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Top 23 Python neural-network Projects
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Pandas comes with many complex tabular data operations. And, since it exists in a Python environment, it can be coupled with lots of other powerful libraries, such as Requests (for connecting to other APIs), Matplotlib (for plotting data), Keras (for training machine learning models), and many more.
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Project mention: [D] How is it checked if models do not just memorize their training examples? | reddit.com/r/MachineLearning | 2022-04-28
But there's a nice survey on Arxiv here of various deepfake / face swap methods. Some of methods listed in the table on page 4, such as Faceswap and Faceswap-GAN, apparently use encoder-decoder models. I think Faceswap-GAN was the one that I was thinking of in particular; apparently it adds a perceptual loss and an adversarial loss to an autoencoder.
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Sonar
Write Clean Python Code. Always.. Sonar helps you commit clean code every time. With over 225 unique rules to find Python bugs, code smells & vulnerabilities, Sonar finds the issues while you focus on the work.
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We have the technology....
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I was going through this example of a LSTM language model on github (link).What it does in general is pretty clear to me. But I'm still struggling to understand what calling contiguous() does, which occurs several times in the code.
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Tools: Hugging Face SpaCy Scikit-Learn MLFlow There is no flag to discern a human owner vs a corporate entity, so you have to figure it out on your own. ML can assist given there are tens of thousands of records to go.
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This basic example from segmentation models PyTorch repo would be good tutorial to start with. The library is very good, I like the unet, fpn and deeplabv3+ architectures with regnety as encoder https://github.com/qubvel/segmentation_models.pytorch/blob/master/examples/binary_segmentation_intro.ipynb
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InfluxDB
Build time-series-based applications quickly and at scale.. InfluxDB is the Time Series Platform where developers build real-time applications for analytics, IoT and cloud-native services. Easy to start, it is available in the cloud or on-premises.
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Project mention: Pyod – A Comprehensive and Scalable Python Library for Outlier Detection | news.ycombinator.com | 2022-08-10
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Sorry maybe someone could chime in and help but I use chainer to upscale. https://github.com/chainer/chainer
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Project mention: How can we model an observation space of an env with different features and sizes. | reddit.com/r/reinforcementlearning | 2022-12-20
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You can also have a look at these later surveys that give an idea of the different types of GNNs. Also if you prefer Tensorflow you can use the Graph Nets library.
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There are many good options when it comes to tools and libraries for implementing data augmentation into our deep learning pipeline. You could for instance do your own augmentations using NumPy or Pillow. Some of the most popular dedicated libraries for image augmentation include Albumentations, imgaug, and Augmentor. Both TensorFlow and PyTorch even come with their own packages dedicated to image augmentation.
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I was looking at [`gluonts`](https://github.com/awslabs/gluonts/tree/dev/src/gluonts/core) source code and I found a `py.typed` file. That is something I always put in my type-annotated modules: it's literally an empty file which denotes that the module is marked for "internal or external use in type checking" [mypy docs](https://mypy.readthedocs.io/en/stable/installed_packages.html?highlight=py.typed#creating-pep-561-compatible-packages). However, I never saw before the `.typesafe` file. What does it denote? Does it have to be used alongside a `py.typed`?
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If are interested in just predictions you can try Hummingbird. It is part of the PyTorch ecosystem. We get already trained scikit-learn models and translate them into PyTorch models. From them you can run your model on any hardware support by PyTorch, export it into TVM, ONNX, etc. Performance on hardware acceleration is quite good (orders of magnitude better than scikit-learn is some cases)
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dm_control
DeepMind's software stack for physics-based simulation and Reinforcement Learning environments, using MuJoCo.
Project mention: Installing & Using MuJoCo 2.1.5 with OpenAi Gym | reddit.com/r/reinforcementlearning | 2022-05-18Deepmind Control Suite is a good alternative to Open AI Gym for continuous control tasks. It contains many of the environments present in Gym and also a few extra ones. Deepmind Control Suite also uses Mujoco. I found the installation to be straightforward. Check out https://github.com/deepmind/dm_control
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igel
a delightful machine learning tool that allows you to train, test, and use models without writing code
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Project mention: [D] Am I stupid for avoiding high level frameworks? | reddit.com/r/MachineLearning | 2022-11-23
You may consider using Composer Composer by MosaicML.
