Top 23 Python neural-network Projects
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The report output for scalene does look much nicer, but the slowness for me dropped me from continuing to use it. Maybe there's some bad interaction with tensorflow/pytest. I can try to make an example, but I'd guess if you try running it on tensorflows actual unit tests (something like this) you'd get similar behavior.
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Project mention: Use the infamous Deep Fakes project for things other than faces | reddit.com/r/learnmachinelearning | 2021-10-25
My current challenge is getting those masked wheel images to be able to swap between images, or to apply a new wheel on a car image. To get a decent result that doesn't look fake, it would have to do some minor warping and resizing. To me, this seems like exactly what the Deep Fakes repo does. https://github.com/deepfakes/faceswap
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Scout APM
Less time debugging, more time building. Scout APM allows you to find and fix performance issues with no hassle. Now with error monitoring and external services monitoring, Scout is a developer's best friend when it comes to application development.
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AI/ML is awesome and will bring a ton of good to the world, but there are also serious risks and safety considerations. Enhanced surveillance and State control is certainly one of them, and perhaps one of the ripest use cases for abuse is around facial recognition. One of the top trending repos in 2021 was Tencent’s GFPGAN, which ‘aims at developing Practical Algorithms for Real-world Face Restoration’. Another trending library was DeepFaceLab, for creating deep fakes. Note, famously in 2020, Huawei published about testing software for facial recognition of Uighurs. Earlier that year, IBM announced it would no longer develop facial recognition software. I come from a country where state surveillance is fairly normalized, albeit discreet. I’m talking journalists have their homes broken into, their messenger texts intercepted, and the secret police taps your cell phone type surveillance. So when our government bought 1000+ Huawei smart cameras a couple years back with facial recognition embedded, human rights activist were not thrilled.
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Project mention: How to 'practice' pytorch after finishing its basic tutorial? | reddit.com/r/learnmachinelearning | 2021-05-09
I tried to move straight to practicing implementing papers and trying to understand other people's codes but failed miserably. I feel like there was too much of a gap between the basic tutorial and being able to implement ideas into code....hence the question: Is there any resource/way to practice pytorch in general? I did find this and this, but I just wanted to hear what others have gone through to become better at PyTorch up to the point they can build stuff from their own ideas
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SpaCy is one of the most popular NLP libraries, and is very fast and flexible.
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Project mention: Where to learn how to turn photos into images in the style of famous painters? | reddit.com/r/MediaSynthesis | 2021-10-25
Try: https://github.com/lengstrom/fast-style-transfer . There are links to the underlying papers.
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Project mention: [D] Unsupervised Outlier Detection - Advise Requested | reddit.com/r/MachineLearning | 2021-12-03
The source code and documentaion of PyOD is the best survey about OOD. Besides, the normalized flow and VQVAE are also feasible.
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OPS
OPS - Build and Run Open Source Unikernels. Quickly and easily build and deploy open source unikernels in tens of seconds. Deploy in any language to any cloud.
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You could also use qubvel's segmentation models if you would like to explore semantic segmentation.
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igel
a delightful machine learning tool that allows you to train, test, and use models without writing code
Project mention: Train/fit, test, and use models without writing code | reddit.com/r/ArtificialInteligence | 2021-06-29Link to the repo: https://github.com/nidhaloff/igel
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ONNX opens an avenue for direct inference using a number of languages and platforms. For example, a model could be run directly on Android to limit data sent to a third party service. ONNX is an exciting development with a lot of promise. Microsoft has also released Hummingbird which enables exporting traditional models (sklearn, decision trees, logistical regression..) to ONNX.
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dm_control
DeepMind's software stack for physics-based simulation and Reinforcement Learning environments, using MuJoCo.
Project mention: [D] MuJoCo vs PyBullet? (esp. for custom environment) | reddit.com/r/reinforcementlearning | 2021-12-07If you're interested in using Mujoco, I'd suggest checking out the dm_control package for Python bindings rather than interfacing with C++ directly. I think one downside to Mujoco currently is that you cannot dynamically add objects, and the entire simulation is initialized and loaded according to the MJCF / XML file.
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Project mention: [D] Using activity regularization instead of batch norm. | reddit.com/r/MachineLearning | 2021-04-03
Tangentially, theoretically you can't use BN in the discriminator for WGAN-GP anyway (assuming that you're using the Gulrajani work) because it breaks the sample independence assumptions of the GP. If you have a relatively structured dataset (eg all faces, all cars, all giraffes, etc.) and no class conditioning, look into StyleGAN2-ADA for the best results. If you have a dataset with a lot of variation and a lot of classes try using the BigGAN repo.
