An imagenet10 subset by fast.ai.
Size of full sized image version: 1.5 GB.
ImageNet Large Scale Visual Recognition Challenge dataset by 
Ciro Santilli 35 Updated 2024-12-23 +Created 1970-01-01
Ciro Santilli 35 Updated 2024-12-23 +Created 1970-01-01Subset of ImageNet. About 167.62 GB in size according to www.kaggle.com/competitions/imagenet-object-localization-challenge/data.
Contains 1,281,167 images and exactly 1k categories which is why this dataset is also known as ImageNet1k: datascience.stackexchange.com/questions/47458/what-is-the-difference-between-imagenet-and-imagenet1k-how-to-download-it
www.kaggle.com/competitions/imagenet-object-localization-challenge/overview clarifies a bit further how the categories are inter-related according to WordNet relationships:
The 1000 object categories contain both internal nodes and leaf nodes of ImageNet, but do not overlap with each other.
image-net.org/challenges/LSVRC/2012/browse-synsets.php lists all 1k labels with their WordNet IDs.There is a bug on that page however towards the middle:and there is one missing label if we ignore that dummy
n02119789: kit fox, Vulpes macrotis
n02100735: English setter
n02096294: Australian terriern03255030: dumbbell
href="ht:
n02102040: English springer, English springer spanielhref= line. A thinkg of beauty!Also the lines are not sorted by synset, if we do then the first three lines are:
n01440764: tench, Tinca tinca
n01443537: goldfish, Carassius auratus
n01484850: great white shark, white shark, man-eater, man-eating shark, Carcharodon carchariasgist.github.com/aaronpolhamus/964a4411c0906315deb9f4a3723aac57 has lines of type:therefore numbered on the exact same order as image-net.org/challenges/LSVRC/2012/browse-synsets.php
n02119789 1 kit_fox
n02100735 2 English_setter
n02110185 3 Siberian_huskygist.github.com/yrevar/942d3a0ac09ec9e5eb3a lists all 1k labels as a plaintext file with their benchmark IDs.therefore numbered on sorted order of image-net.org/challenges/LSVRC/2012/browse-synsets.php
{0: 'tench, Tinca tinca',
1: 'goldfish, Carassius auratus',
2: 'great white shark, white shark, man-eater, man-eating shark, Carcharodon carcharias',The official line numbering in-benchmark-data can be seen at
LOC_synset_mapping.txt, e.g. www.kaggle.com/competitions/imagenet-object-localization-challenge/data?select=LOC_synset_mapping.txtn01440764 tench, Tinca tinca
n01443537 goldfish, Carassius auratus
n01484850 great white shark, white shark, man-eater, man-eating shark, Carcharodon carchariashuggingface.co/datasets/imagenet-1k also has some useful metrics on the split:
- train: 1,281,167 images, 145.7 GB zipped
- validation: 50,000 images, 6.67 GB zipped
- test: 100,000 images, 13.5 GB zipped
How can I be as great by Justine Musk by Ciro Santilli 35 Updated 2024-12-23 +Created 1970-01-01
Ciro Santilli 35 Updated 2024-12-23 +Created 1970-01-01www.quora.com/How-can-I-be-as-great-as-Bill-Gates-Steve-Jobs-Elon-Musk-or-Sir-Richard-Branson/answer/Justine-Musk is a fantastic ansewr by Justine Musk, Elon Musk's ex-fife, to the question:One of her key thesis is Many successful people are neurodiverse:
How can I be as great as Bill Gates, Steve Jobs, Elon Musk or Sir Richard Branson?
These people tend to be freaks and misfits who were forced to experience the world in an unusually challenging way. They developed strategies to survive, and as they grow older they find ways to apply these strategies to other things, and create for themselves a distinct and powerful advantage. They don't think the way other people think. They see things from angles that unlock new ideas and insights. Other people consider them to be somewhat insane.
- github.com/deepmind/meltingpot TODO vs DeepMind Lab2D? Also 2D discrete. Started in 2021.
- github.com/deepmind/ai-safety-gridworlds mentioned e.g. at www.youtube.com/watch?v=CGTkoUidQ8I by Rober Miles
Creating Multimodal Interactive Agents from DeepMind by Two Minute Papers (2023)
Source. www.deepmind.com/blog/building-interactive-agents-in-video-game-worldsOpen-Ended Learning Leads to Generally Capable Agents by DeepMind (2021)
Short name: XLand. Whitepaper: www.deepmind.com/blog/generally-capable-agents-emerge-from-open-ended-play.The author Ole Tange answers every question about it on Stack Exchange. What a legend!
This program makes you respect GNU make a bit more. Good old make with
-j can not only parallelize, but also take in account a dependency graph.Some examples under:
man parallel_exampesTo get the input argument explicitly job number use the magic string sample output:
{}, e.g.:printf 'a\nb\nc\n' | parallel echo '{}'a
b
cTo get the job number use sample output:
{#} as in:printf 'a\nb\nc\n' | parallel echo '{} {#}'a 1
b 2
c 3
c 3{%} contains which thread the job running in, e.g. if we limit it to 2 threads with -j2:printf 'a\nb\nc\nd\n' | parallel -j2 echo '{} {#} {%}'a 1 1
b 2 1
c 3 2
d 4 1% symbol in many programming languages such as C.To pass multiple CLI argments per command you can use sample output:
-X e.g.:printf 'a\nb\nc\nd\n' | parallel -j2 -X echo '{} {#} {%}'a b 1 1
c d 2 2Way too few people know about this. Spread the word.
Shor's algorithm Explained by minutephysics (2019)
Source. Example: llvm/hello.ll adapted from: llvm.org/docs/LangRef.html#module-structure but without double newline.
To execute it as mentioned at github.com/dfellis/llvm-hello-world we can either use their crazy assembly interpreter, tested on Ubuntu 22.10:This seems to use
sudo apt install llvm-runtime
lli hello.llputs from the C standard library.Or we can Lower it to assembly of the local machine:which produces:and then we can assemble link and run with gcc:or with clang:
sudo apt install llvm
llc hello.llhello.sgcc -o hello.out hello.s -no-pie
./hello.outclang -o hello.out hello.s -no-pie
./hello.outhello.s uses the GNU GAS format, which clang is highly compatible with, so both should work in general.Even added UI app support as of 2022: learn.microsoft.com/en-us/windows/wsl/tutorials/gui-apps That's awesome!
Source code: github.com/neuronsimulator/nrn
www.frontiersin.org/articles/10.3389/fninf.2019.00063/fullCoreNEURON: An Optimized Compute Engine for the NEURON Simulator (2019) Merged back into mainstream: github.com/BlueBrain/CoreNeuron
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