baberabb Goto Github PK

18335

18.335 - Introduction to Numerical Methods course

agieval

aspect_extraction

Reproduction project for the RNLP class, University of Tübingen, MA-ISCL, Summer Semester 2021.

backend_test

bayesian-surprise

Bayesian Weighting for De-Biasing Thematic Maps

catalog_of_copyright_entries_project

NYPL Project to transcribe and parse pages from the US Catalog of Copyright Entries

cce-python

Python tools for processing data from the Catalog of Copyright Entries

cce-renewals

Tab-delimited versions of Catalog of Copyright Entries renewals

cheetah

Mac app for crushing remote tech interviews with AI

clean-code-python

:bathtub: Clean Code concepts adapted for Python

cnn_lstm

comparison-on-logistic-regression-and-random-forest

Comparing the performance of Random Forests and Logistic Regression on a binary classification problem to predict whether a medical patient’s breast tumor is malignant or benign

cookbook

Deep learning for dummies. All the practical details and useful utilities that go into working with real models.

dqn

Code implimentation for DQN and Soft Actor Critic

drl

coursework directory

facerecognition

facerecognition_video

Facial Detection and Emotion Classification

gpt-fast

Simple and efficient pytorch-native transformer text generation in <1000 LOC of python.

gym

A toolkit for developing and comparing reinforcement learning algorithms.

ivy

Unified AI

jupyterongcp

Setting Up Jupyter Notebooks On Google Cloud

langchain

⚡ Building applications with LLMs through composability ⚡

licensed-pile

Repo to hold code and track issues for the collection of permissively licensed data

lm-evaluation-harness

A framework for few-shot evaluation of autoregressive language models.

logiqa2.0

Logiqa2.0 dataset - logical reasoning in MRC and NLI tasks

london-stopandsearch

project for principles of data science module at City, University of Londo

minihf

MiniHF is an inference, human preference data collection, and fine-tuning tool for local language models. It is intended to help the user develop their prompts into full models.

mlx-examples

Examples in the MLX framework

module-1

Module 1 - Autodifferentiation

module-2

Module 2 - Tensors