examples
cppflow
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examples | cppflow | |
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142 | 9 | |
7,699 | 756 | |
1.2% | - | |
6.2 | 0.0 | |
7 days ago | 10 months ago | |
Jupyter Notebook | C++ | |
Apache License 2.0 | MIT License |
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
examples
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Open Source Ascendant: The Transformation of Software Development in 2024
AI's Open Embrace Artificial intelligence (AI) and machine learning (ML) are increasingly leveraging open-source frameworks like TensorFlow [https://www.tensorflow.org/] and PyTorch [https://pytorch.org/]. This democratization of AI tools is driving innovation and lowering entry barriers across industries.
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Best AI Tools for Students Learning Development and Engineering
Which label applies to a tool sometimes depends on what you do with it. For example, PyTorch or TensorFlow can be called a library, a toolkit, or a machine-learning framework.
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Releasing The Force Of Machine Learning: A Novice’s Guide 😃
TensorFlow: An open-source machine learning framework for high-performance numerical computations, especially well-suited for deep learning.
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MLOps in practice: building and deploying a machine learning app
The tool used to build the model per se was TensorFlow, a very powerful and end-to-end open source platform for machine learning with a rich ecosystem of tools. And in order to to create the needed script using TensorFlow Jupyter Notebook was used, which is a web-based interactive computing platform.
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🔥14 Excellent Open-source Projects for Developers😎
10. TensorFlow - Make Machine Learning Work for You 🤖
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🔥🚀 Top 10 Open-Source Must-Have Tools for Crafting Your Own Chatbot 🤖💬
#2 TensorFlow
- Are there people out there who still like Sam atlman - AI IS AT DANGER
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How popular are libraries in each technology
Machine learning is the process of using algorithms and statistical models to enable computers to learn from data. There are many tools and libraries available for machine learning, but the most popular by far is TensorFlow. TensorFlow is an open-source platform for machine learning developed by Google. It has over 176k stars on Github and is used by companies such as Airbnb and Intel.
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React + Tensorflow.js , a cool recipe for AI powered applications
Tensorflow is Google's "end-to-end machine learning platform". It's a framework to manage the whole lifecycle of a Machine Learning (and AI) project, from data preparation to production deployment. Remember the math stuff we talked about in the last section? Tensorflow manages that in addition to a lot of other stuff. Its core API is written for Python and you have to know your math just a little bit in order to play with it. It's more for deep learning models (neural networks) and has a lot of already implemented "layers" for you to use in your network. You can prepare data (images included with the option of image augmentation for small data sets ... yay! 😃), experiment with different model architectures, tune the model's hyperparameters (a fancy name for model configs), train, validate and test your models and monitor your models in production. It's a great framework, but it is not an easy one to learn, especially if you don't like math that much!
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List of AI-Models
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cppflow
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[P] libtensorflow_cc: Pre-built TensorFlow C++ API
It’s been awhile since I’ve looked at it, so not sure how hard it would be to get to work. I only commented since you mentioned that you would support other operating systems. For others interested in cross platform support there is also cppflow.
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Deep learning classification with C++
what about start with keras and convert model to c++ ? https://github.com/pplonski/keras2cpp https://github.com/serizba/cppflow
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Using embedding model in C++ app
My solution so far: I am using a compiled Tensorflow C DLL in combination with cppflow (https://github.com/serizba/cppflow). However, I get problems when I take models which use operations from the tensorflow_text python module since I don’t know how to get their C++ API.
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What is the most used library for AI in C++ ?
I use cppflow to run compiled tensorflow models natively in C++. It works like a charm :)
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Simplest way to deploy Keras NN model into C++?
If your re using keras with TensorFlow you can save it as a saved model format and then you can easily use cppflow to perform inference with it.
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I trained a Neural Network to understand my commands when playing my game
The whole game is written in C++ using SFML for the graphics, entt as Entity-Component-System and tensorflow for the Neural Network. Tensorflow itself is written in C, so I use cppflow to integrate it into my C++ framework.
What are some alternatives?
mlpack - mlpack: a fast, header-only C++ machine learning library
onnxruntime - ONNX Runtime: cross-platform, high performance ML inferencing and training accelerator
awesome-teachable-machine - Useful resources for creating projects with Teachable Machine models + curated list of already built Awesome Apps!
qt-tf-lite-example - Qt TensorFlow Lite example
face-api.js - JavaScript API for face detection and face recognition in the browser and nodejs with tensorflow.js
Selenium WebDriver - A browser automation framework and ecosystem.
keras2cpp - This is a bunch of code to port Keras neural network model into pure C++.
ssd_keras - A Keras port of Single Shot MultiBox Detector
emlearn - Machine Learning inference engine for Microcontrollers and Embedded devices
Apache Spark - Apache Spark - A unified analytics engine for large-scale data processing
Pytorch - Tensors and Dynamic neural networks in Python with strong GPU acceleration
DeepSpeech - DeepSpeech is an open source embedded (offline, on-device) speech-to-text engine which can run in real time on devices ranging from a Raspberry Pi 4 to high power GPU servers.