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BigGAN (https://github.com/ajbrock/BigGAN-PyTorch) - This is a PyTorch implementation of the BigGAN model for generating high-resolution images. It is trained on a large dataset and can generate a wide range of images, including photographs of animals, objects, and landscapes.
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Project mention: [D] Dr. Petar Veličković (Deepmind) - Categories, Graphs, Reasoning and Graph Expander Propagation | reddit.com/r/MachineLearning | 2022-12-09
Found relevant code at https://github.com/PetarV-/GAT + all code implementations here
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Project mention: LSTM/CNN architectures for time series forecasting[Discussion] | reddit.com/r/MachineLearning | 2022-05-06
Pytorch-forecasting
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I am fresh to nix os especially when it comes to using python on it how do I install packages withought using pip I need to install numpy~=1.19.5 transformers~=4.8.2 tqdm~=4.45.0 setuptools~=51.3.3 wandb>=0.11.2 einops~=0.3.0 requests~=2.25.1 fabric~=2.6.0 optax==0.0.6 git+https://github.com/deepmind/dm-haiku git+https://github.com/EleutherAI/lm-evaluation-harness/ ray[default]==1.4.1 jax~=0.2.12 Flask~=1.1.2 cloudpickle~=1.3.0 tensorflow-cpu~=2.5.0 google-cloud-storage~=1.36.2 smart_open[gcs] func_timeout ftfy fastapi uvicorn lm_dataformat which I can just do pip -r thetxtfile but idk how to do this in nix os also I would be using python3.7 so far this is what I have come up with but I know its wrong { pkgs ? import {} }: let packages = python-packages: with python-packages; [ mesh-transformer-jax/ jax==0.2.12 numpy~=1.19.5 transformers~=4.8.2 tqdm~=4.45.0 setuptools~=51.3.3 wandb>=0.11.2 einops~=0.3.0 requests~=2.25.1 fabric~=2.6.0 optax==0.0.6 #the other packages ]; pkgs.mkShell { nativeBuildInputs = [ pkgs.buildPackages.python37 ]; }
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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.
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Project mention: [D] [R] Dialogue generation with contrastive objectives | reddit.com/r/MachineLearning | 2022-06-07
Code for https://arxiv.org/abs/1901.08149 found: https://github.com/huggingface/transfer-learning-conv-ai
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NOTE:
The open source projects on this list are ordered by number of github stars.
The number of mentions indicates repo mentiontions in the last 12 Months or
since we started tracking (Dec 2020).
The latest post mention was on 2023-02-01.
Python neural-networks related posts
- [Q] `py.typed` and `.typesafe`
- [D] is it time to investigate retrieval language models?
- Medical Image Segmentation Human Retina
- 🚀 AWS launches Fortuna, an open-source library for Uncertainty Quantification
- 🚀 AWS launches Fortuna, an open-source library for Uncertainty Quantification
- Automatic generation of image-segmentation mask pairs with StableDiffusion
- [D] GPU-enabled scikit-learn
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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 | 57,203 |
| 2 | faceswap | 43,325 |
| 3 | DeepFaceLab | 36,976 |
| 4 | pytorch-tutorial | 25,543 |
| 5 | spaCy | 25,101 |
| 6 | fast-style-transfer | 10,648 |
| 7 | segmentation_models.pytorch | 6,728 |
| 8 | pyod | 6,677 |
| 9 | chainer | 5,765 |
| 10 | keras-rl | 5,368 |
| 11 | graph_nets | 5,216 |
| 12 | Augmentor | 4,850 |
| 13 | gluonts | 3,318 |
| 14 | hummingbird | 3,085 |
| 15 | dm_control | 3,049 |
| 16 | igel | 3,024 |
| 17 | composer | 2,934 |
| 18 | BigGAN-PyTorch | 2,637 |
| 19 | GAT | 2,627 |
| 20 | pytorch-forecasting | 2,561 |
| 21 | dm-haiku | 2,349 |
| 22 | NCRFpp | 1,846 |
| 23 | transfer-learning-conv-ai | 1,532 |
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