Search Results for "as built" - Page 2
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Showing 33 open source projects for "as built"
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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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Gemini 3 and 200+ AI Models on One PlatformBuild, govern, and optimize agents and models with Gemini Enterprise Agent Platform.
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SageMaker MXNet Training Toolkit
Toolkit for running MXNet training scripts on SageMaker
...You can also train and deploy models with Amazon algorithms, which are scalable implementations of core machine learning algorithms that are optimized for SageMaker and GPU training. If you have your own algorithms built into SageMaker compatible Docker containers, you can train and host models using these as well. -
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PyText
A natural language modeling framework based on PyTorch
...We use PyText at Facebook to iterate quickly on new modeling ideas and then seamlessly ship them at scale. Distributed-training support built on the new C10d backend in PyTorch 1.0. Mixed precision training support through APEX (trains faster with less GPU memory on NVIDIA Tensor Cores). Extensible components that allows easy creation of new models and tasks. -
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Tensor2Tensor
Library of deep learning models and datasets
Deep Learning (DL) has enabled the rapid advancement of many useful technologies, such as machine translation, speech recognition and object detection. In the research community, one can find code open-sourced by the authors to help in replicating their results and further advancing deep learning. However, most of these DL systems use unique setups that require significant engineering effort and may only work for a specific problem or architecture, making it hard to run new experiments and... -
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A Machine Learning Course with Python
A course about machine learning with Python
...The purpose of this project is to provide the most important aspects of Machine Learning by presenting a series of simple and yet comprehensive tutorials using Python. In this project, we built our tutorials using many different well-known Machine Learning frameworks such as Scikit-learn. In this project you will learn what is the definition of Machine Learning? When it started and what is the trending evolution? What are the Machine Learning categories and subcategories? What are the mostly used Machine Learning algorithms and how to implement them? -
Add Two Lines of Code. Get Full APM.Works out of the box for Rails, Django, Express, Phoenix, and more. Monitoring exceptions and performance in no time.
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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 networks can be trained through interaction with simulated environments. The project is commonly used by learners who want to move beyond theory and understand the practical mechanics of training RL agents. ... -
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Face Recognition
World's simplest facial recognition api for Python & the command line
...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. It could even do real-time face recognition and blur faces on videos when used with other Python libraries. -
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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' argument in model constructor for all image models, weights='msd' for the music tagging model). ... -
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text_summurization_abstractive_methods
Multiple implementations for abstractive text summurization
This repo is built to collect multiple implementations for abstractive approaches to address text summarization it is built to simply run on google colab , in one notebook so you would only need an internet connection to run these examples without the need to have a powerful machine , so all the code examples would be in a jupyter format , and you don't have to download data to your device as we connect these jupyter notebooks to google drive
