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Weights & Biases Profile picture
Weights & Biases
@weights_biases
The developer-first MLOps platform, built for practitioners by practitioners.
Feb 10, 2022 • 5 tweets • 4 min read
💬 The world of GPT-3

In this episode of Gradient Dissent, @npew, @borisdayma, and @l2k talk about:

✏️ Applying GPT-3 to translation and copywriting
💪 The performance benefits of fine-tuning GPT-3
💻 Developing the @OpenAI API

(Bonus topic: The story behind the @OpenAI and @weights_biases collaboration 🪄🐝 )

Dec 4, 2021 • 5 tweets • 3 min read
🏆 🥳🕺🏽 We're thrilled to announce the first winners of #27DaysOfJAX

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

🏅 @imPGulati, for writing his first blog ever & contributing

🏅 @dionhaefner, for 🤯 🤯 🤯 the great read!

Blogs in the🧵👇
Sep 10, 2021 • 11 tweets • 5 min read
We're grateful that deeply understanding and improving datasets is acknowledged as a non-negotiable part of training pipelines 🙏

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

W&B Tables fixes this 👇

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:

📺: )

🛑 First, filtering for multiple column values
Sep 9, 2021 • 5 tweets • 3 min read
New podcast episode! 📢

@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.

🎥:

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
Nov 5, 2020 • 7 tweets • 4 min read
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!

👩‍🚀 Try it → 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