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
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
#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.
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
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.
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. 🤔
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
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
@ramikrispin@BenjaminHan How do this and corenet (https://github.com/apple/corenet) fit together? The corenet repo has examples for inference with MLX for models trained with corenet; is that it, does MLX not have, e.g., activation and loss fns, optimizers, etc.?
@Lobrien@BenjaminHan The corenet is deep learning application where the MLX is array framework for high performance on Apple silicon. This mean that if you are using mac with M1-3 CPU it should perform better when using MLX on the backend (did not test it myself)