The Living Thing / Notebooks - machine learning

The Living Thing / Notebooks :

Javascript machine learning —
2017-01-13… 2018-04-17
Probabilistic deep learning —
2017-01-11… 2018-03-23
Compressing artificial neural networks —
2016-10-12… 2018-03-23
Deep learning — [ designing the fanciest usable differentiable loss surface ] —
2016-10-14… 2018-03-22
Natural language processing — [ Dave, although you took very thorough precautions in the pod against my hearing you, I could see your lips move. ] —
2018-01-11… 2018-03-07
Controllerism — [ and other fancy words for making thing happen by waving your arms about on stage ] —
2014-11-17… 2018-03-05
Learnable indexes and hashes —
2018-01-12… 2018-01-12
pytorch — [ #torched ] —
2017-10-31… 2018-01-11
Here’s how I would do art with machine learning if I had to. —
2016-06-06… 2017-11-26
Moral calculus —
2014-08-04… 2017-11-21
Convolutional neural networks —
2017-11-10… 2017-11-10
Recurrent neural networks —
2016-06-16… 2017-10-25
Model interpretation, fairness and trust —
2016-09-01… 2017-10-13
Distributed sensing and swarm sensing —
2014-10-13… 2017-10-12
Learning graphical models from data — [ what is independent of what ] —
2017-09-20… 2017-09-27
Inference in graphical models — [ Given what I know about what I know, what do I know? ] —
2017-09-20… 2017-09-20
(Probabilistic) graphical models —
2014-08-05… 2017-09-11
Brains — [ neural networks made of real neurons, in functioning brains ] —
2014-11-03… 2017-08-15
Quantum-probabilistic graphical models —
2017-08-07… 2017-08-07
Applied psephology —
2016-07-12… 2017-06-20
Generalisation in neural networks — [ Generalisation for street fighters ] —
2017-02-12… 2017-06-20
Why does deep learning work? — [ despite the fact we are totally just making this shit up ] —
2017-05-30… 2017-05-30
Marketing psychology —
2017-04-27… 2017-05-29
Generative deep learning models — [ automatic fractal Trump ] —
2017-05-29… 2017-05-29
Machine learning for physics — [ Turbulent mixing at the boundary between two disciplines with differing inertia ] —
2017-05-15… 2017-05-25
Directed graphical models —
2016-04-29… 2017-04-06
Human behaviour control, methods for —
2017-04-04… 2017-04-04
Musical metrics and manifolds —
2014-09-26… 2017-03-27
Analysis/resynthesis of audio —
2016-01-15… 2017-03-13
Random neural networks —
2017-02-17… 2017-02-19
Hierarchical models —
2015-06-07… 2017-02-11
Machine listening —
2014-10-10… 2017-01-18
Gesture recognition —
2014-10-17… 2017-01-17
Neural network activation functions —
2017-01-12… 2017-01-12
Differentiable learning of automata —
2016-10-14… 2017-01-11
Causal graphical models —
2016-10-26… 2016-10-26
UNSW Stats reading group 2016 - Causal DAGs — [ An introduction to conditional independence DAGs and their use for causal data. ] —
2016-10-17… 2016-10-21
Inference from disorder —
2016-10-19… 2016-10-19
Directed graphical models —
2016-10-18… 2016-10-17
Heterodox neural networks — [ non-deep-learning-type networks for machine learning. ] —
2014-07-25… 2016-10-14
Generalised linear models —
2016-03-24… 2016-08-31
Speech recognition —
2015-02-22… 2016-08-17
Factorisation machines —
2015-02-15… 2016-07-18
Bio computing —
2016-05-29… 2016-05-29
Probably Approximately Correct —
2014-11-24… 2016-05-29
Conditional random fields —
2016-04-20… 2016-04-29
Machine vision —
2015-01-03… 2015-12-15
Multi agent systems —
2015-11-21… 2015-11-21
Tunings —
2015-10-29… 2015-11-08
Distributed sensing —
2014-10-13… 2015-10-14
Grammatical inference —
2011-05-30… 2015-02-19
Wacky regression —
2014-10-29… 2015-01-12