Top 23 Python Deeplearning Projects

• faceswap

  10 49,112 8.0 Python

  Deepfakes Software For All

Project mention: faceswap VS facefusion - a user suggested alternative | libhunt.com/r/faceswap | 2024-01-30

• horovod

  8 13,942 5.8 Python

  Distributed training framework for TensorFlow, Keras, PyTorch, and Apache MXNet.

  

• InfluxDB

  www.influxdata.com sponsored

  Power Real-Time Data Analytics at Scale. Get real-time insights from all types of time series data with InfluxDB. Ingest, query, and analyze billions of data points in real-time with unbounded cardinality.

  

• ludwig

  3 10,778 9.5 Python

  Low-code framework for building custom LLMs, neural networks, and other AI models

  

Project mention: Show HN: Toolkit for LLM Fine-Tuning, Ablating and Testing | news.ycombinator.com | 2024-04-07

This is a great project, little bit similar to https://github.com/ludwig-ai/ludwig, but it includes testing capabilities and ablation.

questions regarding the LLM testing aspect: How extensive is the test coverage for LLM use cases, and what is the current state of this project area? Do you offer any guarantees, or is it considered an open-ended problem?

Would love to see more progress toward this area!

• NeMo

  29 9,951 9.8 Python

  NeMo: a framework for generative AI

  

Project mention: [P] Making a TTS voice, HK-47 from Kotor using Tortoise (Ideally WaveRNN) | /r/MachineLearning | 2023-07-06

I don't test WaveRNN but from the ones that I know the best that is open source is FastPitch. And it's easy to use, here is the tutorial for voice cloning.

• awesome-chatgpt-zh

  2 9,817 8.2 Python

  ChatGPT 中文指南🔥，ChatGPT 中文调教指南，指令指南，应用开发指南，精选资源清单，更好的使用 chatGPT 让你的生产力 up up up! 🚀

• nebuly

  105 8,368 8.4 Python

  The user analytics platform for LLMs

  

Project mention: Nebuly – The LLM Analytics Platform | news.ycombinator.com | 2023-10-07

• clearml

  20 5,217 8.1 Python

  ClearML - Auto-Magical CI/CD to streamline your AI workload. Experiment Management, Data Management, Pipeline, Orchestration, Scheduling & Serving in one MLOps/LLMOps solution

  

Project mention: FLaNK Stack Weekly 12 February 2024 | dev.to | 2024-02-12

• WorkOS

  workos.com sponsored

  The modern identity platform for B2B SaaS. The APIs are flexible and easy-to-use, supporting authentication, user identity, and complex enterprise features like SSO and SCIM provisioning.

  

• MiDaS

  26 4,057 2.4 Python

  Code for robust monocular depth estimation described in "Ranftl et. al., Towards Robust Monocular Depth Estimation: Mixing Datasets for Zero-shot Cross-dataset Transfer, TPAMI 2022"

  

Project mention: Distance estimation from monocular vision using deep learning | /r/computervision | 2023-06-13

Hi, I have made use of the KITTI dataset for this, and yes it depends on objects of know sizes. Here I have defined the following classes: Car, Van, Truck, Pedestrian, Person_sitting, Cyclist, Tram, Misc, or DontCare and the predictions are pretty accurate for those classes. Even if it's not the same class, it still recognizes the object since I have made use of the coco names dataset here and that is used along with YOLO for object detection. And there are several already implemented projects that make use of deep learning models trained on 2D datasets to predict 3D distance. This was one of my inspirations for this project: https://blogs.nvidia.com/blog/2019/06/19/drive-labs-distance-to-object-detection/ Furthermore, there are well-documented and researched papers like DistYOLO or MiDaS that makes use of deep learning for depth estimation

• PyTorchZeroToAll

  1 3,825 0.0 Python

  Simple PyTorch Tutorials Zero to ALL!

• InvoiceNet

  4 2,378 3.9 Python

  Deep neural network to extract intelligent information from invoice documents.

• X-AnyLabeling

  1 2,360 9.6 Python

  Effortless data labeling with AI support from Segment Anything and other awesome models.

