Search Results for "machine learning python" - Page 63
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Showing 1983 open source projects for "machine learning python"
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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.
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Build Agents and Models on One PlatformGemini Enterprise Agent Platform is Google Cloud's comprehensive platform for developers to build, scale, govern, and optimize agents and models. Choose from Google's most advanced models and third-party models like Anthropic's Claude Model Family.
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AngelReader
An E-book, Audio-book, & Library Loader in One Application
AngelReader: A minimalist but powerful GUI application that has the capacity to load [1] E-books in plain text format with the least use of both software and hardware resources. It can also load [2] Audio-books with the basic functions of play, stop, pause, and resume with the same minimalist economy that doesn't hog computer resources. When used in integration with the AngelReader Library Selector, it can function as a mini library management system for books in electronic formats. It's in... -
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DeepLearn
Implementation of research papers on Deep Learning+ NLP+ CV in Python
Welcome to DeepLearn. This repository contains an implementation of the following research papers on NLP, CV, ML, and deep learning. The required dependencies are mentioned in requirement.txt. I will also use dl-text modules for preparing the datasets. If you haven't use it, please do have a quick look at it. CV, transfer learning, representation learning. -
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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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SSD Keras
A Keras port of single shot MultiBox detector
This is a Keras port of the SSD model architecture introduced by Wei Liu et al. in the paper SSD: Single Shot MultiBox Detector. Ports of the trained weights of all the original models are provided below. This implementation is accurate, meaning that both the ported weights and models trained from scratch produce the same mAP values as the respective models of the original Caffe implementation. The main goal of this project is to create an SSD implementation that is well documented for those... -
Cut Data Warehouse Costs by 54%BigQuery delivers 54% lower TCO with exabyte scale and flexible pricing. Free migration tools handle the SQL translation automatically.
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stanford-tensorflow-tutorials
This repository contains code examples for the Stanford's course
This repository contains code examples for the course CS 20: TensorFlow for Deep Learning Research. It will be updated as the class progresses. Detailed syllabus and lecture notes can be found in the site. For this course, I use python3.6 and TensorFlow 1.4.1. -
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Face Recognition
World's simplest facial recognition api for Python & the command line
Face Recognition is the world's simplest face recognition library. It allows you to recognize and manipulate faces from Python or from the command line using dlib's (a C++ toolkit containing machine learning algorithms and tools) state-of-the-art face recognition built with deep learning. Face Recognition is highly accurate and is able to do a number of things. It can find faces in pictures, manipulate facial features in pictures, identify faces in pictures, and do face recognition on a folder of images from the command line. ... -
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Learn Python the Hard Way
Concise study notes derived from “Learn Python the Hard Way”
This repository contains concise study notes derived from “Learn Python the Hard Way,” organized to reinforce core Python concepts through small, targeted examples. It emphasizes hands-on practice—short scripts, exercises, and explanations that help cement syntax, data structures, functions, and modules. The notes call out common gotchas, idioms, and style preferences so learners form good habits early. Because the content is intentionally compact, it’s easy to revisit a topic quickly when... -
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A project for a highly scalable protocol and framework and a collection of software tools for m2m (machine to machine communication) and IoT Internet of Things. The programs here works on Windows and Linux and are based around VSCP, The Very Simple Control Protocol. Repository is here: https://github.com/grodansparadis/vscp_software and here https://github.com/grodansparadis/vscp_firmware
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DIGITS
Deep Learning GPU training system
The NVIDIA Deep Learning GPU Training System (DIGITS) puts the power of deep learning into the hands of engineers and data scientists. DIGITS can be used to rapidly train the highly accurate deep neural network (DNNs) for image classification, segmentation and object detection tasks. DIGITS simplifies common deep learning tasks such as managing data, designing and training neural networks on multi-GPU systems, monitoring performance in real-time with advanced visualizations, and selecting... -
Save Up to 91% on Cloud Compute With Spot VMsRun batch jobs at 60-91% off with Spot VMs. Long-running workloads get automatic discounts with sustained use.
