Search Results for "learn python source codes" - Page 9
Sort By:
Showing 438 open source projects for "learn python source codes"
View related business solutions-
Demo Series - Small Business Backup By VeeamWatch this on-demand demo series and learn how to protect your Microsoft 365 data with clear, simple, actionable steps that are easy to implement for businesses of all sizes.
-
MongoDB Atlas runs apps anywhereMongoDB Atlas gives you the freedom to build and run modern applications anywhere—across AWS, Azure, and Google Cloud. With global availability in over 115 regions, Atlas lets you deploy close to your users, meet compliance needs, and scale with confidence across any geography.
-
1
ImageAI
A python library built to empower developers
ImageAI is an easy-to-use Computer Vision Python library that empowers developers to easily integrate state-of-the-art Artificial Intelligence features into their new and existing applications and systems. It is used by thousands of developers, students, researchers, tutors and experts in corporate organizations around the world. You will find features supported, links to official documentation as well as articles on ImageAI. ImageAI is widely used around the world by professionals,... -
2
UnionML
Build and deploy machine learning microservices
Creating ML apps should be simple and frictionless. UnionML is an open-source Python framework built on top of Flyte™, unifying the complex ecosystem of ML tools into a single interface. Combine the tools that you love using a simple, standardized API so you can stop writing so much boilerplate and focus on what matters: the data and the models that learn from them. Fit the rich ecosystem of tools and frameworks into a common protocol for machine learning. -
3
Reinforcement-learning
Implementation of Reinforcement Learning Algorithms. Python, OpenAI
Reinforcement-learning is a widely used educational repository that provides implementations, exercises, and solutions for a broad range of reinforcement learning algorithms, designed to complement foundational texts and courses in the field. The project collects popular approaches such as dynamic programming, Monte Carlo methods, temporal difference learning, Q-learning, SARSA, deep Q-networks, and policy gradient techniques, often demonstrated with Python and OpenAI Gym environments so... -
4
Shennina
Automating Host Exploitation with AI
Shennina is an automated host exploitation framework. The mission of the project is to fully automate the scanning, vulnerability scanning/analysis, and exploitation using Artificial Intelligence. Shennina is integrated with Metasploit and Nmap for performing the attacks, as well as being integrated with an in-house Command-and-Control Server for exfiltrating data from compromised machines automatically. Shennina scans a set of input targets for available network services, uses its AI engine... -
$300 Free Credits to Build on Google CloudPut your $300 in credit toward real workloads, then keep building with free monthly usage for 20+ products. No commitment and no charge until you upgrade.
-
5
BCI
BCI: Breast Cancer Immunohistochemical Image Generation
Breast Cancer Immunohistochemical Image Generation through Pyramid Pix2pix. We have released the trained model on BCI and LLVIP datasets. We host a competition for breast cancer immunohistochemistry image generation on Grand Challenge. Project pix2pix provides a python script to generate pix2pix training data in the form of pairs of images {A,B}, where A and B are two different depictions of the same underlying scene, these can be pairs {HE, IHC}. Then we can learn to translate A(HE images)... -
6
DialoGPT
Large-scale pretraining for dialogue
DialoGPT is an open-source conversational language model developed by Microsoft Research for generating natural dialogue responses using large-scale transformer architectures. The system is built on the GPT-2 architecture and is designed specifically for multi-turn conversation tasks, enabling machines to produce coherent responses during interactive dialogue. The model was trained on a massive dataset of approximately 147 million conversational exchanges extracted from Reddit discussion... -
7
Yellowbrick
Visual analysis and diagnostic tools to facilitate ML selection
Yellowbrick extends the Scikit-Learn API to make model selection and hyperparameter tuning easier. Under the hood, it’s using Matplotlib. Yellowbrick is a suite of visual diagnostic tools called "Visualizers" that extend the scikit-learn API to allow human steering of the model selection process. In a nutshell, Yellowbrick combines scikit-learn with matplotlib in the best tradition of the scikit-learn documentation, but to produce visualizations for your machine learning workflow. -
8
Emb-GAM
An interpretable and efficient predictor using pre-trained models
Deep learning models have achieved impressive prediction performance but often sacrifice interpretability, a critical consideration in high-stakes domains such as healthcare or policymaking. In contrast, generalized additive models (GAMs) can maintain interpretability but often suffer from poor prediction performance due to their inability to effectively capture feature interactions. In this work, we aim to bridge this gap by using pre-trained neural language models to extract embeddings for... -
9
Gym
Toolkit for developing and comparing reinforcement learning algorithms
Gym by OpenAI is a toolkit for developing and comparing reinforcement learning algorithms. It supports teaching agents, everything from walking to playing games like Pong or Pinball. Open source interface to reinforce learning tasks. The gym library provides an easy-to-use suite of reinforcement learning tasks. Gym provides the environment, you provide the algorithm. You can write your agent using your existing numerical computation library, such as TensorFlow or Theano. It makes no... -
Paessler: Easy to Use With Enterprise Power. Free TrialYou shouldn't have to choose between a monitoring tool that's easy to use and one that's powerful enough for a complex environment. PRTG's low-code interface lets any admin build dashboards, set alerts and monitor devices without scripting, while custom sensors and full API access are there when your team needs deeper control. One platform, no compromise. Download a free 30-day trial now.
