Search Results for "python-gtksourceview2" - Page 43
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1220 projects for "python-gtksourceview2" with 2 filters applied:
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OpenSeq2Seq
Toolkit for efficient experimentation with Speech Recognition
OpenSeq2Seq is a TensorFlow-based toolkit for efficient experimentation with sequence-to-sequence models across speech and NLP tasks. Its core goal is to give researchers a flexible, modular framework for building and training encoder–decoder architectures while fully leveraging distributed and mixed-precision training. The toolkit includes ready-made models for neural machine translation, automatic speech recognition, speech synthesis, language modeling, and additional NLP tasks such as... -
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Generative Models
Collection of generative models, e.g. GAN, VAE in Pytorch
This project is a comprehensive open-source collection of implementations of various generative machine learning models designed to help researchers and developers experiment with deep generative techniques. The repository contains practical implementations of well-known architectures such as Generative Adversarial Networks (GANs), Variational Autoencoders (VAEs), Restricted Boltzmann Machines, and Helmholtz Machines, implemented primarily using modern deep learning frameworks like PyTorch... -
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LearningToCompare_FSL
Learning to Compare: Relation Network for Few-Shot Learning
LearningToCompare_FSL is a PyTorch implementation of the “Learning to Compare: Relation Network for Few-Shot Learning” paper, focusing on the few-shot learning experiments described in that work. The core idea implemented here is the relation network, which learns to compare pairs of feature embeddings and output relation scores that indicate whether two images belong to the same class, enabling classification from only a handful of labeled examples. The repository provides training and... -
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Deep Reinforcement Learning TensorFlow
TensorFlow implementation of Deep Reinforcement Learning papers
Deep Reinforcement Learning TensorFlow is a comprehensive TensorFlow codebase that implements several foundational deep reinforcement learning algorithms for educational and experimental use. The repository focuses on clarity and modularity so users can study how different RL approaches are built and compare their behavior across environments. It includes implementations of well-known algorithms such as Deep Q-Networks (DQN), policy gradients, and related variants, demonstrating how neural... -
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When to use TensorFlowSharp
TensorFlow API for .NET languages
...Its primary purpose is to enable developers working in Microsoft-based environments to load trained TensorFlow models and perform inference or additional training within .NET applications. The library focuses mainly on providing access to the low-level TensorFlow runtime rather than offering the high-level abstractions commonly available in Python libraries like Keras. This design allows applications written in C# or F# to execute machine learning graphs produced by Python workflows while maintaining compatibility with the TensorFlow runtime. -
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DC-TTS
TensorFlow Implementation of DC-TTS: yet another text-to-speech model
DC-TTS is a TensorFlow implementation of the DC-TTS architecture, a fully convolutional text-to-speech system designed to be efficiently trainable while producing natural speech. It follows the “Efficiently Trainable Text-to-Speech System Based on Deep Convolutional Networks with Guided Attention” paper, but the author adapts and extends the design to make it practical for real experiments. The model is split into two networks: Text2Mel, which maps text to mel-spectrograms, and SSRN... -
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Siamese and triplet learning
Siamese and triplet networks with online triplet mining in PyTorch
Siamese and triplet learning is a PyTorch implementation of Siamese and triplet neural network architectures designed for learning embedding representations in machine learning tasks. These types of networks learn to map images into a compact feature space where the distance between vectors reflects the similarity between inputs. Such embeddings are commonly used in applications like face recognition, image similarity search, and few-shot learning. The repository demonstrates how to train... -
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FastPhotoStyle
Style transfer, deep learning, feature transform
FastPhotoStyle is a deep learning-based image stylization framework designed to transfer the style of one photograph onto another while preserving photorealistic quality. Unlike traditional artistic style transfer methods that produce painterly outputs, this approach focuses on maintaining realistic textures, lighting, and spatial consistency. The method is based on a two-step process that includes a stylization phase followed by a smoothing operation, ensuring that the output image remains... -
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Serenata de Amor
Artificial Intelligence for social control of public administration
Serenata de Amor is an open civic technology project that uses data science and artificial intelligence to promote transparency and accountability in public administration. The project was developed by a community of volunteers associated with Open Knowledge Brasil who believe that open data and technology can help citizens monitor government spending. It focuses on analyzing publicly available datasets related to reimbursements claimed by Brazilian congress members in order to detect... -
Custom VMs From 1 to 96 vCPUs With 99.95% UptimeLive migration and automatic failover keep workloads online through maintenance. One free e2-micro VM every month.
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Command Line Parser GetPot
Tool to parse the command line and configuration files.
Powerful command line and configuration file parsing for C++, Python, Ruby and Java (others to come). This tool provides many features, such as separate treatment for options, variables, and flags, unrecognized object detection, prefixes and much more. -
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Training Image Operators from Samples
Tools to train Image Operators automatically from a set of samples.
