Search Results for "machine learning python" - Page 60
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Showing 1983 open source projects for "machine learning python"
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TF Quant Finance
High-performance TensorFlow library for quantitative finance
TF Quant Finance is a high-performance library of quantitative finance components built on TensorFlow, aimed at research and production workloads. It implements pricing engines, risk measures, stochastic models, optimizers, and random number generators that are differentiable and vectorized for accelerators. Users can value options and fixed-income instruments, simulate paths, fit curves, and calibrate models while leveraging TensorFlow’s jit compilation and automatic differentiation. The... -
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Rainbow
Rainbow: Combining Improvements in Deep Reinforcement Learning
Combining improvements in deep reinforcement learning. Results and pretrained models can be found in the releases. Data-efficient Rainbow can be run using several options (note that the "unbounded" memory is implemented here in practice by manually setting the memory capacity to be the same as the maximum number of timesteps). -
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CCZero (中国象棋Zero)
Implement AlphaZero/AlphaGo Zero methods on Chinese chess
ChineseChess-AlphaZero is a project that implements the AlphaZero algorithm for the game of Chinese Chess (Xiangqi). It adapts DeepMind’s AlphaZero method—combining neural networks and Monte Carlo Tree Search (MCTS)—to learn and play Chinese Chess without prior human data. The system includes self-play, training, and evaluation pipelines tailored to Xiangqi's unique game mechanics. -
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pytorch-examples
Simple examples to introduce PyTorch
The pytorch-examples project is a collection of concise and practical examples demonstrating how to use PyTorch for machine learning and deep learning tasks. It focuses on clarity and minimalism, providing small, self-contained scripts that illustrate key concepts such as neural network training, optimization, and data handling. The examples cover a range of topics including supervised learning, generative models, and reinforcement learning, making it a valuable resource for both beginners and experienced practitioners. ... -
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I3D models trained on Kinetics
Convolutional neural network model for video classification
Kinetics-I3D, developed by Google DeepMind, provides trained models and implementation code for the Inflated 3D ConvNet (I3D) architecture introduced in the paper “Quo Vadis, Action Recognition? A New Model and the Kinetics Dataset” (CVPR 2017). The I3D model extends the 2D convolutional structure of Inception-v1 into 3D, allowing it to capture spatial and temporal information from videos for action recognition. This repository includes pretrained I3D models on the Kinetics dataset, with... -
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RoboSat
Semantic segmentation on aerial and satellite imagery
RoboSat is an end-to-end pipeline written in Python 3 for feature extraction from aerial and satellite imagery. Features can be anything visually distinguishable in the imagery for example: buildings, parking lots, roads, or cars. -
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Project Malmo
A platform for Artificial Intelligence experimentation on Minecraft
How can we develop artificial intelligence that learns to make sense of complex environments? That learns from others, including humans, how to interact with the world? That learns transferable skills throughout its existence, and applies them to solve new, challenging problems? Project Malmo sets out to address these core research challenges, addressing them by integrating (deep) reinforcement learning, cognitive science, and many ideas from artificial intelligence. The Malmo platform... -
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Django Celery
Old Celery integration project for Django
Celery is a simple, flexible, and reliable distributed system to process vast amounts of messages, while providing operations with the tools required to maintain such a system. It’s a task queue with focus on real-time processing, while also supporting task scheduling. Celery has a large and diverse community of users and contributors, you should come join us on IRC or our mailing-list. Celery is Open Source and licensed under the BSD License. A task queue’s input is a unit of work called a... -
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InferSent
InferSent sentence embeddings
InferSent is a supervised sentence embedding method that learns universal representations from Natural Language Inference data and transfers well to many downstream tasks. It uses a BiLSTM encoder with max-pooling to produce fixed-length sentence vectors that capture semantics beyond bag-of-words statistics. Trained on large NLI datasets, the embeddings generalize across tasks like sentiment analysis, entailment, paraphrase detection, and semantic similarity with simple linear classifiers.... -
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captcha_break
Identification codes
This project will use Keras to build a deep convolutional neural network to identify the captcha verification code. It is recommended to use a graphics card to run the project. The following visualization codes are jupyter notebookall done in . If you want to write a python script, you can run it normally with a little modification. Of course, you can also remove these visualization codes. captcha is a library written in python to generate verification codes. It supports image verification... -
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ChainerCV
ChainerCV: a Library for Deep Learning in Computer Vision
ChainerCV is a collection of tools to train and run neural networks for computer vision tasks using Chainer. In ChainerCV, we define the object detection task as a problem of, given an image, bounding box-based localization and categorization of objects. Bounding boxes in an image are represented as a two-dimensional array of shape (R,4), where R is the number of bounding boxes and the second axis corresponds to the coordinates of bounding boxes. ChainerCV supports dataset loaders, which can... -
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The Google Cloud Developer's Cheat Sheet
Cheat sheet for Google Cloud developers
Every product in the Google Cloud family described in <=4 words (with liberal use of hyphens and slashes) by the Google Developer Relations Team. This list only includes products that are publicly available. There are several products in pre-release/private-alpha that will not be included until they go public beta or GA. Many of these products have a free tier. There is also a free trial that will enable you try almost everything. API platforms and ecosystems, developer and management tools,... -
