Latest revision as of 00:07, 28 April 2017

Nanometer Pattern Generation System. SPIE Handbook (Seems it might be a 'mod' for an electron beam). "Scan coils". "Beam blanker". 10min for 40x40micron. Scanning capacitance microscopy (SCM). Beam measured in picoAmps. 100k magnification. Uses a 'cup' to measure beam amps. A 'gold standard' is used to calibrate beam. Chip isn't going to be perfectly flat, look at the corners to know the height to focus, raise the stage.

Youtube Video? 2 - Unwatched

Photolithography Overview for MEMS - Unwatched

Intel Talk - Unwatched

Second-harmonic generation

DIY Semiconductor manufacturing In a box

Overview of elisworth

Casting[edit | edit source]

‘quick view’ comparisons of casting materials - Polyester resin is cheap.

Make silicon mold out of clear silicon + cornstarch Auger Mixer for Oogoo

'fiberglass resin' taxidermy

HydroCal (Is this gypsum?), whats the amount the powder makes?

3D Printer[edit | edit source]

Dollo github National Pipe Thread / British Standard Pipe

Electronics[edit | edit source]

Rotory Encoders

Virtual Encoder

Afrotechmods

3d Printing[edit | edit source]

3D Printing

Physics[edit | edit source]

Doc Schuster

Optics[edit | edit source]

This guys page also

Anti-reflective coating

Doc Schuster - Optics

3d printed lenslets for solar

https://en.wikipedia.org/wiki/Nonimaging_optics

DIY 3D printed lenses

Scala[edit | edit source]

Functional Programming Principles in Scala

Functional Program Design in Scala

Parallel programming (Scala)

Big Data Analysis with Scala and Spark

Statistical Learning[edit | edit source]

Stanford University

Finance[edit | edit source]

Finance

Complex Systems?[edit | edit source]

Dave Ackley

Math[edit | edit source]

Maths

Programming[edit | edit source]

Dave Akley - Robust First Computing

Algorithmic thinking

Vulkan Talks

GameDev[edit | edit source]

Part I: Client Side of 64 Network DO’s and DON’Ts for Game Engine Developers

Game Economy Designers

PBR[edit | edit source]

MachineLearning[edit | edit source]

non-artistic-style-transfer
Deep Neural Decision Forests Explained
Deep Learning Patterns
Deep Learning Gallery
Awesome Deep Learning Papers
NanoNets: How to use Deep Learning when you have Limited Data
Deep Learning on medium.com
NIPS2016 - Videos!
ai-software-learns-to-make-ai-software
KDD2016 talks
Center for Brains, Minds and Machines (CBMM)
Nando de Freitas
OpenAI.com - GANs - Read This
A Course in Machine Learning - Free Book
NIPS2016 talks
This has lots of good infos - Includes an AndrewNG talk and recommended papers.
Reddit best papers
Yes you should understand backprop - CS231N lecture on backprop (claims to focus on intuition)
Wide Residential Networks Impl - 3rd party - keras impl
Learning AI if you suck at math
CNN Tricks
RNN CS188
Neural Networks and Deep Learning - Free Online Book
An introduction to Generative Adversarial Networks (with code in TensorFlow)

Ideas[edit | edit source]

Would it be possible to train a network on cut up sections based on how much they cause the neurons to spike? Don't train sections that have a big response already.
Would it be possible to modify the inputs to 'censor' bits that cause over-fitting.
Would it be possible to 'move' and object across the view overtime and learn that it's still the same object as a kind of data-augmentation?

Could an auto-encoder be used to synthesise convolution filters for a pre-initialisation?

Is it possible to learn a fitness function by starting in the goal state and trying to learn how to leave it.
Then learn how to leave that state for a new one.
Would be easier in a discrete, reversible, deterministic world.
- Would need to define how different 2 positions need to be to be considered different states.
- Is is possible to learn what a 'state' is?
- by taking 2 'non-goal' states and learning to move between them without triggering the goal and using a state halfway between as a new state?
- By taking a bunch of 'non-goal' states and and finding the maximum difference between them?
- Or using the distance between goal and non-goal?
- Or by using the distance between 2 non-goal states?
Need to be able to reverse the 'move out of goal'.
- If the actions are reversible and deterministic then just undo them.
- Could relearn how to get from the state to the goal
- How to determine how far away a non-goal state is? How much time/how many actions it takes to get to the goal state from the non-goal state?
What about a key/lock/door puzzle
- By default it wouldn't learn to put the key in the lock as the puzzle would either start in a solved state (or actor would be stuck behind the door).
- Could start the puzzle solved and make it become unsolved as the actor walks backwards, ie you pick up the key when you go though the unlocked door which becomes locked, then have to 'loose' the key it in the place where it's actually obtained. Is this just turning into as complex a problem as solving the puzzle in the first place?

