where:
Remember that is a 4-vetor, gamma matrices are 4x4 matrices, so the whole thing comes down to a dot product of two 4-vectors, with a modified by matrix multiplication/derivatives, and the result is a scalar, as expected for a Lagrangian.
Like any other Lagrangian, you can then recover the Dirac equation, which is the corresponding equations of motion, by applying the Euler-Lagrange equation to the Lagrangian.
There is only space for two languages at most in the world: the compiled one, and the interpreted one.
Those two are languages not by any means perfect from a language design point of view, and there are likely already better alternatives, they are only chosen due to a pragmatic tradeoff between ecosystem and familiarity.
Ciro predicts that Python will become like Fortran in the future: a legacy hated by most who have moved to JavaScript long ago (which is slightly inferior, but too similar, and with too much web dominance to be replaced), but with too much dominance in certain applications like machine learning to be worth replacing, like Fortran dominates certain HPC applications. We'll see. Maybe non performance critical scripting languages are easier to replace.
C++ however is decent, and is evolving in very good directions in the 2010's, and will remain relevant in the foreseeable future.
Bash can also be used when you're lazy. But if the project goes on, you will sooner or later regret that choice.
The language syntax in itself does not matter. All that matters is how many useful libraries and tooling it has.
This is how other languages compare:
- C: but cannot make a large codebase DRY without insanity
- Ruby: the exact same as Python, and only strong in one domain: web development, while Python rules everything else, and is not bad on web either. So just kill Ruby, please.
- JavaScript: it is totally fine if Node.js destroys Python and becomes the ONE scripting language to rule them all since Python and JavaScript are almost equally crappy (although JavaScript is a bit more of course).One thing must be said tough:
someobject.not_defined_propertysilently returningundefinedrather than blowing up is bullshit. - Go: likely a good replacement for Python. If the ecosystem gets there, will gladly use it more.
- Java: good language, but has an ugly enterprisey ecosystem, Oracle has made/kept the development process too closed, and API patenting madness on Android just kills if off completely
- Haskell: many have tried to learn some functional stuff, but too hard. Sounds really cool though.
- Rust: sounds cool, you will gladly replace C and C++ with it if the ecosystem ramps up.
- C: Microsoft is evil
- Tcl, Perl: Python killed them way back and is less insane
- R, GNU Octave and any other "numerical computing language": all of this is a waste of society's time as explained at: Section "Numerical computing language"
- Swift: Ciro would rather stay away from Apple dominated projects if possible since they sell a closed source operating system
A way to write the wavefunction such that the position operator is:i.e., a function that takes the wavefunction as input, and outputs another function:
If you believe that mathematicians took care of continuous spectrum for us and that everything just works, the most concrete and direct thing that this representation tells us is that:equals:
the probability of finding a particle between and at time
These people are heroes. There's nothing else to say.
If you are going to do closed source, at least do it like this.
Basically the opposite of need to know for software.
How the hell are you supposed to develop an open source implementation of something that has a closed standard?
Closed source on offline products used by millions of people is evil, when you could just have those for free with open source software! Thus Ciro's hatred for Microsoft Windows and MacOS (at least userland, maybe).
Ciro Santilli can accept closed source on server products more easily than offline, because the servers have to be paid for somehow (by stealing your private data).
Open source development model in which developers develop in private, and only release code to the public during releases.
Notable example project: Android Open Source Project.