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FID score for PyTorch
Compute FID scores with PyTorch
This is a port of the official implementation of Fréchet Inception Distance to PyTorch. FID is a measure of similarity between two datasets of images. It was shown to correlate well with human judgement of visual quality and is most often used to evaluate the quality of samples of Generative Adversarial Networks. FID is calculated by computing the Fréchet distance between two Gaussians fitted to feature representations of the Inception network. The weights and the model are exactly the same... -
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CakeChat
CakeChat: Emotional Generative Dialog System
CakeChat is a backend for chatbots that are able to express emotions via conversations. The code is flexible and allows to condition model's responses by an arbitrary categorical variable. For example, you can train your own persona-based neural conversational model or create an emotional chatting machine. Hierarchical Recurrent Encoder-Decoder (HRED) architecture for handling deep dialog context. Multilayer RNN with GRU cells. The first layer of the utterance-level encoder is always... -
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vid2vid
Pytorch implementation of our method for high-resolution
vid2vid is a deep learning framework for high-resolution video-to-video translation that generates photorealistic videos from structured inputs such as semantic maps, pose sequences, or edge maps. Built on top of image-to-image translation techniques like pix2pixHD, it extends these ideas into the temporal domain by ensuring consistency across video frames. The system can synthesize complex outputs such as realistic talking faces, human motion animations, or dynamic street scenes by learning... -
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easy12306
Automatic recognition of 12306 verification code
Automatic recognition of 12306 verification code using machine learning algorithm. Identify never-before-seen pictures. -
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UnsupervisedMT
Phrase-Based & Neural Unsupervised Machine Translation
Unsupervised Machine Translation is a research repository that implements both phrase-based SMT and neural MT approaches for translation without parallel corpora. The neural component supports multiple architectures—seq2seq, biLSTM with attention, and Transformer—and allows extensive parameter sharing across languages to improve data efficiency. Training relies on denoising auto-encoding and back-translation, with on-the-fly, multithreaded generation of synthetic parallel data to continually... -
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MAML-Pytorch
Elegant PyTorch implementation of paper Model-Agnostic Meta-Learning
MAML-Pytorch is a PyTorch implementation of Model-Agnostic Meta-Learning for supervised learning experiments. It focuses on reproducing and exploring the MAML approach for few-shot learning research. The repository supports MiniImagenet and Omniglot, two common benchmark datasets for meta-learning experiments. It includes separate training scripts, dataset loaders, learner components, and meta-learning logic. The project also notes that MAML can be difficult to train and presents the... -
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automl-gs
Provide an input CSV and a target field to predict, generate a model
Give an input CSV file and a target field you want to predict to automl-gs, and get a trained high-performing machine learning or deep learning model plus native Python code pipelines allowing you to integrate that model into any prediction workflow. No black box: you can see exactly how the data is processed, and how the model is constructed, and you can make tweaks as necessary. automl-gs is an AutoML tool which, unlike Microsoft's NNI, Uber's Ludwig, and TPOT, offers a zero code/model definition interface to getting an optimized model and data transformation pipeline in multiple popular ML/DL frameworks, with minimal Python dependencies (pandas + scikit-learn + your framework of choice). automl-gs is designed for citizen data scientists and engineers without a deep statistical background under the philosophy that you don't need to know any modern data preprocessing and machine learning engineering techniques to create a powerful prediction workflow. -
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PivotSuite
Network Pivoting Toolkit
PivotSuite is a portable, platform-independent and powerful network pivoting toolkit, Which helps Red Teamers / Penetration Testers to use a compromised system to move around inside a network. It is a Standalone Utility, Which can use as a Server or as a Client. If the compromised host is directly accessible (Forward Connection) from Our pentest machine, Then we can run pivotsuite as a server on the compromised machine and access the different subnet hosts from our pentest machine, Which was... -
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AeroPython
Classical Aerodynamics of potential flow using Python
The AeroPython series of lessons is the core of a university course (Aerodynamics-Hydrodynamics, MAE-6226) by Prof. Lorena A. Barba at the George Washington University. The first version ran in Spring 2014 and these Jupyter Notebooks were prepared for that class, with assistance from Barba-group PhD student Olivier Mesnard. In Spring 2015, we revised and extended the collection, adding student assignments to strengthen the learning experience. The course is also supported by an open learning... -
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Platformer 2D Godot Game
Test project with a 2D platform game developing in Godot 3.1
Test project with a 2D platform game developing in Godot 3.1, reusable mechanics for: State Machine, basics AI, Android Games. -
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NeuroNER
Named-entity recognition using neural networks
...Identified entities can be used in various downstream applications such as patient note de-identification and information extraction systems. They can also be used as features for machine learning systems for other natural language processing tasks. Leverages the state-of-the-art prediction capabilities of neural networks (a.k.a. "deep learning") Is cross-platform, open source, freely available, and straightforward to use. Enables the users to create or modify annotations for a new or existing corpus. Train the neural network that performs the NER. ... -
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MUSE
A library for Multilingual Unsupervised or Supervised word Embeddings
...The training and evaluation pipeline is lightweight and fast, so experimenting with different languages or initialization strategies is easy. Beyond dictionary induction, the learned embeddings are often used as building blocks for downstream tasks like classification, retrieval, or machine translation.
