Search Results for "code-server" - Page 14
Sort By:
Showing 396 open source projects for "code-server"
View related business solutions-
$300 Free Credits for Your Google Cloud ProjectsLaunch your next project with $300 in free Google Cloud credits—no strings attached. Test, build, and deploy without risk. Use your credits across the entire Google Cloud platform to find what works best for your needs. After your credits are used, continue with always-free tier services. Only pay when you're ready to scale. Sign up in minutes and start exploring.
-
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.
-
1
Image classification models for Keras
Keras code and weights files for popular deep learning models
...Pre-trained weights can be automatically loaded upon instantiation (weights='imagenet' argument in model constructor for all image models, weights='msd' for the music tagging model). Weights are automatically downloaded if necessary, and cached locally in ~/.keras/models/. This repository contains code for the following Keras models, VGG16, VGG19, ResNet50, Inception v3, and CRNN for music tagging. -
2
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. -
3
...Obtaining the teachingbox: FOR USERS: If you want to download the latest releases, please visit: http://search.maven.org/#search|ga|1|teachingbox FOR DEVELOPERS: 1) If you use Apache Maven, just add the following dependency to your pom.xml: <dependency> <groupId>org.sf.teachingbox</groupId> <artifactId>teachingbox-core</artifactId> <version>1.2.3</version> </dependency> 2) If you want to check out the most recent source-code: git clone https://git.code.sf.net/p/teachingbox/core teachingbox-core Documentation: https://sourceforge.net/p/teachingbox/documentation/HEAD/tree/trunk/manual/
-
4
Seldon Server
Machine learning platform and recommendation engine on Kubernetes
...Seldon Core is a progression of the goals of the Seldon-Server project but also a more restricted focus to solving the final step in a machine learning project which is serving models in production. Seldon Server is a machine learning platform that helps your data science team deploy models into production. It provides an open-source data science stack that runs within a Kubernetes Cluster. -
Host LLMs in Production With On-Demand GPUsDeploy your model, get an endpoint, pay only for compute time. No GPU provisioning or infrastructure management required.
-
5
kcws
Deep Learning Chinese Word Segment
...Change to the code directory.After installing tensorflow, switch to the kcws code directory. Currently, the custom dictionary is supported in the decoding stage. Please refer to kcws/cc/test_seg.cc for specific usage. The dictionary is in text format. -
6
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... -
7
Tangent
Source-to-source debuggable derivatives in pure Python
Existing libraries implement automatic differentiation by tracing a program's execution (at runtime, like PyTorch) or by staging out a dynamic data-flow graph and then differentiating the graph (ahead-of-time, like TensorFlow). In contrast, Tangent performs ahead-of-time autodiff on the Python source code itself, and produces Python source code as its output. Tangent fills a unique location in the space of machine learning tools. As a result, you can finally read your automatic derivative code just like the rest of your program. Tangent is useful to researchers and students who not only want to write their models in Python, but also read and debug automatically-generated derivative code without sacrificing speed and flexibility. ... -
8
Five video classification methods
Code that accompanies my blog post outlining five video classification
Classifying video presents unique challenges for machine learning models. As I’ve covered in my previous posts, video has the added (and interesting) property of temporal features in addition to the spatial features present in 2D images. While this additional information provides us more to work with, it also requires different network architectures and, often, adds larger memory and computational demands.We won’t use any optical flow images. This reduces model complexity, training time, and... -
9
EasyPR
An easy, flexible, and accurate plate recognition project
EasyPR is an open-source license plate recognition system designed to detect and recognize vehicle license plates from images using computer vision and machine learning techniques. The project focuses primarily on recognizing Chinese license plates but also demonstrates general approaches to automatic number plate recognition systems. Built on top of the OpenCV computer vision library, EasyPR provides algorithms for detecting license plate regions in images, segmenting characters, and... -
Build Securely on AWS with Proven FrameworksMoving to the cloud brings new challenges. How can you manage a larger attack surface while ensuring great network performance? Turn to Fortinet’s Tested Reference Architectures, blueprints for designing and securing cloud environments built by cybersecurity experts. Learn more and explore use cases in this white paper.
