Search Results for "neural net python"
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Showing 597 open source projects for "neural net python"
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1
XX-Net
A web proxy tool
XX-Net is an easy-to-use, anti-censorship web proxy tool from China. It includes GAE_proxy and X-Tunnel, with support for multiple platforms. -
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Autograd
Efficiently computes derivatives of numpy code
Autograd can automatically differentiate native Python and Numpy code. It can handle a large subset of Python's features, including loops, ifs, recursion and closures, and it can even take derivatives of derivatives of derivatives. It supports reverse-mode differentiation (a.k.a. backpropagation), which means it can efficiently take gradients of scalar-valued functions with respect to array-valued arguments, as well as forward-mode differentiation, and the two can be composed arbitrarily.... -
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Python Outlier Detection
A Python toolbox for scalable outlier detection
...PyOD has multiple neural network-based models, e.g., AutoEncoders, which are implemented in both PyTorch and Tensorflow. PyOD contains multiple models that also exist in scikit-learn. It is possible to train and predict with a large number of detection models in PyOD by leveraging SUOD framework. A benchmark is supplied for select algorithms to provide an overview of the implemented models. -
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neural-style in TensorFlow
Neural style in TensorFlow
neural-style in TensorFlow is a TensorFlow implementation of neural style transfer. It creates a new image by combining the content of one image with the artistic style of one or more style images. The project uses TensorFlow automatic differentiation and the Adam optimizer rather than the original L-BFGS approach. Users can run it from the command line by providing a content image, style image inputs, and an output path. It supports checkpoint outputs, iteration control, style blending, and... -
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NeuralForecast
Scalable and user friendly neural forecasting algorithms.
NeuralForecast offers a large collection of neural forecasting models focusing on their performance, usability, and robustness. The models range from classic networks like RNNs to the latest transformers: MLP, LSTM, GRU, RNN, TCN, TimesNet, BiTCN, DeepAR, NBEATS, NBEATSx, NHITS, TiDE, DeepNPTS, TSMixer, TSMixerx, MLPMultivariate, DLinear, NLinear, TFT, Informer, AutoFormer, FedFormer, PatchTST, iTransformer, StemGNN, and TimeLLM. There is a shared belief in Neural forecasting methods'... -
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Playwright for Python
Python version of the Playwright testing and automation library
...Single API to automate Chromium, Firefox and WebKit. Capable automation for single page apps that rely on the modern web platform. Use the Playwright API in JavaScript & TypeScript, Python, .NET and, Java. With Playwright, test how your app behaves in Apple Safari with WebKit builds for Windows, Linux and macOS. Test locally and on CI. Use device emulation to test your responsive web apps in mobile web browsers. Playwright supports headless (without browser UI) and headed (with browser UI) modes for all browsers and all platforms. ... -
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Stock prediction deep neural learning
Predicting stock prices using a TensorFlow LSTM
Predicting stock prices can be a challenging task as it often does not follow any specific pattern. However, deep neural learning can be used to identify patterns through machine learning. One of the most effective techniques for series forecasting is using LSTM (long short-term memory) networks, which are a type of recurrent neural network (RNN) capable of remembering information over a long period of time. This makes them extremely useful for predicting stock prices. Predicting stock... -
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BindsNET
Simulation of spiking neural networks (SNNs) using PyTorch
A Python package used for simulating spiking neural networks (SNNs) on CPUs or GPUs using PyTorch Tensor functionality. BindsNET is a spiking neural network simulation library geared towards the development of biologically inspired algorithms for machine learning. This package is used as part of ongoing research on applying SNNs to machine learning (ML) and reinforcement learning (RL) problems in the Biologically Inspired Neural & Dynamical Systems (BINDS) lab. -
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Chemprop
Message Passing Neural Networks for Molecule Property Prediction
Chemprop is a repository containing message-passing neural networks for molecular property prediction. -
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PySR
High-Performance Symbolic Regression in Python and Julia
...Symbolic regression works best on low-dimensional datasets, but one can also extend these approaches to higher-dimensional spaces by using "Symbolic Distillation" of Neural Networks, as explained in 2006.11287, where we apply it to N-body problems. Here, one essentially uses symbolic regression to convert a neural net to an analytic equation. Thus, these tools simultaneously present an explicit and powerful way to interpret deep neural networks. -
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Haiku
JAX-based neural network library
...It preserves Sonnet’s module-based programming model for state management while retaining access to JAX’s function transformations. Haiku can be expected to compose with other libraries and work well with the rest of JAX. Similar to Sonnet modules, Haiku modules are Python objects that hold references to their own parameters, other modules, and methods that apply functions on user inputs. -
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Ultimate Vocal Remover (UVR5)
GUI for a Vocal Remover that uses Deep Neural Networks
This application uses state-of-the-art source separation models to remove vocals from audio files. UVR's core developers trained all of the models provided in this package (except for the Demucs v3 and v4 4-stem models). -
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SKORCH
