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)
(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.
(2/2) The book covers different optimization methods, such as:
✅ Mathematical Optimization
✅ Linear Optimization
✅ Network Optimization
✅ Convex Optimization
✅ Stochastic Optimization
I was so, SO happy to leave Slack behind. And, remembering how that last group of people used it: YEESH.
“If you use Slack for work, your messages and DMs are now being used to train the company’s machine learning features — and everyone is opted in by default.
“A quiet Individual users can’t opt out either, something critics have called a “privacy mess.”
If you use Slack for work, your messages and DMs to friends and colleagues are now being used to train the company’s machine learning features — and everyone is opted in by default.
A quiet update to the company’s policy suggests messages, data and files sent by users are helping Slack to improve its in-app features like channel recommendations, search results and emoji suggestions, reports @PCMag. Individual users can’t opt out either, something critics have called a “privacy mess.”
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!
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.
(2/2) The course covers the following topics:
✅ Deep learning foundation
✅ Computer vision
✅ Deep generative modeling
✅ Reinforcement learning
✅ Robot learning
✅ Text to image