TensorLayer
quickai
| TensorLayer | quickai | |
|---|---|---|
| 1 | 7 | |
| 7,275 | 162 | |
| 0.0% | - | |
| 0.0 | 3.7 | |
| about 1 year ago | about 1 month ago | |
| Python | Python | |
| GNU General Public License v3.0 or later | MIT License |
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TensorLayer
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Introduction to ‘TensorLayer’: A Python-based Versatile Deep Learning Library Designed for Machine Learning Researchers
Github: https://github.com/tensorlayer/TensorLayer
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?
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chainerrl - ChainerRL is a deep reinforcement learning library built on top of Chainer.
gpt-neo_dungeon - Colab notebooks to run a basic AI Dungeon clone using gpt-neo-2.7B
gpt-j-chatbot - A GPT-J Chatbot Template for creating AI Characters (Virtual Girlfriend Chatbot, Replika-esque)
segyio - Fast Python library for SEGY files.
yagooglesearch - Yet another googlesearch - A Python library for executing intelligent, realistic-looking, and tunable Google searches.
YOLOv6 - YOLOv6: a single-stage object detection framework dedicated to industrial applications.
SRGAN - Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network
chappie.ai - Generalized AI to perform a multitude of tasks written in python3
biprop - Identify a binary weight or binary weight and activation subnetwork within a randomly initialized network by only pruning and binarizing the network.
Note - Easily implement parallel training and distributed training. Machine learning library. Note.neuralnetwork.tf package include Llama2, Llama3, CLIP, ViT, ConvNeXt, SwiftFormer, etc, these models built with Note are compatible with TensorFlow and can be trained with TensorFlow.