neuralfit
ivy
| neuralfit | ivy | |
|---|---|---|
| 1 | 17 | |
| 16 | 14,015 | |
| - | 0.1% | |
| 10.0 | 10.0 | |
| about 1 year ago | 9 days ago | |
| Python | Python | |
| - | GNU General Public License v3.0 or later |
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.
neuralfit
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[P] NeuralFit now allows evolution of recurrent neural networks
Hi all! Some time ago I made a post about NeuralFit (https://github.com/neural-fit/neuralfit), which allows you to evolve neural networks with just a few lines of code.
ivy
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Keras 3.0
See also https://github.com/unifyai/ivy which I have not tried but seems along the lines of what you are describing, working with all the major frameworks
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Show HN: Carton – Run any ML model from any programming language
is this ancillary to what [these guys](https://github.com/unifyai/ivy) are trying to do?
- Ivy: All in one machine learning framework
- Ivy ML Transpiler and Framework
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[D] Keras 3.0 Announcement: Keras for TensorFlow, JAX, and PyTorch
https://unify.ai/ They are trying to do what Ivy is doing already.
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Ask for help: what is the best way to have code both support torch and numpy?
Check Ivy.
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CoreML Stable Diffusion
ROCm's great for data centers, but good luck finding anything about desktop GPUs on their site apart from this lone blog post: https://community.amd.com/t5/instinct-accelerators/exploring...
There's a good explanation of AMD's ROCm targets here: https://news.ycombinator.com/item?id=28200477
It's currently a PITA to get common Python libs like Numba to even talk to AMD cards (admittedly Numba won't talk to older Nvidia cards either and they deprecate ruthlessly; I had to downgrade 8 versions to get it working with a 5yo mobile workstation). YC-backed Ivy claims to be working on unifying ML frameworks in a hardware-agnostic way but I don't have enough experience to assess how well they're succeeding yet: https://lets-unify.ai
I was happy to see DiffusionBee does talk the GPU in my late-model intel Mac, though for some reason it only uses 50% of its power right now. I'm sure the situation will improve as Metal 3.0 and Vulkan get more established.
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DL Frameworks in a nutshell
Won't it all come together with https://lets-unify.ai/ ?
- Unified Machine Learning
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[Discussion] Opinions on unify AI
What do you think about unify AI https://lets-unify.ai.
What are some alternatives?
spaCy - 💫 Industrial-strength Natural Language Processing (NLP) in Python
PaddleNLP - 👑 Easy-to-use and powerful NLP and LLM library with 🤗 Awesome model zoo, supporting wide-range of NLP tasks from research to industrial applications, including 🗂Text Classification, 🔍 Neural Search, ❓ Question Answering, ℹ️ Information Extraction, 📄 Document Intelligence, 💌 Sentiment Analysis etc.
szabadfogasu-maszk - A face mask detection system using Tensorflow/Keras and OpenCV, for the "<19 Szabadfogású Számítógép" competition in 2020.
ColossalAI - Making large AI models cheaper, faster and more accessible
neataptic - :rocket: Blazing fast neuro-evolution & backpropagation for the browser and Node.js
DeepFaceLive - Real-time face swap for PC streaming or video calls
Pytorch - Tensors and Dynamic neural networks in Python with strong GPU acceleration
PaddleOCR - Awesome multilingual OCR toolkits based on PaddlePaddle (practical ultra lightweight OCR system, support 80+ languages recognition, provide data annotation and synthesis tools, support training and deployment among server, mobile, embedded and IoT devices)
GLOM-TensorFlow - An attempt at the implementation of GLOM, Geoffrey Hinton's paper for emergent part-whole hierarchies from data
lisp - Toy Lisp 1.5 interpreter
Kornia - Geometric Computer Vision Library for Spatial AI
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