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LabBros's Projects

aerosolve icon aerosolve

A machine learning package built for humans.

amazon-dsstne icon amazon-dsstne

Deep Scalable Sparse Tensor Network Engine (DSSTNE) is an Amazon developed library for building Deep Learning (DL) machine learning (ML) models

awesome-robotics icon awesome-robotics

A curated list of awesome links and software libraries that are useful for robots.

baselines icon baselines

OpenAI Baselines: high-quality implementations of reinforcement learning algorithms

cas-eval icon cas-eval

Clicks-Attention-Satisfaction Evaluation Model and Metric

citable-data icon citable-data

Data, in citable form, produced by the Coudert research group

cleverhans icon cleverhans

A library for benchmarking vulnerability to adversarial examples

collenchyma icon collenchyma

Extendable HPC-Framework for CUDA, OpenCL and common CPU

convnetjs icon convnetjs

Deep Learning in Javascript. Train Convolutional Neural Networks (or ordinary ones) in your browser.

cuticula icon cuticula

Machine Intelligence Preprocessing Framework

datalab icon datalab

Interactive tools and developer experiences for Big Data on Google Cloud Platform.

datasets icon datasets

My datasets - Original data or Aggregated / cleaned / restructured existing datasets. Released here under Creative Commons B

deepdetect icon deepdetect

Deep Learning API and Server in C++11 with Python bindings and support for Caffe, Tensorflow and XGBoost

ecosystem icon ecosystem

Integration of TensorFlow with other open-source frameworks

elf icon elf

An Extensive, Lightweight and Flexible Research Platform for Real-time Strategy Games

facets icon facets

Visualizations for machine learning datasets

fairseq-py icon fairseq-py

Facebook AI Research Sequence-to-Sequence Toolkit written in Python.

fold icon fold

Deep learning with dynamic computation graphs in TensorFlow

frog icon frog

A machine learning library for Rust.

gan-playground icon gan-playground

GAN Playground - Experiment with Generative Adversarial Nets in your browser. An introduction to GANs.

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