Project mention: X-AnyLabeling: Effortless Data Labeling with AI, Segment Anything and Others | news.ycombinator.com | 2024-03-19

• contrastive-unpaired-translation

  6 2,096 2.1 Python

  Contrastive unpaired image-to-image translation, faster and lighter training than cyclegan (ECCV 2020, in PyTorch)

• DeBERTa

  4 1,844 3.6 Python

  The implementation of DeBERTa

  

• QualityScaler

  69 1,708 8.0 Python

  QualityScaler - image/video deeplearning upscaling for any GPU

Project mention: How do I change the learning hub video resolution? | /r/ANSYS | 2023-12-10

• pycm

  18 1,428 5.0 Python

  Multi-class confusion matrix library in Python

Project mention: PyCM 4.0 Released: Multilabel Confusion Matrix Support | /r/coolgithubprojects | 2023-06-07

• pygod

  3 1,207 8.7 Python

  A Python Library for Graph Outlier Detection (Anomaly Detection)

  

Project mention: RAG Using Structured Data: Overview and Important Questions | news.ycombinator.com | 2024-01-10

Ok, using ChatGPT and Bard (the irony lol) I learned a bit more about GNNs:

GNNs are probabilistic and can be trained to learn representations in graph-structured data and handling complex relationships, while classical graph algorithms are specialized for specific graph analysis tasks and operate based on predefined rules/steps.

* Why is PyG it called "Geometric" and not "Topologic" ?

Properties like connectivity, neighborhoods, and even geodesic distances can all be considered topological features of a graph. These features remain unchanged under continuous deformations like stretching or bending, which is the defining characteristic of topological equivalence. In this sense, "PyTorch Topologic" might be a more accurate reflection of the library's focus on analyzing the intrinsic structure and connections within graphs.

However, the term "geometric" still has some merit in the context of PyG. While most GNN operations rely on topological principles, some do incorporate notions of Euclidean geometry, such as:

- Node embeddings: Many GNNs learn low-dimensional vectors for each node, which can be interpreted as points in a vector space, allowing geometric operations like distances and angles to be applied.

- Spectral GNNs: These models leverage the eigenvalues and eigenvectors of the graph Laplacian, which encodes information about the geometric structure and distances between nodes.

- Manifold learning: Certain types of graphs can be seen as low-dimensional representations of high-dimensional manifolds. Applying GNNs in this context involves learning geometric properties on the manifold itself.

Therefore, although topology plays a primary role in understanding and analyzing graphs, geometry can still be relevant in certain contexts and GNN operations.

* Real world applications:

- HuggingFace has a few models [0] around things like computational chemistry [1] or weather forecasting.

- PyGod [2] can be used for Outlier Detection (Anomaly Detection).

- Apparently ULTRA [3] can "infer" (in the knowledge graph sense), that Michael Jackson released some disco music :-p (see the paper).

- RGCN [4] can be used for knowledge graph link prediction (recovery of missing facts, i.e. subject-predicate-object triples) and entity classification (recovery of missing entity attributes).

- GreatX [5] tackles removing inherent noise, "Distribution Shift" and "Adversarial Attacks" (ex: noise purposely introduced to hide a node presence) from networks. Apparently this is a thing and the field is called "Graph Reliability" or "Reliable Deep Graph Learning". The author even has a bunch of "awesome" style lists of links! [6]

- Finally this repo has a nice explanation of how/why to run machine learning algorithms "outside of the DB":

"Pytorch Geometric (PyG) has a whole arsenal of neural network layers and techniques to approach machine learning on graphs (aka graph representation learning, graph machine learning, deep graph learning) and has been used in this repo [7] to learn link patterns, also known as link or edge predictions."

--

0: https://huggingface.co/models?pipeline_tag=graph-ml&sort=tre...

1: https://github.com/Microsoft/Graphormer

2: https://github.com/pygod-team/pygod

3: https://github.com/DeepGraphLearning/ULTRA

4: https://huggingface.co/riship-nv/RGCN

5: https://github.com/EdisonLeeeee/GreatX

6: https://edisonleeeee.github.io/projects.html

7: https://github.com/Orbifold/pyg-link-prediction

• PixelLib

  3 1,008 0.0 Python

  Visit PixelLib's official documentation https://pixellib.readthedocs.io/en/latest/

• kogpt

  1 996 4.2 Python

  KakaoBrain KoGPT (Korean Generative Pre-trained Transformer) (by kakaobrain)

  

• PaddleHelix

  1 782 5.4 Python

  Bio-Computing Platform Featuring Large-Scale Representation Learning and Multi-Task Deep Learning “螺旋桨”生物计算工具集

  

• Neupy

  0 740 0.0 Python

  NeuPy is a Tensorflow based python library for prototyping and building neural networks

• trankit

  1 705 6.5 Python

  Trankit is a Light-Weight Transformer-based Python Toolkit for Multilingual Natural Language Processing

• MetaSpore

  11 627 3.7 Python

  A unified end-to-end machine intelligence platform

  

• PyTorch_CIFAR10

  1 600 0.0 Python

  Pretrained TorchVision models on CIFAR10 dataset (with weights)

• SaaSHub

  www.saashub.com sponsored

  SaaSHub - Software Alternatives and Reviews. SaaSHub helps you find the best software and product alternatives

  

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• A note from our sponsor - SaaSHub
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