Search Results for "load" - Page 2
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Showing 36 open source projects for "load"
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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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Fully Managed MySQL, PostgreSQL, and SQL ServerCloud SQL handles your database ops end to end, so you can focus on your app.
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midipiano chung is a free open source standalone virtual midi acoustic piano synthesizer /expander with samples based sounds and originals dsps effects, connected to the choozen virtual midi input (up to 3 if any) and midiout(thru) ports of your computer.It is easily extensible by adding or modifying sound files (mp3,wav) in the /sounds/ folder.Works well as output for midi_chung player and midirec_chung recorder or an external usb midi master keyboard. It is written in compiled freebasic...
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TenorSpace.js
Neural network 3D visualization framework
TensorSpace is a neural network 3D visualization framework built using TensorFlow.js, Three.js and Tween.js. TensorSpace provides Keras-like APIs to build deep learning layers, load pre-trained models, and generate a 3D visualization in the browser. From TensorSpace, it is intuitive to learn what the model structure is, how the model is trained and how the model predicts the results based on the intermediate information. After preprocessing the model, TensorSpace supports the visualization of pre-trained models from TensorFlow, Keras and TensorFlow.js. ... -
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ADL mining framework
A set of applications for the mining of Activities of Daily Living
...In this project, an ontology-based framework for the mining of ADL with a generic ontology and a modular architecture is proposed. The framework includes applications to: i) load multiple datasets available in literature, ii) to provide different methods for the segmentation of the activities, and iii) to transform the datasets into different ontological models. -
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anaGo
Bidirectional LSTM-CRF and ELMo for Named-Entity Recognition
...Thus, we can easily use anaGo for any language. In anaGo, the simplest type of model is the Sequence model. Sequence model includes essential methods like fit, score, analyze and save/load. For more complex features, you should use the anaGo modules such as models, preprocessing and so on. -
$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.
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Bender
Easily craft fast Neural Networks on iOS
...Bender allows you to run trained models, you can use Tensorflow, Keras, Caffe, the choice is yours. Either freeze the graph or export the weights to files. You can import a frozen graph directly from supported platforms or re-define the network structure and load the weights. Either way, it just takes a few minutes. Bender suports the most common ML nodes and layers but it is also extensible so you can write your own custom functions. With Core ML, you can integrate trained machine learning models into your app, it supports Caffe and Keras 1.2.2+ at the moment. Apple released conversion tools to create CoreML models which then can be run easily. ... -
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When to use TensorFlowSharp
TensorFlow API for .NET languages
...The project exposes TensorFlow’s native C API through a strongly typed interface designed to integrate naturally with the .NET ecosystem. Its primary purpose is to enable developers working in Microsoft-based environments to load trained TensorFlow models and perform inference or additional training within .NET applications. The library focuses mainly on providing access to the low-level TensorFlow runtime rather than offering the high-level abstractions commonly available in Python libraries like Keras. This design allows applications written in C# or F# to execute machine learning graphs produced by Python workflows while maintaining compatibility with the TensorFlow runtime. -
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Five video classification methods
Code that accompanies my blog post outlining five video classification
...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 a whole whack load of hyperparameters we don’t have to worry about. Every video will be subsampled down to 40 frames. So a 41-frame video and a 500-frame video will both be reduced to 40 frames, with the 500-frame video essentially being fast-forwarded. We won’t do much preprocessing. A common preprocessing step for video classification is subtracting the mean, but we’ll keep the frames pretty raw from start to finish. -
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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. ... -
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nunn
This is an implementation of a machine learning library in C++17
nunn is a collection of ML algorithms and related examples written in modern C++17. -
Custom VMs From 1 to 96 vCPUs With 99.95% UptimeLive migration and automatic failover keep workloads online through maintenance. One free e2-micro VM every month.
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Unsupervised Random Forest
On-line Unsupervised Random Forest
This tool uses Random Forest and PAM to cluster observations and to calculate the dissimilarity between observations. It supports on-line prediction of new observations (no need to retrain); and supports datasets that contain both continuous (e.g. CPU load) and categorical (e.g. VM instance type) features. In particular, we use an unsupervised formulation of the Random Forest algorithm to calculate similarities and provide them as input to a clustering algorithm. For the sake of efficiency and meeting the dynamism requirement of autonomic clouds, our methodology consists of two steps: (i) off-line clustering and (ii) on-line prediction. ... -
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Monk Computer Vision
A low code unified framework for computer vision and deep learning
Monk is an open source low code programming environment to reduce the cognitive load faced by entry level programmers while catering to the needs of Expert Deep Learning engineers. There are three libraries in this opensource set. - Monk Classiciation- https://monkai.org. A Unified wrapper over major deep learning frameworks. Our core focus area is at the intersection of Computer Vision and Deep Learning algorithms
