Trying something new, everyone is guaranteed an interview! Open interviews! For a limited time no one will be skipped (except for clear cases of abuse).
So we still have about 10 more 100% remote positions to hire for full-time market-fair positions here at QOTO/CleverThis.
100% remote, work from anywhere, even the beach, market-fair offers. Ethics first, we treat our people like family.
We have an urgent need for Machine learning experts with a background in NLP and Deep Learning (Natural Language Processing and Neural Networks). There is a focus on Knowledge Graphs, Mathematics, Java, C, looking for Polyglots.
We are an open-source first company, we give back heavily to the OSS community.
We need everything from jr to sr, data scientist to programmer. If your IT and your good, you might be a fit.
I will personally be both your direct boss, and hiring manager. I am also the founder and inventor.
The NLP position can be found at this link, other positions can be found on the menu bar on the left:
If you would like to submit yourself for an interview, which for a limited time I am guaranteeing you will get a first stage interview, then you can submit your application here, and even schedule your interview as you apply, instantly!
So… Big Tech is allowed to blatantly steal the work, styles and therewith the job opportunities of thousands of artists and writers without being reprimanded, but it takes similarity to the voice of a famous actor to spark public outrage about AI. 🤔
Please boost for reach if this kind of stuff interests you. Will post more on this later.
Once upon a time, there was a cool emulator frontend called Retroarch. This emulator wasn't accessible until I and a few other gamers went to them and asked about adding accessibility. An amazing person known as BarryR made it happen. Now, if you turn on accessibility mode in settings, or pass the "--accessibility" (or something like that) flag on the command line, you get spoken menus, including the emulator's pause menu, good for saving states and such. Then, using PIL and other image processing Python utilities, running a server and hooking into Retroarch, the script allowed players to move around the map, battle, talk to NPC's, ETC. The only problem was, no one wanted to test it. The blind gaming community pretty much spoke, saying that we want new games. We want cool new, easy accessibility. So that's what we have no, follow the beacon or get sighted help in the case of diablo and such. It's sad, but meh. It's what we wanted I guess. No Zelda for us. So, this is about as far as he got:
To expand on what devinprater was saying: I am working on an accessibility pack/service for Final Fantasy 1 for the NES (this was what was shown in the latest RetroArch update). The idea is similar to how Pokemon Crystal access works, but it's using the RetroArch AI Service interface to do so.
Right now, the FF1 access service is mostly done, but I need more testers to try it out and give me feedback on how it's working. Right now, you can get up to the point where you get the ship, but there's no code to deal with how the ship moves, so that still needs to be done. Likewise with the airship later on.
The service works the latest version of RetroArch, on linux and mac, but not windows. This is due to how nvda reads out the text and until the next major update to nvda (which will have a feature to fix this), it'll have to wait. If you have those, I (or maybe devinprater) can help you set it up on mac/linux to test out. The package itself is available at: https://ztranslate.net/download/ff1_pac … zip?owner=
Version 1.7.1 of the NeuralForecast #Python library was released last month by Nixtla. The NeuralForecast library, as the name implies, provides a neural network framework for time series forecasting. 🧵👇🏼
The MLX is Apple's framework for machine learning applications on Apple silicon. The MLX examples repository provides a set of examples for using the MLX framework. This includes examples of:
✅ Text models such as transformer, Llama, Mistral, and Phi-2 models
✅ Image models such as Stable Diffusion
✅ Audio and speech recognition with OpenAI's Whisper
✅ Support for some Hugging Face models
(1/2) Hands-On Mathematical Optimization with Python 🚀
The Hands-On Mathematical Optimization with Python book by Krzysztof Postek, Alessandro Zocca, Joaquim Gromicho, and Jeffrey Kantor provides the foundation for mathematical optimization. As the name implies, the book is hands-on with Python examples, mainly using Pyomo.
The End To End Data Science With R is a new book by Rene Essomba. The book, as the name implies, focuses on the core data science applications using R ❤️. This book covers the following topics:
✅ Exploratory data analysis
✅ Data visualization
✅ Supervised learning
✅ Unsupervised learning
✅ Time series
✅ Natural language processing
✅ Image classification
Version 0.12.0 of the skforecast Python library for time series forecasting with regression models was released this week. The release includes new features, updates for existing ones, and bug fixes. 🧵👇🏼
(1/2) Google released a new foundation model for time series forecasting 🚀
The TimeFM (Time Series Foundation Model) is a foundation model for time series forecasting applications. This pre-trained model was developed by the Google Research team. It joins the recent trend of leveraging foundation models for time series forecasting, which includes Salesforce's Moirai and Amazon's Chronos.
MIT launched the 2024 edition of the Introduction to Deep Learning course by Prof. Alexander Amini and Prof.Ava Amini. The course started at the end of April and will run until June. The course lectures are published weekly. The course syllabus keeps changing from year to year, reflecting the rapid changes in this field.
The Machine Learning for Beginners by Microsoft Developer is an introductory course for classical machine learning. This crash course mainly focuses on regression analysis with Python 🐍, and it covers topics such as:
✅ General setup
✅ Cleaning data
✅ Data visualization
✅ Regression models
✅ Polynomial regression
✅ Logistic regression
Hello Mastodon, I know that a lot of you discuss the high environmental cost (such as energy use and water use) of AI and I hope that some of you could reply with authoritative publications/links regarding this problem! I want to try to convince an environmental science colleague #climatechange#AI#chatgpt#energy#technology#machinelearning#llm
FreeCodeCamp released today a new course for fine tuning LLM models. The course, by Krish Naik, focuses on different tuning methods such as QLORA, LORA, and Quantization using different models such as Llama2, Gradient, and Google Gemma model.
First and foremost, we had more than 20 wonderful participants at #YoMos2024, ranging from Bachelor- to #PhD- level #students all modelling #ecological systems. Everyone had the chance to present their projects and methods and we had vivid question+discussion rounds afterwards. Although #YoMos seems like a niche group already, we heard about a bandwidth of methods including mechanistic #eco(-evo)-models, #SDMs, #climate and vegeation models, network models, #Machinelearning, #AI and much more.
College Precalculus – Full Course with Python Code by Ed Pratowski and freeCodeCamp focus on the foundation of calculus with Python implementation. This 12 hours course covers the following topics:
✅ Core trigonometry
✅ Matrix operation
✅ Working with complex numbers
✅ Probability
#AI and #MachineLearning models are shifting a number of core assumptions on which the various Web stakeholders have been relying on for years.
In this presentation at the @w3c member meeting in #Hiroshima 🇯🇵, @dontcallmeDOM reviews the systemic impact and possible mitigation the Web community should consider to ensure the long term prosperity of the #Web in the face of these changes.
Here is a short e-book with a sequence of tutorials on the scientific Python ecosystem for beginners. This includes topics such as:
✅ Working with numerical data using NumPy
✅ Data visualization with Matplotlib
✅ Scientific computing with SciPy
✅ Statistics with Python
✅ Machine learning with scikit-learn