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This is what a nuclear disaster area looks like.
Check out Audible: http://bit.ly/AudibleVe
Broadcast locations and times:

North America: PBS, July 28 & 29 @ 10pm ET / 9pm Central
Europe: ZDF/arte, July 31 @ 10pm
Australia: SBS, August 6, 13, 20 @ 8:30pm EST

Not broadcast in your country? Contact your local broadcaster and/or email www.genepoolproductions.com

Music by Kevin Macleod http://incompetech.com 'Come Play With Me' & 'Lost Frontier'

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Explanation and implementation of interval scheduling problem using a greedy algorithm.
CODE REPOSITORY: https://github.com/AladdinPerz....on/Algorithms-Collec

GPT-J-6B - Just like GPT-3 but you can actually download the weights and run it at home. No API sign-up required, unlike some other models we could mention, right? Any just like GPT-3, this has 3 super easy example ways you can run this -v ia the EleutherAI website, Google Colab TPU or single GPU on a local system.

Buy art & support a nerd!
https://t.co/kAUP0Vfq48?amp=1

Github: https://github.com/kingoflolz/mesh-transformer-jax


Requirements for this guide to running locally:
* Ubuntu 20.04
* 32 GB RAM and an Nvidia GPU with at least 24 GB VRAM
* Anaconda - https://www.anaconda.com/products/individual

== Python virtual environment (Anaconda) ==
conda create --name mesh-transformer-jax python=3.8
conda activate mesh-transformer-jax

== Download & install ==
git clone https://github.com/kingoflolz/mesh-transformer-jax.git
cd mesh-transformer-jax

sudo apt install zstd
wget https://the-eye.eu/public/AI/G....PT-J-6B/step_383500_
tar -I zstd -xf step_383500_slim.tar.zstd

pip install torch==1.8.1+cu111 torchvision==0.9.1+cu111 torchaudio==0.8.1 -f https://download.pytorch.org/whl/torch_stable.html
pip install jaxlib==0.1.67+cuda111 -f https://storage.googleapis.com..../jax-releases/jax_re
pip install -r requirements.txt # (Edited to use tensorflow-gpu 2.5.0 for no reason)
pip install jax==0.2.12

Thanks to ML6 for virtually hosting us tonight! For those who would like to attend live, have a look at https://www.meetup.com/ai-camp....us-berlin/events/282

Talk 1

Title: How we trained our own Dutch GPT-2 using Transformers

Speaker: Thomas Vrancken

Abstract:
Text Generation and the GPT series of Transformer models have been a hot topic since the public got to know the astounding power of it. The latest GPT-3 can mimic a human conversation to a close to scary level.
At ML6 we trained and open sourced our own Dutch GPT-2 model using Huggingface’s Transformers library. This talk addresses the questions:
How do you do that? What kind of data do you need and how to access enough compute power to actually train the model?

Bio:
Thomas Vrancken is an ML Engineer at ML6 with a background in strategy consulting and research. Thomas is passionate about NLP, data science and making real world impacts with creative Machine Learning applications. Staying versatile is his credo, joining a maximum of events with interesting talks is a means of achieving it.

Talk 2

Title: Efficient Transformers

Speaker: Mats Uytterhoeven

Abstract:
In recent years we’ve seen an exponential increase in the size of pre-trained transformer based models and although they push the state-of-the-art to ever greater heights, they also become increasingly cumbersome to work with. This has prompted researchers around the world to try and find more efficient alternatives to the classic transformer architecture and has spawned an interesting new research direction. In this talk, we will have a look at some of the interesting ideas in this area and what the future may hold for these transformer based models.

Bio:
Mats Uytterhoeven is an ML Engineer at ML6 interested in a broad range of topics. His main focus is on NLP and unsupervised learning problems. When he's not hacking on machine learning code, he likes playing tennis, reading (non-fiction), and traveling. He believes machine learning can have a positive impact on people's lives and loves working on projects that can make a difference.