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ECDICT
Free English to Chinese Dictionary Database
ECDICT is a comprehensive English–Chinese dictionary dataset packaged for developers who need an offline, queryable lexicon for applications, NLP, or educational tools. It aggregates headwords, phonetics, parts of speech, translations, and example information into formats that are easy to integrate. The project provides multiple distribution forms—commonly SQLite/CSV/StarDict-style files—so you can choose the right storage and query approach for your app. Because it’s offline and local, it... -
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mAP
Evaluates the performance of your neural net for object recognition
In practice, a higher mAP value indicates a better performance of your neural net, given your ground truth and set of classes. The performance of your neural net will be judged using the mAP criteria defined in the PASCAL VOC 2012 competition. We simply adapted the official Matlab code into Python (in our tests they both give the same results). First, your neural net detection-results are sorted by decreasing confidence and are assigned to ground-truth objects. We have "a match" when they... -
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The Deep Review
A collaboratively written review paper on deep learning, genomics, etc
This repository is home to the Deep Review, a review article on deep learning in precision medicine. The Deep Review is collaboratively written on GitHub using a tool called Manubot (see below). The project operates on an open contribution model, welcoming contributions from anyone. To see what's incoming, check the open pull requests. For project discussion and planning see the Issues. As of writing, we are aiming to publish an update of the deep review. We will continue to make project... -
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EvalAI
Evaluating state of the art in AI
EvalAI is an open-source platform for evaluating and comparing machine learning (ML) and artificial intelligence (AI) algorithms at scale. We allow the creation of an arbitrary number of evaluation phases and dataset splits, compatibility using any programming language, and organizing results in both public and private leaderboards. Certain large-scale challenges need special computing capabilities for evaluation. -
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Vaex
Out-of-Core hybrid Apache Arrow/NumPy DataFrame for Python
Data science solutions, insights, dashboards, machine learning, deployment. We start at 100GB. Vaex is a high-performance Python library for lazy Out-of-Core data frames (similar to Pandas), to visualize and explore big tabular datasets. It calculates statistics such as mean, sum, count, standard deviation etc, on an N-dimensional grid for more than a billion (10^9) samples/rows per second. -
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Zhao
A compilation of "The Princely Party Relationship Network"
zhao is a repository that consolidates research, data, and insights related to Zhao, which is likely an individual’s research collection, notes, or curated resources on deep learning, AI, or computational topics (name and content context suggest specialized study). The project may include code examples, experiment results, references to academic papers, mathematical notes, and supporting scripts to explore specific ML methods, benchmarks, or theoretical findings. Because it aggregates... -
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Serenata de Amor
Artificial Intelligence for social control of public administration
...It focuses on analyzing publicly available datasets related to reimbursements claimed by Brazilian congress members in order to detect suspicious or irregular expenses. Machine learning techniques and data analysis pipelines are used to identify anomalies that may indicate misuse of public funds. The system also includes automated tools that assist in processing large datasets and generating reports about potentially problematic transactions. By making both the data and the analysis tools open source, the project encourages civic participation and collaborative oversight of government activities. -
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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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Image classification models for Keras
Keras code and weights files for popular deep learning models
All architectures are compatible with both TensorFlow and Theano, and upon instantiation the models will be built according to the image dimension ordering set in your Keras configuration file at ~/.keras/keras.json. For instance, if you have set image_dim_ordering=tf, then any model loaded from this repository will get built according to the TensorFlow dimension ordering convention, "Width-Height-Depth". Pre-trained weights can be automatically loaded upon instantiation (weights='imagenet'... -
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Universe Starter Agent
A starter agent that can solve a number of universe environments
The universe-starter-agent repository is an archived OpenAI codebase designed as a starter reinforcement-learning agent that can interact with and solve tasks in OpenAI’s Universe environment platform. Its purpose is to serve as a baseline or reference implementation so researchers or developers can see how to build agents that operate in real-time, visual environments (e.g., games, browser apps) via pixel observations and keyboard/mouse actions. Under the hood, this starter agent implements... -
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PyTorch Book
PyTorch tutorials and fun projects including neural talk
This is the corresponding code for the book "The Deep Learning Framework PyTorch: Getting Started and Practical", but it can also be used as a standalone PyTorch Getting Started Guide and Tutorial. The current version of the code is based on pytorch 1.0.1, if you want to use an older version please git checkout v0.4or git checkout v0.3. Legacy code has better python2/python3 compatibility, CPU/GPU compatibility test. The new version of the code has not been fully tested, it has been tested... -
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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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tacotron
A TensorFlow Implementation of Tacotron
Tacotron is a heavily documented TensorFlow implementation of the end-to-end text-to-speech architecture introduced in the original Tacotron paper. It converts text into speech by learning acoustic representations and attention-based alignments from paired text and audio. The repository includes preprocessing, model modules, training, evaluation, and synthesis scripts. Example training setups use LJ Speech, Nick Offerman audiobook recordings, and the World English Bible dataset. Users can... -
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Intel neon
Intel® Nervana™ reference deep learning framework
neon is Intel's reference deep learning framework committed to best performance on all hardware. Designed for ease of use and extensibility. See the new features in our latest release. We want to highlight that neon v2.0.0+ has been optimized for much better performance on CPUs by enabling Intel Math Kernel Library (MKL). The DNN (Deep Neural Networks) component of MKL that is used by neon is provided free of charge and downloaded automatically as part of the neon installation. The gpu... -
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AI learning
AiLearning, data analysis plus machine learning practice
We actively respond to the Research Open Source Initiative (DOCX) . Open source today is not just open source, but datasets, models, tutorials, and experimental records. We are also exploring other categories of open source solutions and protocols. I hope you will understand this initiative, combine this initiative with your own interests, and do what you can. Everyone's tiny contributions, together, are the entire open source ecosystem. We are iBooker, a large open-source community,... -
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WireFactory Simulator
Small factory simulator for control logic test purpose
Wire Factory Simulator (WFS) is a free software to simulate the control of a factory, mainly controlled by IEC 61499 applications. You can build your own factory and controller. The controller can be implemented on any programming language. WFS was originally built to simulate the action of an IEC 614999 application. So, the devices in the factory are easily connected to IEC 61499 applications. The WFS were mainly tested with FBDK (http://www.holobloc.com/). Wire Factory has been...