TRIOS - Training Image Operators from Samples is a set of tools to bring Image Processing closer to scientists in general. It is capable of estimating an operator between two images using only pairs of samples that contain an input image and the desired output. The operator is saved to a file and can be applied to any image. -
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R-FCN
R-FCN: Object Detection via Region-based Fully Convolutional Networks
R-FCN (“Region-based Fully Convolutional Networks”) is an object detection framework that makes almost all computation fully convolutional and shared across the image, unlike prior region-based approaches (e.g. Faster R-CNN) which run per-region sub-networks. The repository provides an implementation (in Python) supporting end-to-end training and inference of R-FCN models on standard datasets. The authors propose position-sensitive score maps to reconcile the need for translation variance (in detection) and translation invariance (in classification). R-FCN is efficient (low per-region overhead) and competitive in accuracy (e.g. with ResNet backbones). ... -
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bulbea
Deep Learning based Python Library for Stock Market Prediction
bulbea is an open-source Python library designed for financial analysis and stock market prediction using machine learning and deep learning techniques. The library provides tools for retrieving financial time series data, preprocessing market data, and training predictive models that estimate future price movements. bulbea integrates common machine learning frameworks such as TensorFlow and Keras to build neural network models capable of learning patterns in historical financial data. ... -
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Neural Photo Editor
A simple interface for editing natural photos
Neural Photo Editor is an experimental machine learning application that demonstrates how generative neural networks can be used as an interactive photo editing tool. The project implements the system described in the research paper Neural Photo Editing with Introspective Adversarial Networks, which introduces a generative model capable of modifying images in semantically meaningful ways. Instead of editing images by directly manipulating pixels, the software allows users to influence... -
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PyDaMelo
Python-compatible Data mining elementary objects
An attempt at offering machine learning and data mining algorithms at the finest grain we are able to, easy to combine together through Python scripting to glue together the Lego-like bricks. -
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H2O-3
H2O is an Open Source, Distributed, Fast & Scalable Machine Learning
...It supports many machine learning algorithms including generalized linear models, gradient boosting machines, deep learning networks, and ensemble techniques. The platform provides interfaces for multiple programming languages such as Python, R, Java, and Scala, making it accessible to a wide range of developers and data scientists. H2O-3 integrates with big data technologies such as Hadoop and Apache Spark, enabling organizations to run machine learning workflows on large-scale data infrastructure. The platform also includes a web-based interface called Flow that allows users to build models interactively through notebooks and visual tools. -
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Retrieval-Based Conversational Model
Dual LSTM Encoder for Dialog Response Generation
Retrieval-Based Conversational Model in Tensorflow is a project implementing a retrieval-based conversational model using a dual LSTM encoder architecture in TensorFlow, illustrating how neural networks can be trained to select appropriate responses from a fixed set of candidate replies rather than generate them from scratch. The core idea is to embed both the conversation context and potential replies into vector representations, then score how well each candidate fits the current dialogue,... -
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GT NLP Class
Course materials for Georgia Tech CS 4650 and 7650
...Students work through programming exercises and problem sets that build intuition for both classical algorithms (like HMMs and CRFs) and neural approaches (like word embeddings and sequence models). The materials emphasize theory grounded in practical experimentation, often via Python notebooks or scripts that visualize results and encourage ablation studies. Clear organization and self-contained examples make it possible to follow along outside the classroom, using the repo as a self-study resource. For learners and instructors alike, the course provides a coherent path from foundational linguistics to current techniques, with reproducible code that makes concepts concrete. -
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SLEF
Subjective Logic Experimental Framework
Experiment developed for the paper Subjective Logic Operators in Trust Assessment: An Empirical Study by F. Cerutti, L. M. Kaplan, T. J. Norman, N. Oren, and A. Toniolo ISF Journal, 2014 -
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Spark Python Notebooks
Apache Spark & Python (pySpark) tutorials for Big Data Analysis
Spark Python Notebooks is a curated collection of example Jupyter notebooks designed to help developers and data engineers learn Apache Spark using Python in an interactive environment. Rather than only providing static code files, this project uses notebooks to teach practical data processing workflows, exposing users to real Spark programming patterns like working with RDDs, DataFrames, and distributed computations. -
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ffnet is a fast and easy-to-use feed-forward neural network training solution for python. Many nice features are implemented: arbitrary network connectivity, automatic data normalization, very efficient training tools, network export to fortran code. Now ffnet has also a GUI called ffnetui.
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ExSTraCS
Extended Supervised Tracking and Classifying System
...It can flexibly handle (1) discrete or continuous attributes, (2) missing data, (3) balanced or imbalanced datasets, and (4) binary or many classes. A complete users guide for ExSTraCS is included. Coded in Python 2.7. -
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The General Hidden Markov Model Library (GHMM) is a C library with additional Python bindings implementing a wide range of types of Hidden Markov Models and algorithms: discrete, continous emissions, basic training, HMM clustering, HMM mixtures.
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IMAGINE
Biological image viewer and processor
Detection, enumeration, and sizing of biological organisms by image analysis.