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
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. 🤔
#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.
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.
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.
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
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
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
(1/2) Shiny Apps for demystifying statistical models and methods 🚀
This is a cool website that explains different statistical concepts with the use of interactive Shiny Apps. Ben Prytherch made this website from the Department of Statistics at Colorado State University.