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4 Dec, 5 tweets, 3 min read
πŸ† πŸ₯³πŸ•ΊπŸ½ We're thrilled to announce the first winners of #27DaysOfJAX

πŸ… @rareblog, for running JAX on a Jetson Nano & writing about Spiking NNs in JAX

πŸ… @imPGulati, for writing his first blog ever & contributing

πŸ… @dionhaefner, for 🀯 🀯 🀯 the great read!

Blogs in theπŸ§΅πŸ‘‡
#27DaysOfJAX has only started πŸ”₯

It's not too late to join our JAX course, hosted by @bhutanisanyam1 & @cgarciae88!

πŸ“Œ wandb.me/JAX-registrati…

πŸ“’ Tag Sanyam, Cristian or us to submit your work.
We'll keep an eye πŸ‘€ out for your contributions πŸ“

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More from @weights_biases

Weights & Biases Profile picture
10 Sep
We're grateful that deeply understanding and improving datasets is acknowledged as a non-negotiable part of training pipelines πŸ™

However exploring datasets can be cumbersome, and documenting and sharing findings is often messy

W&B Tables fixes this πŸ‘‡

W&B Tables enables quick and powerful exploration of image, video, audio, tabular, molecule and NLP datasets.

@metaphdor used Tables to explore 100k rows from @Reddit's Go Emotions dataset:

πŸ“Ί: )

πŸ›‘ First, filtering for multiple column values
πŸ”Ž Exploring the distribution of reddit comments by sub-reddit name:
Read 11 tweets
Weights & Biases Profile picture
9 Sep
New podcast episode! πŸ“’

@l2k and @emilymbender dive into the problems with bigger and bigger language models, the difference between form and meaning, the limits of benchmarks, and the #BenderRule.

πŸŽ₯:

They discuss 4 of Emily's papers ⬇️

1/5
"On the Dangers of Stochastic Parrots: Can Language Models Be Too Big? 🦜" (Bender, Gebru et al. 2021)

Possible risks associated with bigger and bigger language models, and ways to mitigate those risks.

dl.acm.org/doi/pdf/10.114…

2/5
"Climbing towards NLU: On Meaning, Form, and Understanding in the Age of Data" (Bender & Koller, 2020)

Why systems trained only on form (like language models) have no a priori way to learn meaning.

aclanthology.org/2020.acl-main.…

3/5
Read 5 tweets
Weights & Biases Profile picture
5 Nov 20
We're thrilled to announce that YOLOv5 now comes with @weights_biases baked in!

With no additional lines of code, you now get automatic bounding box debugging, GPU usage and performance metrics, reproducible models, & more!

πŸ‘©β€πŸš€ Try it β†’ colab.research.google.com/github/ultraly…

#deeplearning
What does YOLOv5 + W&B give you?

1. You can monitor how your models and hyperparameters are performing, including automatically tracking:

- Training and validation losses
- Precision, Recall, mAP@0.5, mAP@0.5:0.95
- Learning Rate over time
2. Automatically tracked system metrics like GPU Type,Β GPU Utilization, power, temperature,Β CUDA memory usage; and system metrics like Disk I/0, CPU utilization, RAM memory usage.
Read 7 tweets

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