home.work.caltech.edu
Introduction to machine learning covering basic theory, algorithms and applications.
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lev.la
Pushing the frontier of neural imaging.
lex.page
Creates or edits images with generative models and visual controls.
github.com
A generic approach that allows to mimic most factorization models by feature engineering.
libra.cs.uoregon.edu
Algorithms for learning and inference with discrete probabilistic models.
github.com
Lifelines is a complete survival analysis library, written in pure Python.
github.com
Lightly is a computer vision framework for self-supervised learning.
github.com
Pretrain computer vision models on unlabeled data for industrial applications.
github.com
Explaining the predictions of any machine learning classifier.
github.com
A comprehensive toolkit to build Machine Learning applications with Rust.
alias-i.com
A tool kit for processing text using computational linguistics.
github.com
A graph sampling extension library for NetworkX with a Scikit-Learn like API.
livebench.ai
A Challenging, Contamination-Free LLM Benchmark.
github.com
A header-only C++11 Neural Network library. Low dependency, native traditional chinese document.
llamahub.ai
A hub of integrations for LlamaIndex including data loaders, tools, vector databases, LLMs and more.
blog.steelph0enix.dev
Psst, kid, want some cheap and small LLMs?
github.com
Provides a central interface to connect your LLM's with external data.
github.com
LlamaIndex is a data framework for your LLM applications.
llamaindex.ai
A data framework for building LLM applications over external data.
github.com
Build agents which are controlled by LLMs.
github.com
LLM App is a Python library that helps you build real-time AI-powered data pipelines with few lines of code.
llm-eval.github.io
Research on evaluation of LLMs conducted by Microsoft Research and other collaborated institutes. (Updated at: 2023/10).
airtrain.ai
No-code batch compute platform for LLM evaluation and tuning workloads.
docs.clarifai.com
Documentation for LLM Evaluation Clarifai Guide, covering setup, features, and practical usage.