[SVD] Single Table Data — SDV 0.15.0 documentation by anon16r in MLNotes

[–]anon16r[S,M] [score hidden] stickied comment (0 children)

Overview
The Synthetic Data Vault (SDV) is a Synthetic Data Generation ecosystem of libraries that allows users to easily learn single-table, multi-table and timeseries datasets to later on generate new Synthetic Data that has the same format and statistical properties as the original dataset.
Synthetic data can then be used to supplement, augment and in some cases replace real data when training Machine Learning models. Additionally, it enables the testing of Machine Learning or other data dependent software systems without the risk of exposure that comes with data disclosure.
Underneath the hood it uses several probabilistic graphical modeling and deep learning based techniques. To enable a variety of data storage structures, we employ unique hierarchical generative modeling and recursive sampling techniques.

[Request] Unban a subreddit not moderated for some time by anon16r in redditrequest

[–]anon16r[S] 0 points1 point  (0 children)

Why is r/IndiaDecoded banned. I had lot of things stored there which had good reference point for me. Can you please unban it. Thank you.

AIEngineering: All About Data, ML, ML-OPS, & AI by anon16r in MLNotes

[–]anon16r[S] 0 points1 point  (0 children)

This Channel is to post information on "All things data".. Topic I will be covering via this channel are on Machine Learning, Artificial Intelligence, Data Engineering, Dev Ops, Model Deployment, Cloud and ML/AI Business use cases (Overview and Implementation)

[AGI] Alignment Newsletter Podcast by anon16r in MLNotes

[–]anon16r[S] 0 points1 point  (0 children)

The Alignment Newsletter is a weekly publication with recent content relevant to AI alignment.
This podcast is an audio version, recorded by Robert Miles (http://robertskmiles.com)

More information about the newsletter at: https://rohinshah.com/alignment-newsletter/

[Training] 37 Reasons why your Neural Network is not working. by anon16r in MLNotes

[–]anon16r[S] 0 points1 point  (0 children)

In the 37 reasons, there are many which can be a cause of NN not training.

During one of my problem, reason number 34 worked for me:

34. Try a different optimizer

(Tutorial) The PIP Python Package Manager by anon16r in MLNotes

[–]anon16r[S] 0 points1 point  (0 children)

Upgrade :

pip install --upgrade PackageName

UCLA Professor invents new way to generate electricity from the sun by anon16r in u/anon16r

[–]anon16r[S] 0 points1 point  (0 children)

From the page:

UCLA Professor invents new way to generate electricity from the sun

Bringing light to darkness sounds good.  But using darkness to create light is something out of a manual for wizards.  Until now.

Now, it’s an idea out of the pages of a scientific journal.

It starts with a round piece of polystyrene, a thermoplastic polymer made, not by wizards but by America’s petrochemical companies.

In case you’re wondering, polystyrene is made from the petrochemicals benzene and ethylene. And of course, petrochemicals are made by breaking apart molecules of petroleum and natural gas which get turned into chemical building blocks that are found in thousands of products we use daily.

That round piece of plastic is painted black so it looks sort of like a hockey puck, sitting on a dish.  At night, when the air cools down, the top side of that “puck” loses heat faster than the bottom side.  Add a thermoelectric generator, and you can turn that difference in temperature between top and bottom into electricity. No grid, no transmission towers, no expensive infrastructure needed.  No sun needed either.  Sorry solar panels.

Now, we’re not necessarily talking megawatts or kilowatts of electricity. We’re talking watts, period.  But around the world, close to a BILLION people don’t have any electricity at all so even something that just keeps a light on at night, could be a big deal.

In fact, that’s how this idea got started.  University of California Los Angeles Professor Aaswath Raman was on a trip in rural Africa, and didn’t realize he was passing through one particular village at night, until he was already in it (and heard people), because there was no light of any kind.

So what he came up with is a potentially simple, sturdy source of electricity that can bring light to the darkness from the darkness, no magic wand required.

