book
labml
| book | labml | |
|---|---|---|
| 2 | 23 | |
| 183 | 1,874 | |
| 0.0% | 2.5% | |
| 2.7 | 9.7 | |
| 12 months ago | 6 days ago | |
| Jupyter Notebook | Jupyter Notebook | |
| - | MIT License |
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
book
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[P] Using Sparsity & Clustering to compress your models: Efficient Deep Learning Book
We now have a new chapter focusing on sparsity and clustering, two advanced compression techniques that you can use to reduce the footprint of your model (size, latency, etc.) while retaining your model accuracy. You can read the chapter here, and go through the accompanying codelabs here.
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[P] Efficient Deep Learning Book
The goal is to introduce these ideas in a single place, without having to parse many papers, try to get a working code sample, and then spend time debugging. With the accompanying codelabs, we hope that our readers can make their models 4-20x smaller, faster, and better in quality.
labml
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Creating stickers using SD with img2img
Used the PromptArt app by labml.ai to generate a sticker of an image I took from my iPhone. The results are amazing.
- [D] Why doesn’t your team use an experiment tracking tool?
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Probe PyTorch models
💻 Github
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[P] Probe PyTorch models
🧑🏫 Demo that extracts attention maps of BERT
- Show HN: Probe PyTorch Models
- [D] How do you guys tune hyperparameters, when a single training run takes a long time (days to weeks)?
- Machine Learning Best Practices
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[D] Machine Learning Best Practices
from github
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[P] Annotated deep learning paper implementations
labmlai/labml is a set of tools (tracking experiments, configurations, a bunch of helpers) we coded to ease our ML work (which later improved and open sourced). So we use it in all our projects because it makes things easier for us.
- React's UI State Model vs. Vanilla JavaScript
What are some alternatives?
DeepLearningExamples - State-of-the-Art Deep Learning scripts organized by models - easy to train and deploy with reproducible accuracy and performance on enterprise-grade infrastructure.
nn - 🧑🏫 60 Implementations/tutorials of deep learning papers with side-by-side notes 📝; including transformers (original, xl, switch, feedback, vit, ...), optimizers (adam, adabelief, sophia, ...), gans(cyclegan, stylegan2, ...), 🎮 reinforcement learning (ppo, dqn), capsnet, distillation, ... 🧠
rikai - Parquet-based ML data format optimized for working with unstructured data
guildai - Experiment tracking, ML developer tools
Practical_RL - A course in reinforcement learning in the wild
Deep-Learning-Push-Up-Counter - Deep Learning approach to count the number of repetitions in a video of push ups or pull ups.
tensorflow-onnx - Convert TensorFlow, Keras, Tensorflow.js and Tflite models to ONNX
MIRNet-TFJS - TensorFlow JS models for MIRNet for low-light💡 image enhancement
Lottery_Ticket_Hypothesis-TensorFlow_2 - Implementing "The Lottery Ticket Hypothesis" paper by "Jonathan Frankle, Michael Carbin"
spock - spock is a framework that helps manage complex parameter configurations during research and development of Python applications
tensorflow-deep-learning - All course materials for the Zero to Mastery Deep Learning with TensorFlow course.
YPDL-Build-a-movie-recommendation-engine-with-TensorFlow - In this tutorial, we are going to build a Restricted Boltzmann Machine using TensorFlow that will give us recommendations based on movies that have been watched already. The datasets we are going to use are acquired from GroupLens and contains movies, users, and movie ratings by these users.