A scikit-learn compatible neural network library that wraps PyTorch
A scikit-learn compatible neural network library that wraps PyTorch. -
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micrograd
A tiny scalar-valued autograd engine and a neural net library
micrograd is a tiny, educational automatic differentiation engine focused on scalar values, built to show how backpropagation works end to end with minimal code. It constructs a dynamic computation graph as you perform math operations and then computes gradients by walking that graph backward, making it an approachable “from scratch” autograd reference. On top of the core autograd “Value” concept, the project includes a small neural network library that lets you define and train simple... -
15
Penzai
A JAX research toolkit to build, edit, & visualize neural networks
Penzai, developed by Google DeepMind, is a JAX-based library for representing, visualizing, and manipulating neural network models as functional pytree data structures. It is designed to make machine learning research more interpretable and interactive, particularly for tasks like model surgery, ablation studies, architecture debugging, and interpretability research. Unlike conventional neural network libraries, Penzai exposes the full internal structure of models, enabling fine-grained... -
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Stanza
Stanford NLP Python library for many human languages
...The toolkit is designed to be parallel among more than 70 languages, using the Universal Dependencies formalism. Stanza is built with highly accurate neural network components that also enable efficient training and evaluation with your own annotated data. -
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Hummingbird
Hummingbird compiles trained ML models into tensor computation
Hummingbird is a library for compiling trained traditional ML models into tensor computations. Hummingbird allows users to seamlessly leverage neural network frameworks (such as PyTorch) to accelerate traditional ML models. Thanks to Hummingbird, users can benefit from (1) all the current and future optimizations implemented in neural network frameworks; (2) native hardware acceleration; (3) having a unique platform to support both traditional and neural network models; and having all of... -
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DeepCTR-Torch
Easy-to-use,Modular and Extendible package of deep-learning models
...Factorization-Machine and it’s variants are widely used to learn the low-order feature interaction. High-order Extractor learns feature combination through complex neural network functions like MLP, Cross Net, etc. -
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nunif
Misc; latest version of waifu2x; 2D video to stereo 3D video
nunif is a deep learning–based image processing framework focused on image upscaling, restoration, denoising, and enhancement tasks using neural network models. The project provides a collection of AI-powered utilities designed primarily for anime-style artwork, illustrations, and high-quality image restoration workflows. It includes command-line tools and graphical interfaces for applying trained neural models to improve image resolution and visual clarity while minimizing artifacts. nunif... -
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Keras
Python-based neural networks API
Python Deep Learning library -
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SpikingJelly
SpikingJelly is an open-source deep learning framework
SpikingJelly is an open-source deep learning framework for spiking neural networks that is primarily built on top of PyTorch and aimed at neuromorphic computing research. The project provides the components needed to build, train, and evaluate neural models that communicate through discrete spikes rather than the continuous activations used in conventional artificial neural networks. This makes it especially relevant for researchers interested in biologically inspired computing, event-driven... -
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Bot Framework SDK for Python
Build and connect intelligent bots that interact naturally
This repository contains code for the Python version of the Microsoft Bot Framework SDK, which is part of the Microsoft Bot Framework - a comprehensive framework for building enterprise-grade conversational AI experiences. This SDK enables developers to model conversation and build sophisticated bot applications using Python. SDKs for JavaScript and .NET are also available. -
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CoreNet
CoreNet: A library for training deep neural networks
CoreNet is Apple’s internal deep learning framework for distributed neural network training, designed for high scalability, low-latency communication, and strong hardware efficiency. It focuses on enabling large-scale model training across clusters of GPUs and accelerators by optimizing data flow and parallelism strategies. CoreNet provides abstractions for data, tensor, and pipeline parallelism, allowing models to scale without code duplication or heavy manual configuration. Its distributed... -
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Bittensor
Internet-scale Neural Networks
Bittensor is a decentralized machine learning protocol that allows AI models to collaborate, learn, and earn tokens within a global network. It introduces a blockchain-based economy for neural networks, where participants are incentivized to contribute valuable knowledge and compute power. Bittensor combines peer-to-peer learning with on-chain rewards, creating a self-governing, scalable AI system that evolves without centralized control. It is a novel approach to aligning incentives in AI... -
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DeepVariant
DeepVariant is an analysis pipeline that uses a deep neural networks
DeepVariant is an analysis pipeline that uses a deep neural network to call genetic variants from next-generation DNA sequencing data. DeepVariant is a deep learning-based variant caller that takes aligned reads (in BAM or CRAM format), produces pileup image tensors from them, classifies each tensor using a convolutional neural network, and finally reports the results in a standard VCF or gVCF file. DeepTrio is a deep learning-based trio variant caller built on top of DeepVariant. DeepTrio...