-
10
UnrealCV
Connecting Computer Vision to Unreal Engine
UnrealCV is a project to help computer vision researchers build virtual worlds using Unreal Engine (UE). It extends UE with a plugin. UnrealCV can be used in two ways. The first one is using a compiled game binary with UnrealCV embedded. This is as simple as running a game, no knowledge of Unreal Engine is required. The second is installing the UnrealCV plugin into Unreal Engine and using the editor to build a new virtual world. -
11
auto_ml
Automated machine learning for analytics & production
...Here's an example that includes serializing and loading the trained model, then getting predictions on single dictionaries, roughly the process you'd likely follow to deploy the trained model. Before you go any further, try running the code. Load up some data (either a DataFrame, or a list of dictionaries, where each dictionary is a row of data). Make a column_descriptions dictionary that tells us which attribute name in each row represents the value we’re trying to predict. Pass all that into auto_ml, and see what happens! You can pass in your own function to perform feature engineering on the data. ... -
12
Chronological Cohesive Units
The experimental source code for the paper
The experimental source code for the paper, "A Novel Recommendation Approach Based on Chronological Cohesive Units in Content Consuming" -
13
Lip Reading
Cross Audio-Visual Recognition using 3D Architectures
The input pipeline must be prepared by the users. This code is aimed to provide the implementation for Coupled 3D Convolutional Neural Networks for audio-visual matching. Lip-reading can be a specific application for this work. Audio-visual recognition (AVR) has been considered as a solution for speech recognition tasks when the audio is corrupted, as well as a visual recognition method used for speaker verification in multi-speaker scenarios. -
14
Computational Linear Algebra for Coders
Free online textbook of Jupyter notebooks
...The project presents linear algebra concepts from a practical perspective focused on how computers perform matrix operations efficiently and accurately. The course materials are organized as Jupyter notebooks that combine explanations, code demonstrations, and exercises. Instead of emphasizing purely theoretical mathematics, the project takes a programming-oriented approach that helps developers understand how linear algebra algorithms are implemented in real computational systems. The course explores topics such as matrix decomposition, numerical stability, and optimization techniques that are essential for machine learning and data science applications. -
15
DMTK
Microsoft Distributed Machine Learning Toolkit
...Developed by Microsoft Research, the toolkit provides infrastructure and algorithms designed to train large models efficiently on clusters of machines rather than a single system. At its core is a parameter-server architecture called Multiverso, which manages model parameters and synchronizes updates across distributed training processes. This architecture allows developers to build machine learning systems capable of processing massive datasets and training complex models with reduced infrastructure requirements. DMTK also includes several specialized algorithms and systems, such as LightLDA for large-scale topic modeling and distributed implementations of word embedding techniques used in natural language processing. -
16
Machine Learning for OpenCV
M. Beyeler (2017). Machine Learning for OpenCV
M. Beyeler (2017). Machine Learning for OpenCV: Intelligent image processing with Python. Packt Publishing Ltd., ISBN 978-178398028-4. -
17
Deep Photo Style Transfer
Code and data for paper "Deep Photo Style Transfer"
...It relies on semantic segmentation masks to guide style transfer (so that e.g. sky maps to sky, building maps to building), and uses a matting Laplacian regularization term to ensure smooth transitions. The repository provides code in Torch (Lua), MATLAB / Octave scripts for computing the Laplacian, and pre-trained models. Pretrained models and example scripts for ease of use. Compatibility with MATLAB / Octave for Laplacian computations. -
18
Accord.NET Framework
Scientific computing, machine learning and computer vision for .NET
The Accord.NET Framework provides machine learning, mathematics, statistics, computer vision, computer audition, and several scientific computing related methods and techniques to .NET. The project is compatible with the .NET Framework. NET Standard, .NET Core, and Mono. -
19
Caffe
A fast open framework for deep learning
Caffe is an open source deep learning framework that’s focused on expression, speed and modularity. It’s got an expressive architecture that encourages application and innovation, and extensible code that’s great for active development. Caffe also offers great speed, capable of processing over 60M images per day with a single NVIDIA K40 GPU. It’s arguably one of the fastest convnet implementations around. Caffe is developed by the Berkeley AI Research (BAIR)/The Berkeley Vision and Learning Center (BVLC) and a great community of contributors that continue to make Caffe state-of-the-art in both code and models. ... -
20
Grenade
Deep Learning in Haskell
Grenade is a composable, dependently typed, practical, and fast recurrent neural network library for concise and precise specifications of complex networks in Haskell. Because the types are so rich, there's no specific term level code required to construct this network; although it is of course possible and easy to construct and deconstruct the networks and layers explicitly oneself. Networks in Grenade can be thought of as a heterogeneous list of layers, where their type includes not only the layers of the network but also the shapes of data that are passed between the layers. ... -
21
TEES
Turku Event Extraction System
Turku Event Extraction System (TEES) is a free and open source natural language processing system developed for the extraction of events and relations from biomedical text. It is written mostly in Python, and should work in generic Unix/Linux environments. Currently, the TEES source code repository still remains on GitHub at http://jbjorne.github.com/TEES/ where there is also a wiki with more information. -
22
Speakable Programming for Every Language
Your language to speak with all.
...The alpha IDE is at http://spel.sourceforge.net/src/web/spel.html (wait for it to finish loading before clicking "translate") Since it is early prototype, it's not easy to use, If you are interested, join the mailing list. latest code is in the git repository. -
23
Microsoft-Azures-Basic-C--Pull
This is a simple C# Program that uses Microsoft Azures pull
...I provide some screenshots and tips on where to place what where. After you have placed in your Microsoft Azures API Key and Postman Client and Body. You now be able to insert inputs via code like the example in my image(s). -
24
Python Machine Learning book
The book code repository and info resource
What you can expect are 400 pages rich in useful material just about everything you need to know to get started with machine learning. From theory to the actual code that you can directly put into action! This is not yet just another "this is how scikit-learn works" book. I aim to explain all the underlying concepts, tell you everything you need to know in terms of best practices and caveats, and we will put those concepts into action mainly using NumPy, scikit-learn, and Theano. This is not yet just another "this is how scikit-learn works" book. its aim is to explain how Machine Learning works, tell you everything you need to know in terms of best practices and caveats, and then we will learn how to put those concepts into action using NumPy, scikit-learn, Theano and so on. ... -
25
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. These notebooks often demonstrate how to transform, analyze, and visualize large datasets using PySpark APIs, which mirrors many real-world big data use cases. ...