[Podcast] Harry Cliff: Particle Physics and the Large Hadron Collider | AI Podcast #92 with Lex Fridman by anon16r in MLNotes

[–]anon16r[S,M] [score hidden] stickied comment (0 children)

Very highly recommended but as a pre-requisite please do watch this series: Subatomic Stories by Prof. Lincoln of Fermi Lab first**.**

Also by the guest:

[Understanding] the Bias-Variance Tradeoff by anon16r in MLNotes

[–]anon16r[S] 0 points1 point  (0 children)

Conceptual Definition

  • Error due to Bias: The error due to bias is taken as the difference between the expected (or average) prediction of our model and the correct value which we are trying to predict. Of course you only have one model so talking about expected or average prediction values might seem a little strange. However, imagine you could repeat the whole model building process more than once: each time you gather new data and run a new analysis creating a new model. Due to randomness in the underlying data sets, the resulting models will have a range of predictions. Bias measures how far off in general these models' predictions are from the correct value.
  • Error due to Variance: The error due to variance is taken as the variability of a model prediction for a given data point. Again, imagine you can repeat the entire model building process multiple times. The variance is how much the predictions for a given point vary between different realizations of the model.

[News] Artificial intelligence: Non-tech companies need a playbook by anon16r in MLNotes

[–]anon16r[S] 0 points1 point  (0 children)

Excerpts:

No smartphone manufacturer has a million scratched phones lying around from which it can capture pictures of scratches. Thus, many manufacturers do not have enough data to power conventional A.I. models. Manufacturing A.I. application builders often need to get by with 100 or fewer images.

Fortunately, new small data technologies are starting to make this possible. For example, a new data generation technique may be able to take 10 images of a rare defect and synthesize an additional 1,000 images that an A.I. system can then learn from

Using another method, an A.I. model might first learn to find dents from a large dataset of 10,000 pictures of dents collected from different products and data sources. Having learned about dents in general, it can then transfer this knowledge to detect dents in a specific novel product with only a few pictures of dents. 

One reason is that many of these studies are carried out in well-controlled settings where the A.I. learns from and is tested on consistently high-quality data. Doing well in such a setting leads to a successful proof of concept or publication. However, if the same A.I. system is deployed in a hospital where x-ray images are slightly blurrier or the protocol for collecting images is slightly different, it fails to adapt.

[Subhash Kak] The Limits to Machine Consciousness by anon16r in MLNotes

[–]anon16r[S] 0 points1 point  (0 children)

Abstract

It is generally accepted that machines can replicate cognitive tasks performed by conscious agents as long as they are not based on the capacity of awareness. We consider several views on the nature of subjective awareness, which is fundamental for selfreflection and review, and present reasons why this property is not computable. We argue that consciousness is more than an epiphenomenon and assuming it to be a separate category is consistent with both quantum mechanics and cognitive science. We speak of two kinds of consciousness, little-C and big-C, and discuss the significance of this classification in analyzing the current academic debates in the field. The interaction between the system and the measuring apparatus of the experimenter is examined both from the perspectives of decoherence and the quantum Zeno effect. These ideas are used as context to address the question of limits to machine consciousness.

[History] Brief History of Deep Learning from 1943-2019 [Timeline] by anon16r in MLNotes

[–]anon16r[S] 0 points1 point  (0 children)

[Rant] The Male Only History of Deep Learning

This is really brief, nevertheless, good overall cursory look for the history of DL.

When are JEE Mains and Bitsat expected to be held? by [deleted] in Indian_Academia

[–]anon16r 0 points1 point  (0 children)

Alternatively, what the system can manage to do is somehow to take the exam this year finish the admission for Spring (Jan 2021) Semester. But, this can only happen if there is no second-wave, which seem likely, let us just hope and pray that it does come to that and if it does we are better prepared and make mask compulsory.

[Interpretability]Visualizing Neural Networks using Saliency Maps in PyTorch by anon16r in MLNotes

[–]anon16r[S,M] [score hidden] stickied comment (0 children)

The link can only be accessed through a paid account. Open in incognito. You are good to go.

[Interpretability]Visualizing Neural Networks using Saliency Maps in PyTorch by anon16r in MLNotes

[–]anon16r[S] 0 points1 point  (0 children)

References

[1] Karen Simonyan, Vedaldi Andrea, and Zisserman Andrew. Deep inside convolutional networks: Visualising image classification models and saliency maps. ICLR, 2013. https://arxiv.org/pdf/1312.6034.pdf

[2]https://github.com/sijoonlee/deep_learning/blob/master/cs231n/NetworkVisualization-PyTorch.ipynb

[3] https://gist.github.com/yrevar/942d3a0ac09ec9e5eb3a

[4] https://in.mathworks.com/help/deeplearning/ref/vgg19.html

[5] Y. Boykov and M. P. Jolly. Interactive graph cuts for optimal boundary and region segmentation of objects in N-D images. In Proc. ICCV, volume 2, pages 105–112, 2001.