-
10
KAIR
Image Restoration Toolbox (PyTorch). Training and testing codes
Image restoration toolbox (PyTorch). Training and testing codes for DPIR, USRNet, DnCNN, FFDNet, SRMD, DPSR, BSR/GAN, SwinIR. -
11
AnimeGAN
A simple PyTorch Implementation of Generative Adversarial Networks
A simple PyTorch Implementation of Generative Adversarial Networks, focusing on anime face drawing. The images are generated from a DCGAN model trained on 143,000 anime character faces for 100 epochs. Manipulating latent codes enables the transition from images in the first row to the last row. The images are not clean, some outliers can be observed, which degrades the quality of the generated images. Anime-style images of 126 tags are collected from danbooru.donmai.us using the crawler tool... -
12
Video Pre-Training
Learning to Act by Watching Unlabeled Online Videos
The Video PreTraining (VPT) repository provides code and model artifacts for a project where agents learn to act by watching human gameplay videos—specifically, gameplay of Minecraft—using behavioral cloning. The idea is to learn general priors of control from large-scale, unlabeled video data, and then optionally fine-tune those priors for more goal-directed behavior via environment interaction. The repository contains demonstration models of different widths, fine-tuned variants (e.g. for... -
13
Reskin Sensor Library
ReSkin Sensor Interfacing Library
Soft sensors have continued growing interest in robotics, due to their ability to enable both passive conformal contact from the material properties and active contact data from the sensor properties. However, the same properties of conformal contact result in faster deterioration of soft sensors and larger variations in their response characteristics over time and across samples, inhibiting their ability to be long-lasting and replaceable. ReSkin is a tactile soft sensor that leverages... -
14
nlpaug
Data augmentation for NLP
This Python library helps you with augmenting nlp for your machine learning projects. Visit this introduction to understand Data Augmentation in NLP. Augmenter is the basic element of augmentation while Flow is a pipeline to orchestra multi augmenters together. -
15
Walrus
Lightweight Python utilities for working with Redis
The purpose of walrus is to make working with Redis in Python a little easier. Rather than ask you to learn a new library, walrus subclasses and extends the popular redis-py client, allowing it to be used as a drop-in replacement. In addition to all the features in redis-py, walrus adds support for some newer commands, including full support for streams and consumer groups. Persistent structures implemented on top of Hashes. Supports secondary indexes to allow filtering on equality,... -
16
Brain Tokyo Workshop
Experiments and code from Google Brain’s Tokyo research workshop
The Brain Tokyo Workshop repository hosts a collection of research materials and experimental code developed by the Google Brain team based in Tokyo. It showcases a variety of cutting-edge projects in artificial intelligence, particularly in the areas of neuroevolution, reinforcement learning, and model interpretability. Each project explores innovative approaches to learning, prediction, and creativity in neural networks, often through unconventional or biologically inspired methods. The... -
17
MuZero General
A commented and documented implementation of MuZero
muzero-general is an open-source implementation of the MuZero reinforcement learning algorithm introduced by DeepMind. MuZero is a model-based reinforcement learning method that combines neural networks with Monte Carlo Tree Search to learn decision-making policies without requiring explicit knowledge of the environment’s dynamics. The repository provides a well-documented and commented implementation designed primarily for educational purposes. It allows researchers and developers to train... -
18
RQ-Transformer
Implementation of RQ Transformer, autoregressive image generation
Implementation of RQ Transformer, which proposes a more efficient way of training multi-dimensional sequences autoregressively. This repository will only contain the transformer for now. You can use this vector quantization library for the residual VQ. This type of axial autoregressive transformer should be compatible with memcodes, proposed in NWT. It would likely also work well with multi-headed VQ. I also think there is something deeper going on, and have generalized this to any number of... -
