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5001-kaggle

acme

A library of reinforcement learning components and agents

arxiv-sanity-preserver

Web interface for browsing, search and filtering recent arxiv submissions

bsuite

bsuite is a collection of carefully-designed experiments that investigate core capabilities of a reinforcement learning (RL) agent

community

This repository holds the content submitted to https://cloud.google.com/community. Files added to the tutorials/ will appear at https://cloud.google.com/community/tutorials.

deeplearning-models

A collection of various deep learning architectures, models, and tips

dotsandboxes

Python Code for Dots And Boxes

faiss

A library for efficient similarity search and clustering of dense vectors.

fastseqsearch

FastSeqSearch combines the strengths of both ANN and DTW while mitigating their weaknesses. It uses FAISS to quickly narrow down the candidate pool, making the initial search extremely fast. Then, it employs fastdtw to refine these candidates with detailed sequence comparisons, maintaining accuracy without the need for excessive computation.

grok

Grok open release

ml-agents

Unity Machine Learning Agents Toolkit

neural-slam

Pytorch code for ICLR-20 Paper "Learning to Explore using Active Neural SLAM"

penn-state-emotion-classication

project-down-the-rabbit-hole

project-x-spectrum-4202024

The X Spectrum Analysis project aims to analyze and visualize the spectrum of X media posts to determine where they fall on a topic's distribution, whether moderate or extreme, based on vector distances among posts. This tool is instrumental in identifying trends, polarization, and bot-generated content within social media discussions.

qdrant

Qdrant - High-performance, massive-scale Vector Database for the next generation of AI. Also available in the cloud https://cloud.qdrant.io/

rl_pro_telu

seed_rl

SEED RL: Scalable and Efficient Deep-RL with Accelerated Central Inference. Implements IMPALA and R2D2 algorithms in TF2 with SEED's architecture.

sonnet

TensorFlow-based neural network library

tensorflow

Computation using data flow graphs for scalable machine learning

vectorizedchristofidesalgorithm

The Vectorized Christofides Algorithm combines the strengths of the Christofides algorithm with modern computational techniques. By leveraging vectorized operations, the algorithm achieves significant speedup without sacrificing accuracy. This approach ensures efficient handling of large datasets, making it a robust solution for solving the TSP.

workflow