Personal Information
Organización/Lugar de trabajo
Estonia Estonia
Ocupación
PhD in Neuroscience and Artificial Intelligence. Machine Learning Architect at OffWorld.
Sector
Technology / Software / Internet
Sitio web
www.ikuz.eu
Acerca de
PhD in Neuroscience and Artificial Intelligence from Computational Neuroscience Lab at University of Tartu, Estonia with background in computer science, machine learning, deep learning, brain-computer interfaces, neuroscience.
Machine Learning Architect at OffWorld, designing machine learning framework for autonomous mining robots.
Studying the synergy between neuroscience and machine learning, aiming to extend the human brain with the computational abilities of artificial systems.
Etiquetas
deep learning
neuroscience
machine learning
artificial neural network
dnn
brain-computer interface
bci
human brain
reinforcement learning
backpropagation
deepmind
human brain augmentation
biological neuron
successor representations
grid cells
marr
data
introduction
philosophy
incomplete
intuition
inconsistent
gödel
ai
go
alphago
ilsvrc
imagenet
residual network
keras
autoencoder
lstm
predictive coding
visual cortex
ventral stream
nips
turing machines
neuroimaging
fmri
robotic arm
intracranial
article
kalman filter
opengl
gpu
gpgpu
cuda
opencl
shader
brain-computer interfaces
fourier
som
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Presentaciones
(17)Recomendaciones
(2)Introduction to Common Spatial Pattern Filters for EEG Motor Imagery Classification
Tatsuya Yokota
•
Hace 11 años
Lecture9: 123.101
Gareth Rowlands
•
Hace 15 años
Personal Information
Organización/Lugar de trabajo
Estonia Estonia
Ocupación
PhD in Neuroscience and Artificial Intelligence. Machine Learning Architect at OffWorld.
Sector
Technology / Software / Internet
Sitio web
www.ikuz.eu
Acerca de
PhD in Neuroscience and Artificial Intelligence from Computational Neuroscience Lab at University of Tartu, Estonia with background in computer science, machine learning, deep learning, brain-computer interfaces, neuroscience.
Machine Learning Architect at OffWorld, designing machine learning framework for autonomous mining robots.
Studying the synergy between neuroscience and machine learning, aiming to extend the human brain with the computational abilities of artificial systems.
Etiquetas
deep learning
neuroscience
machine learning
artificial neural network
dnn
brain-computer interface
bci
human brain
reinforcement learning
backpropagation
deepmind
human brain augmentation
biological neuron
successor representations
grid cells
marr
data
introduction
philosophy
incomplete
intuition
inconsistent
gödel
ai
go
alphago
ilsvrc
imagenet
residual network
keras
autoencoder
lstm
predictive coding
visual cortex
ventral stream
nips
turing machines
neuroimaging
fmri
robotic arm
intracranial
article
kalman filter
opengl
gpu
gpgpu
cuda
opencl
shader
brain-computer interfaces
fourier
som
Ver más