19
SageMaker Scikit-Learn Extension
A library of additional estimators and SageMaker tools based on scikit
A library of additional estimators and SageMaker tools based on scikit-learn. This project contains standalone scikit-learn estimators and additional tools to support SageMaker Autopilot. Many of the additional estimators are based on existing scikit-learn estimators. SageMaker Scikit-Learn Extension is a Python module for machine learning built on top of scikit-learn. In order to use the I/O functionalies in the sagemaker_sklearn_extension.externals module, you will also need to install the mlio version 0.7 package via conda. ... -
20
Machine Learning PyTorch Scikit-Learn
Code Repository for Machine Learning with PyTorch and Scikit-Learn
Initially, this project started as the 4th edition of Python Machine Learning. However, after putting so much passion and hard work into the changes and new topics, we thought it deserved a new title. So, what’s new? There are many contents and additions, including the switch from TensorFlow to PyTorch, new chapters on graph neural networks and transformers, a new section on gradient boosting, and many more that I will detail in a separate blog post. For those who are interested in knowing... -
21
Deep learning time series forecasting
Deep learning PyTorch library for time series forecasting
Example image Flow Forecast (FF) is an open-source deep learning for time series forecasting framework. It provides all the latest state-of-the-art models (transformers, attention models, GRUs) and cutting-edge concepts with easy-to-understand interpretability metrics, cloud provider integration, and model serving capabilities. Flow Forecast was the first time series framework to feature support for transformer-based models and remains the only true end-to-end deep learning for time series... -
22
MAE (Masked Autoencoders)
PyTorch implementation of MAE
MAE (Masked Autoencoders) is a self-supervised learning framework for visual representation learning using masked image modeling. It trains a Vision Transformer (ViT) by randomly masking a high percentage of image patches (typically 75%) and reconstructing the missing content from the remaining visible patches. This forces the model to learn semantic structure and global context without supervision. The encoder processes only the visible patches, while a lightweight decoder reconstructs the... -
23
NWT - Pytorch (wip)
Implementation of NWT, audio-to-video generation, in Pytorch
Implementation of NWT, audio-to-video generation, in Pytorch. The paper proposes a new discrete latent representation named Memcodes, which can be succinctly described as a type of multi-head hard-attention to learned memory (codebook) key/values. They claim the need for less codes and smaller codebook dimensions in order to achieve better reconstructions. -
24
LayoutParser
A Unified Toolkit for Deep Learning Based Document Image Analysis
With the help of state-of-the-art deep learning models, Layout Parser enables extracting complicated document structures using only several lines of code. This method is also more robust and generalizable as no sophisticated rules are involved in this process. A complete instruction for installing the main Layout Parser library and auxiliary components. Learn how to load DL Layout models and use them for layout detection. The full list of layout models currently available in Layout Parser.... -
25
YOLOv3
Object detection architectures and models pretrained on the COCO data
Fast, precise and easy to train, YOLOv5 has a long and successful history of real time object detection. Treat YOLOv5 as a university where you'll feed your model information for it to learn from and grow into one integrated tool. You can get started with less than 6 lines of code. with YOLOv5 and its Pytorch implementation. Have a go using our API by uploading your own image and watch as YOLOv5 identifies objects using our pretrained models. Start training your model without being an...