Courses[edit | edit source]

Deep learning at Oxford 2015 - Nando de Freitas (Bad audio quality)... (same guy did this talk)
Foundations of Machine Learning Boot Camp + Deep Learning Specific stuff
Neural networks class - Université de Sherbrooke - via Reddit "A complete and easy to follow course for understanding ANNs". Seems to have some math. Short <15min lectures.
undergraduate machine learning at UBC 2012
Machine Learning 2013
videolectures.net
Berkeley - CS 294: Deep Reinforcement Learning, Spring 2017
Berkeley - CS 294-129 10/5/16
Berkeley - CS 294-131: Special Topics in Deep Learning Fall, 2016
Stanford CS231n Winter 2016: Convolutional Neural Networks for Visual Recognition. - Convnets in pratice 15min in talks about retraining just a littlebit..., at <36min talks about convolutions with 3x1 and 1x3.
CS231N forums
Coursera - Machine Learning - Andrew Ng
Coursera - Neural Networks - Geoffrey Hinton
Stanford - These tutorials seem cool
Optimization Algorithms in ML - NIPS2010 - Video (2010)
Kadenze: Creative Applications of Deep Learning with Tensorflow - Course seems nice.
Kadenze: The Nature of Code
Udacity - Machine Learning
Udacity - Deep Learning
TensorFlow - "Best Tensorflow+Deep Learning tutorials on YouTube"
MIT - Intro to Deep Learning
University of California, Berkeley - CS118: Artificial Intelligence
MIT 6.S094 - RNN and control topics.
Oxford - Deep Learning NLP
Connections between physics and deep learning - Center for Brains, Minds and Machines (CBMM)

deeplearningweekly.com - List of opensource curriculum. Lots of courses.

Learning without Backpropagation

Data Visualisation Collection

deeplearning.net Datasets
OpenML - Has heaps of datasets.

Dropout Info

HyperNEAT - HyperNEAT is a well known Neuro-Evolution algorithm
Learning Genetic Algorithms - Guy with candy.

How to Install OpenAI's Universe and Make a Game Bot using reinforcement learning.

fast.ai[edit | edit source]

Lesson 3: At <1:18:00 shows how to manipulate and fine tune a model. Says always use batch normalisation (1:40:00). Mentions the BatchNormalization layer that does batchnorm for you. Shows making a model from scratch and Ensembeling them (1:57:30).
Finished Lesson 4 - (Geoffrey_Hinton says max pooling is bad. Talks about capsule architecture) - Use ADAM, look into the Jeremy Howard's modified ADAM. optimizer=Adam() (model.compile), Previously he talked about always using RMSprop...
Lesson 6: Says loss="sparse_categorical_crossentropy" (model.compile) allows you to avoid one hot encoding (1:26).
Lesson 7: 30min in he talks about some kdd best paper competition. Finding bounding boxes at 49min in.

sentdex

7 Steps to Mastering Machine Learning With Python - "From classification, we look at continuous numeric prediction"

Carnegie Mellon University Course

Coursera

Stanford Machine Learning Unofficial Notes

Udacity Deep learning

The 10 Algorithms Machine Learning Engineers Need to Know

Machine learning for algorithmic trading w/ Bert Mouler

neural aesthetic @ schoolofma :: 10 convnet applications

artwithML

Royal Society on Machine Learning

/r/machinelearning

Evolving AI Lab EvolvingAI.org

DeepLearning.TV - Watched as on 2016 Aug 13

~~Tensorflow and deep learning, without a PhD, Martin Gorner, Google~~

TensorFlow[edit | edit source]

Deep Learning With Python & Tensorflow - PyConSG 2016

Intro to ML and TensorFlow Tutorial

TensorFlow Examples

The Ultimate List of TensorFlow Resources: Books, Tutorials, Libraries and More

TensorFlow_Exercises

Tensorflow: How to restore a previously saved model

PyTorch[edit | edit source]

Practical PyTorch: Classifying Names with a Character-Level RNN

Misc[edit | edit source]

Modulus layer? +1.0 -> -1.0 (modulus is apparently expensive...)
Prune out neurons that don't fire for many images as a way of regularization?

Color Representation Ideas[edit | edit source]

See what effect reducing the bits of colour has on accuracy... GreyScale vs R8G8B8 vs R8G4B4, etc...
Dumb & basic - Red*255*255+green*255+blue. Should reduce the number of channels but still have precision bits left over. Maybe it needs to be offset to the centre though to help future layers...
Would it be possible for a Float32 to be used to encode 8 bits of the next color?
AllRGB - Shows images with one of every colour in it...
The way the human eye splits things up? That's like 6 separate images...
More natural mapping...
Maybe try and keep similar colours together distance wise? Hard to do with a single dimension float.
Hilbert Curve
Periodic algorithms? - Sin,Cos,etc..., Repeating patterns, Partial gradient+periodic. Isn't RGB basically just periodic anyway?
Float32 using 4d color space (but then some colours would be duplicated...]
2xFloats merge red&blue?, green&red? (how does the eye do that?)
Separate out brightness?
Try an optimisation function similar to an embedding?
Maybe try and make similar colours further apart to exaggerate the subtle differences?
Negatives could cause the 'near' dimensional to change... Like having an extra dimension. Would duplicate colours again.
What about that Kaggle competition with the 16 band sate-lights?...
Maybe this is all useless. The convolution layer should just learn what it needs anyway... It's all probability based so it shouldn't need to be too close. This could all hurt it.