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Project mention: Graph Attention Networks (GAT) v2 implementation with side-by-side notes | reddit.com/r/deeplearning | 2021-08-06
Code for https://arxiv.org/abs/1710.10903 found: https://github.com/PetarV-/GAT
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NCRFpp
NCRF++, a Neural Sequence Labeling Toolkit. Easy use to any sequence labeling tasks (e.g. NER, POS, Segmentation). It includes character LSTM/CNN, word LSTM/CNN and softmax/CRF components.
They still talk about Hidden Markov Models (HMMs) in quite a bit of detail in the sequence labelling chapter, but you are quite right, Conditional Random Fields (CRFs) and especially neural network based CRFs are in the top rankings when it comes to named entity recognition (NER) and part-of-speech tagging (POS), e.g. see https://github.com/jiesutd/NCRFpp.
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Project mention: When to go for an 'easy' time-series model vs. using a complex deep learning model (when having experience with the latter) | reddit.com/r/datascience | 2021-11-29
I'm a data trainee at this organisation. I wrote my master thesis about using an event clustering mechanism to enrich an existing dataset to improve short-term demand predictions, using Pytorch Forecasting using the temporal fusion transformer component, and LightGBM (and compare the models with and w/o the event feature, so 4 runs in total).
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As a researcher in RL & ML in a big industry lab, I would say most of my colleagues are moving to JAX 0https://github.com/google/jax], which this article kind of ignores. JAX is XLA-accelerated NumPy, it's cool beyond just machine learning, but only provides low-level linear algebra abstractions. However you can put something like Haiku [https://github.com/deepmind/dm-haiku] or Flax [https://github.com/google/flax] on top of it and get what the cool kids are using :)
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https://github.com/huggingface/transfer-learning-conv-ai (Requires an hefty GPU though...)
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hivemind
Decentralized deep learning in PyTorch. Built to train models on thousands of volunteers across the world.
The problem is that, currently, large ML models need to be trained on clusters of tightly-connected GPUs/accelerators. So it's kinda useless having a bunch of GPUs spread all over the world with huge latency and low bandwidth between them. That may change though - there are people working on it: https://github.com/learning-at-home/hivemind
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Project mention: Physics-Informed ML Simulator for Wildfire Propagation (Video) | reddit.com/r/Julia | 2021-02-15
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deep_learning_and_the_game_of_go
Code and other material for the book "Deep Learning and the Game of Go"
Project mention: Why do engines often evaluate completely winning endgame positions between +60 and +63? What's significant about the low 60's as an evaluation? Or is it just a placeholder when the computer can't quite find a forced mate? | reddit.com/r/chess | 2021-12-06If you want to understand how the new approach used by Leela Zero and Alpha Zero works, the book Deep Learning and the Game of Go is fun and easy to read. Although it's about Go rather than chess, most of the contents are equally relevant to chess.
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uncertainty-baselines
High-quality implementations of standard and SOTA methods on a variety of tasks.
Project mention: Google AI Introduces ‘Uncertainty Baselines Library’ For Uncertainty and Robustness in Deep Learning | reddit.com/r/artificial | 2021-10-17Code for https://arxiv.org/abs/2106.04015 found: https://github.com/google/uncertainty-baselines
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GeneticAlgorithmPython
Source code of PyGAD, a Python 3 library for building the genetic algorithm and training machine learning algorithms (Keras & PyTorch).
My pleasure! I'm assuming you're working in Python, so this repo looks like it could be promising (disclaimer: I haven't used it).
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- GitHub - raphaelsty/cherche: Neural search
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Index
What are some of the best open-source neural-network projects in Python? This list will help you:
| Project | Stars | |
|---|---|---|
| 1 | Keras | 53,748 |
| 2 | faceswap | 40,151 |
| 3 | DeepFaceLab | 30,841 |
| 4 | pytorch-tutorial | 22,814 |
| 5 | spaCy | 22,176 |
| 6 | fast-style-transfer | 10,277 |
| 7 | pyod | 5,181 |
| 8 | segmentation_models.pytorch | 4,752 |
| 9 | igel | 2,963 |
| 10 | hummingbird | 2,723 |
| 11 | dm_control | 2,637 |
| 12 | BigGAN-PyTorch | 2,482 |
| 13 | GAT | 2,267 |
| 14 | NCRFpp | 1,754 |
| 15 | pytorch-forecasting | 1,665 |
| 16 | dm-haiku | 1,662 |
| 17 | transfer-learning-conv-ai | 1,383 |
| 18 | minisom | 991 |
| 19 | hivemind | 904 |
| 20 | deepxde | 825 |
| 21 | deep_learning_and_the_game_of_go | 782 |
| 22 | uncertainty-baselines | 775 |
| 23 | GeneticAlgorithmPython | 752 |
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