examples
cppflow
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examples | cppflow | |
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143 | 9 | |
7,742 | 759 | |
1.2% | - | |
6.2 | 0.0 | |
23 days ago | 11 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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My Favorite DevTools to Build AI/ML Applications!
TensorFlow, developed by Google, and PyTorch, developed by Facebook, are two of the most popular frameworks for building and training complex machine learning models. TensorFlow is known for its flexibility and robust scalability, making it suitable for both research prototypes and production deployments. PyTorch is praised for its ease of use, simplicity, and dynamic computational graph that allows for more intuitive coding of complex AI models. Both frameworks support a wide range of AI models, from simple linear regression to complex deep neural networks.
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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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GPU Survival Toolkit for the AI age: The bare minimum every developer must know
AI models, particularly those built on deep learning frameworks like TensorFlow, exhibit a high degree of parallelism. Neural network training involves numerous matrix operations, and GPUs, with their expansive core count, excel in parallelizing these operations. TensorFlow, along with other popular deep learning frameworks, optimizes to leverage GPU power for accelerating model training and inference.
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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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Tensorflow help
I am on a new ftc team trying to get vision to work. I used the ftc machine learning tool chain but I have yet to get a good result with at best a 10% accuracy rate. I have changed everything possible in the tool chain with little luck. To fix this, I have tried making my own .tflite model using the google colab from https://www.tensorflow.org/. When ever I try to run the same code with my own .tflite model, it gives me the error "User code threw an uncaught exception: IllegalStateException - Error getting native address of native library: task_vision_jni". It gives me the same error with official tensor flow tflite test models, and when I put them on a raspberry pi, both worked just fine. Does anyone have a fix to this error or even just tips for the machine learning toolchain?
cppflow
- Easily run TensorFlow models from C++
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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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[Python] Importing a TensorFlow AI?
I toyed around with this idea a while back but I never got around to finishing the implementation. If all you need is inference with no training and you are relatively familiar with c++ you could look into creating a module for Godot that interfaces with the Tensorflow C API. Something like cppflow would provide an even easier API to work with. Looking into that project could also explain how they interface with the Tensorflow C API if you'd rather cut out the middle man. A module like this would let you train your model in Python and then load it and perform inference in Godot natively.
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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.
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TF-agent with C/C++ environment
Found this which seems more recent (uses TF 2, updated 4 days ago): https://github.com/serizba/cppflow
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
keras2cpp - This is a bunch of code to port Keras neural network model into pure C++.
Selenium WebDriver - A browser automation framework and ecosystem.
ssd_keras - A Keras port of Single Shot MultiBox Detector
Apache Spark - Apache Spark - A unified analytics engine for large-scale data processing
emlearn - Machine Learning inference engine for Microcontrollers and Embedded devices
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.