mat kelcey's repositories

bnn

bee detection tensorflow conv net for a rasp pi on side of a hive

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drivebot

tensorflow deep RL for driving a rover around

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malmomo

tensorflow deep RL hacking on minecraft with malmo

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procedural_objects

blender based random procedural object generation for bullet grasping

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dither_net

a GAN experiment to generate dithers minimising pixel change between frames for an eink very slow movie player

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tcn_grasping

pybullet grasping with time contrastive network embeddings

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ros-hc-sr04-node

raspberry pi c++ ROS hc-sr04 sonar node

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farmbot_api

python api for bulk farm bed sweeps, image capture and plant detection

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movidius_minimal_example

minimal example of training tf conv net and deploying to movidius neural compute stick

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evolved_channel_selection

rather than use all 13 channels in a multi spectral image for classification can we train a model that is robust to all combos, at all resolutions, and use a genetic algorithm to choose which are the most valuable?

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movidius_bug_reports

repro cases for bugs related to NCS api

cached_dilated_causal_convolutions

1D dilated causal convolutions with extreme caching for 5µs inference on an FPGA

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ensemble_net

training ensembles with jax

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bullet_grasping

hacking with the bullet grasping setup

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DaisyExamples

Examples for the Daisy Platform

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embedding-cache

local file cache over tf.hub resnet inception feature vectors

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evolved_cartpole

evolving a gym cartpole controller; directly against bytes of a tf lite microcontroller model

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large_batch

quick hack with global batch size of 172,000

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aliens_drum_kit

building sample embeddings so we can extract a drum kit from the audio of "aliens"

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minimal_pybullet_on_dataflow_eg

Minimal example of running Pybullet under Google Cloud Dataflow

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eurorack-pmod

Hardware and gateware for getting started in FPGA-based audio synthesis with open source tools.

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dm_pix

PIX is an image processing library in JAX, for JAX.

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Mannequins-Technical-Maps

Technical Maps for the Mannequins Modules

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ood_focal_loss

out of distribution using focal loss quick experiments

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shared_data

some shared dataset

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three_strikes_rule

the third time i explain something i write it up

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