Next, in the and then let's create the then back on the mlperf directory we download our model:and finally run!which gives on P51:where The
imagenette2 directory, first let's create a 224x224 scaled version of the inputs as required by the benchmark at mlcommons.org/en/inference-datacenter-21/:#!/usr/bin/env bash
rm -rf val224x224
mkdir -p val224x224
for syndir in val/*: do
syn="$(dirname $syndir)"
for img in "$syndir"/*; do
convert "$img" -resize 224x224 "val224x224/$syn/$(basename "$img")"
done
doneval_map.txt file to match the format expected by MLPerf:#!/usr/bin/env bash
wget https://gist.githubusercontent.com/aaronpolhamus/964a4411c0906315deb9f4a3723aac57/raw/aa66dd9dbf6b56649fa3fab83659b2acbf3cbfd1/map_clsloc.txt
i=0
rm -f val_map.txt
while IFS="" read -r p || [ -n "$p" ]; do
synset="$(printf '%s\n' "$p" | cut -d ' ' -f1)"
if [ -d "val224x224/$synset" ]; then
for f in "val224x224/$synset/"*; do
echo "$f $i" >> val_map.txt
done
fi
i=$((i + 1))
done < <( sort map_clsloc.txt )wget https://zenodo.org/record/4735647/files/resnet50_v1.onnxDATA_DIR=/mnt/sda3/data/imagenet/imagenette2 time ./run_local.sh onnxruntime resnet50 cpu --accuracyTestScenario.SingleStream qps=164.06, mean=0.0267, time=23.924, acc=87.134%, queries=3925, tiles=50.0:0.0264,80.0:0.0275,90.0:0.0287,95.0:0.0306,99.0:0.0401,99.9:0.0464qps presumably means "querries per second". And the time results:446.78user 33.97system 2:47.51elapsed 286%CPU (0avgtext+0avgdata 964728maxresident)ktime=23.924 is much smaller than the time executable because of some lengthy pre-loading (TODO not sure what that means) that gets done every time:INFO:imagenet:loaded 3925 images, cache=0, took=52.6sec
INFO:main:starting TestScenario.SingleStreamLet's try on the GPU now:which gives:TODO lower
DATA_DIR=/mnt/sda3/data/imagenet/imagenette2 time ./run_local.sh onnxruntime resnet50 gpu --accuracyTestScenario.SingleStream qps=130.91, mean=0.0287, time=29.983, acc=90.395%, queries=3925, tiles=50.0:0.0265,80.0:0.0285,90.0:0.0405,95.0:0.0425,99.0:0.0490,99.9:0.0512
455.00user 4.96system 1:59.43elapsed 385%CPU (0avgtext+0avgdata 975080maxresident)kqps on GPU! Pinned article: Introduction to the OurBigBook Project
Welcome to the OurBigBook Project! Our goal is to create the perfect publishing platform for STEM subjects, and get university-level students to write the best free STEM tutorials ever.
Everyone is welcome to create an account and play with the site: ourbigbook.com/go/register. We belive that students themselves can write amazing tutorials, but teachers are welcome too. You can write about anything you want, it doesn't have to be STEM or even educational. Silly test content is very welcome and you won't be penalized in any way. Just keep it legal!
Intro to OurBigBook
. Source. We have two killer features:
- topics: topics group articles by different users with the same title, e.g. here is the topic for the "Fundamental Theorem of Calculus" ourbigbook.com/go/topic/fundamental-theorem-of-calculusArticles of different users are sorted by upvote within each article page. This feature is a bit like:
- a Wikipedia where each user can have their own version of each article
- a Q&A website like Stack Overflow, where multiple people can give their views on a given topic, and the best ones are sorted by upvote. Except you don't need to wait for someone to ask first, and any topic goes, no matter how narrow or broad
This feature makes it possible for readers to find better explanations of any topic created by other writers. And it allows writers to create an explanation in a place that readers might actually find it.
Figure 1. Screenshot of the "Derivative" topic page. View it live at: ourbigbook.com/go/topic/derivativeVideo 2. OurBigBook Web topics demo. Source. - local editing: you can store all your personal knowledge base content locally in a plaintext markup format that can be edited locally and published either:This way you can be sure that even if OurBigBook.com were to go down one day (which we have no plans to do as it is quite cheap to host!), your content will still be perfectly readable as a static site.
- to OurBigBook.com to get awesome multi-user features like topics and likes
- as HTML files to a static website, which you can host yourself for free on many external providers like GitHub Pages, and remain in full control
Figure 3. Visual Studio Code extension installation.
Figure 4. Visual Studio Code extension tree navigation.
Figure 5. Web editor. You can also edit articles on the Web editor without installing anything locally.Video 3. Edit locally and publish demo. Source. This shows editing OurBigBook Markup and publishing it using the Visual Studio Code extension.Video 4. OurBigBook Visual Studio Code extension editing and navigation demo. Source. 
- Infinitely deep tables of contents:
All our software is open source and hosted at: github.com/ourbigbook/ourbigbook
Further documentation can be found at: docs.ourbigbook.com
Feel free to reach our to us for any help or suggestions: docs.ourbigbook.com/#contact