stable-diffusion-dreambooth-colab
fastai
| stable-diffusion-dreambooth-colab | fastai | |
|---|---|---|
| 1 | 9 | |
| 31 | 25,691 | |
| - | 0.8% | |
| 3.6 | 8.0 | |
| 5 months ago | 24 days ago | |
| Jupyter Notebook | Jupyter Notebook | |
| The Unlicense | Apache License 2.0 |
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stable-diffusion-dreambooth-colab
fastai
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Cleared AWS Machine Learning - Specialty exam.. Happy to help!!!
Jeremy Howard's YouTube Channel - Jeremy maintains the fastai library, which is an excellent package that will help anyone build complicated ML architectures in minimum time. His YouTube Channel has a number of free courses which do an amazing job of covering a variety of ML topics, and he also maintains a very active forum for people studying ML.
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Coding your own AI in 2023 with fastai
To create the AI we will use fastai. This is a python library, which is build on top of pytorch. No worries, you don't need to know how to code python. We will learn how this stuff works along the way :)
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Fast.ai starts a corporate partnership program
You may know fast.ai as a popular deep learning course. There is also a deep learning library with the same name (https://github.com/fastai/fastai) as well as software development tools like nbdev (https://nbdev.fast.ai/).
fast.ai has been offering education and tools for free for over 7 years, and has been approached by many companies asking for help. This program offers an avenue for business to get relevant professional services and support.
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People tricking ChatGPT “like watching an Asimov novel come to life”
The "fastai" course is free, and does a really nice job walking you through building simple neural nets from the ground up:
https://github.com/fastai/fastai
What's going on here is the exact same thing, just much, much larger.
- Programação letrada com Jupyter Notebook e Nbdev
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Why noone uses nbdev for library development?
Development NB: https://github.com/fastai/fastai/blob/master/nbs/09_vision.augment.ipynb
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[D] What Repetitive Tasks Related to Machine Learning do You Hate Doing?
There is already a ton of momentum around automating ML workflows. I would suggest you contribute to a preexisting project like, for instance, PyTorch Lightning or fast.ai.
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Good practices for neural network training: identify, save, and document best models
If you are unaware of what fastai is, its official description is:
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D I Refuse To Use Pytorch Because Its A Facebook
Also, not a single docstring to document any code in the library - https://github.com/fastai/fastai/blob/master/fastai/vision/learner.py
What are some alternatives?
Real-ESRGAN-Video-Batch-Process - Upscale any number of videos using this colab notebook!
pytorch-lightning - Build high-performance AI models with PyTorch Lightning (organized PyTorch). Deploy models with Lightning Apps (organized Python to build end-to-end ML systems). [Moved to: https://github.com/Lightning-AI/lightning]
pyprobml - Python code for "Probabilistic Machine learning" book by Kevin Murphy
fastbook - The fastai book, published as Jupyter Notebooks
silero-models - Silero Models: pre-trained speech-to-text, text-to-speech and text-enhancement models made embarrassingly simple
Watermark-Removal-Pytorch - 🔥 CNN for Watermark Removal using Deep Image Prior with Pytorch 🔥.
stable-diffusion-webui-colab - stable diffusion webui colab
PySyft - Perform data science on data that remains in someone else's server
lego-mindstorms - My LEGO MINDSTORMS projects (using set 51515 electronics)
ru-dalle - Generate images from texts. In Russian
pytorch-lightning - Pretrain, finetune and deploy AI models on multiple GPUs, TPUs with zero code changes.
iterative-grabcut - This algorithm uses a rectangle made by the user to identify the foreground item. Then, the user can edit to add or remove objects to the foreground. Then, it removes the background and makes it transparent.