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
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.
Stanford University released a new course last week focusing on Deep Generative Models. The course, by Prof. Stefano Ermon, focuses on the models beyond GenAI models.
(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.
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. 🧵👇🏼
This summer there will be four courses 😯:
Computational Neuroscience, NeuroAI, Deep Learning, and Computational Tools for Climate.
Mentors will hold a one-hour meeting every week with a small cohort of students, where they will discuss with them and help them progress in their journey in industry and academia.
Le thème : les modèles de language et la robotique open hardware. Si ça vous intéresse de découvrir une autre facette que Skynet et la machine à billet,
Here is a great summary or glossary doc about LLM by Aman Chadha. This long doc provides a summary of some of the main concepts related to LLM. This includes topics such as:
✅ Embeddings
✅ Vector database
✅ Prompt engineering
✅ Token
✅ RAG
✅ LLM performance evaluation
✅ Review main LLMs
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. 🧵👇🏼
Very nice picture that was shared by Ronald van Loon on X, you can discuss if the categories are complete and correct, but it illustrates that the field of AI is much more then just transformers/LLMs. #AI#Machinelearning#neuralnetworks#deeplearning#LLM#Transfomers
Meta released today Llama 3, the next generation of the Llama model. LLama 3 is a state-of-the-art open-source large language model. Here are some of the key features of the model: 🧵👇🏼
Google released a new repo with a collection of guides and examples for the Gemini API. This includes a set of guides for prompt engineering and examples of the API features 👇🏼
(1/2) Models Demystified - A Practical Guide from t-tests to Deep Learning 🚀👇🏼
The Models Demystified is a new book by Michael Clark and Seth Berry that focuses on the mechanizing of core data science algorithms. That includes the following topics:
✅ Linear and logistic regression
✅ Generalized Linear Models
✅ Regularization methods
✅ Model training approaches
✅ Deep learning and neural networks
✅ Causal Modeling
Whenever I see OpenAI's Sam Altman with his pseudo-innocent glance, he always reminds me of Carter Burke from Aliens (1986), who deceived the entire spaceship crew in favor of his corporation, with the aim of getting rich by weaponizing a newly discovered intelligent lifeform.
Je bosse au 4/5 sur les modèles de langage (LLM, parfois appelées IAs) et à 2/5 sur la robotique open hardware AMA (jlai.lu) French
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