, dependency free interpreted string literals.
FLAML - A fast library for AutoML and tuning. Join our Discord: https://discord.gg/Cppx2vSPVP.