frugally-deep
tensorflow
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| frugally-deep | tensorflow | |
|---|---|---|
| 5 | 222 | |
| 1,043 | 182,456 | |
| - | 0.8% | |
| 8.0 | 10.0 | |
| 7 days ago | 3 days ago | |
| C++ | C++ | |
| MIT License | Apache License 2.0 |
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.
frugally-deep
- Frugally-deep: Header-only library for using Keras (TensorFlow) models in C++
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Ask HN: What side projects landed you a job?
The interview for my current job first went mediocre, but by talking about frugally-deep (a side project of mine) I was able to excite my (now) employer. :-)
https://github.com/Dobiasd/frugally-deep
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Way to convert keras based model to C++.
Did you work through the FAQ here: https://github.com/Dobiasd/frugally-deep/blob/master/FAQ.md
- Handwritten digit recognition using CircuitPython, Raspberry Pi Pico, OV7670 and 120x160 TFT LCD. r/circuitpython - Handwritten digit recognition using CircuitPython, Raspberry Pi Pico, OV7670 and 120x160 TFT LCD.
tensorflow
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Google lays off its Python team
[3]: https://github.com/tensorflow/tensorflow/graphs/contributors
- TensorFlow-metal on Apple Mac is junk for training
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🔥🚀 Top 10 Open-Source Must-Have Tools for Crafting Your Own Chatbot 🤖💬
To get up to speed with TensorFlow, check their quickstart Support TensorFlow on GitHub ⭐
- One .gitignore to rule them all
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10 Github repositories to achieve Python mastery
Explore here.
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GitHub and Developer Ecosystem Control
Part of the major userbase pull in GitHub revolves around hosting a considerable number of popular projects including Angular, React, Kubernetes, cpython, Ruby, tensorflow, and well even the software that powers this site Forem.
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Non-determinism in GPT-4 is caused by Sparse MoE
Right but that's not an inherent GPU determinism issue. It's a software issue.
https://github.com/tensorflow/tensorflow/issues/3103#issueco... is correct that it's not necessary, it's a choice.
Your line of reasoning appears to be "GPUs are inherently non-deterministic don't be quick to judge someone's code" which as far as I can tell is dead wrong.
Admittedly there are some cases and instructions that may result in non-determinism but they are inherently necessary. The author should thinking carefully before introducing non-determinism. There are many scenarios where it is irrelevant, but ultimately the issue we are discussing here isn't the GPU's fault.
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Can someone explain how keras code gets into the Tensorflow package?
and things like y = layers.ELU()(y) work as expected. I wanted to see a list of the available layers so I went to the Tensorflow GitHub repository and to the keras directory. There's a warning in that directory that says:
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Is it even possible to design a ML model without using Python or MATLAB? Like using C++, C or Java?
Exactly what language do you think TensorFlow is written in? :)
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How to do deep learning with Caffe?
You can use Tensorflow's deep learning API for this.
What are some alternatives?
Pytorch - Tensors and Dynamic neural networks in Python with strong GPU acceleration
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tiny-cnn - header only, dependency-free deep learning framework in C++14
Prophet - Tool for producing high quality forecasts for time series data that has multiple seasonality with linear or non-linear growth.
Taskflow - A General-purpose Parallel and Heterogeneous Task Programming System
Pandas - Flexible and powerful data analysis / manipulation library for Python, providing labeled data structures similar to R data.frame objects, statistical functions, and much more
Genann - simple neural network library in ANSI C
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nano
scikit-learn - scikit-learn: machine learning in Python
Native System Automation - Native cross-platform system automation
LightFM - A Python implementation of LightFM, a hybrid recommendation algorithm.