ai
quickai
| ai | quickai | |
|---|---|---|
| 6 | 7 | |
| 19 | 162 | |
| - | - | |
| 3.5 | 3.7 | |
| about 1 month ago | about 2 months ago | |
| Python | Python | |
| MIT License | MIT License |
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ai
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Made the YouTube Series Implementing ML Models Using NumPy
GitHub (for model impls and other series): https://github.com/oniani/ai
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[D] What advanced models would you like to see implemented from scratch?
All of the videos are and will be available on my YouTube channel. Implementations are and will be in this GitHub repo.
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[N] I Have Released the YouTube Series Discussing and Implementing Activation Functions
GitHub: https://github.com/oniani/ai
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Implementing Logistic Regression from Scratch
Link to the YouTube video: https://www.youtube.com/watch?v=YDa3rX9yLCE Link to the repo containing the code: https://github.com/oniani/ai
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[N] AI/ML Model API Design, Numerical Stability, and More Models from Scratch! (stylepoint)
Repository for the AI/ML series - oniani/ai.
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Implementing Machine Learning Models From Scratch (stylepoint)
Thanks! One thing to note about that implementation is that we could have passed features and labels directly to the fit method. This would avoid unnecessary data copying (i.e., storing data inside the LinearRegression class). I have already updated the GitHub codebase.
quickai
- Show HN: QuickAI Version 2 Released
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QuickAI version 2 released!
I originally released QuickAI here. I am very excited to announce version 2 of QuickAI
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QuickAI is a Python library that makes it extremely easy to experiment with state-of-the-art Machine Learning models.
GitHub: https://github.com/geekjr/quickai
- Show HN: Quickai – Quickly experiment with state-of-the-art ML models
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quickai - A Python library that makes it extremely easy to experiment with state-of-the-art Machine Learning models.
Yeah, totally agree. https://github.com/geekjr/quickai/blob/main/quickai/image_classification.py does really need some reworking. Dicts are the way to go. But once that's done, I think it could actually be a practical lib!
What are some alternatives?
detoxify - Trained models & code to predict toxic comments on all 3 Jigsaw Toxic Comment Challenges. Built using ⚡ Pytorch Lightning and 🤗 Transformers. For access to our API, please email us at [email protected].
gpt-neo_dungeon - Colab notebooks to run a basic AI Dungeon clone using gpt-neo-2.7B
segyio - Fast Python library for SEGY files.
YOLOv6 - YOLOv6: a single-stage object detection framework dedicated to industrial applications.
chappie.ai - Generalized AI to perform a multitude of tasks written in python3
Note - Easily implement parallel training and distributed training. Machine learning library. Note.neuralnetwork.tf package include Llama2, Llama3, Gemma, CLIP, ViT, ConvNeXt, BEiT, Swin Transformer, Segformer, etc, these models built with Note are compatible with TensorFlow and can be trained with TensorFlow.
happy-transformer - Happy Transformer makes it easy to fine-tune and perform inference with NLP Transformer models.
TabFormer - Code & Data for "Tabular Transformers for Modeling Multivariate Time Series" (ICASSP, 2021)
TensorLayer - Deep Learning and Reinforcement Learning Library for Scientists and Engineers
rtdl - Research on Tabular Deep Learning [Moved to: https://github.com/yandex-research/rtdl]
transfer-learning-conv-ai - 🦄 State-of-the-Art Conversational AI with Transfer Learning
falcongpt - Simple GPT app that uses the falcon-7b-instruct model with a Flask front-end.