Search Results for "data classification"

The Artifact Geomorph Toolbox 3D software is designed to provide the archaeologist interested in artifact shape variability with a toolbox to allow the acquisition, analysis and results exploration of homologous 3D landmark-based geometric morphometric data. As such, the toolbox contains an automated item and semi-landmarks positioning procedure and the fundamental statistical analyses and procedures to allow the processing and analysis of the data. It is designed to be easy to use and...

Slides and Jupyter notebooks for the Deep Learning lectures at Master Year 2 Data Science from Institut Polytechnique de Paris. This course is being taught at as part of Master Year 2 Data Science IP-Paris. Note: press "P" to display the presenter's notes that include some comments and additional references. This lecture is built and maintained by Olivier Grisel and Charles Ollion.

NEW VERSION 3.2.1.5 - MARS 2022 ECG Logger is a Wearable Cardio Monitor for Long-Term (up to 24h) ECG Data Acquisition and Analysis (aka Holter) with an ECG live (real-time) mode. The ECG Logger project is aimed for providing a very low-cost open-source "Hardware and Software" for a Cardiac Rhythmic Holter. The hardware has been made very simple and is based on an Arduino Nano micro-controller with two companion boards for the SD card mass-storage and the instrumentation differential...

...This repository covers essential building blocks like sessions (for older TF versions), placeholders, variables, activation functions, and optimizers, before guiding learners through building end-to-end models for regression, classification, and data pipelines. Beyond the basics, the project includes examples of convolutional neural networks, recurrent networks, autoencoders, reinforcement learning, generative adversarial networks, and transfer learning workflows. By pairing code examples with conceptual explanations, the tutorials make abstract machine learning ideas accessible and encourage experimentation with TensorBoard visualization and distributed training.

...Rather than creating implementations from scratch, we draw from existing state-of-the-art libraries and build additional utility around loading image data, optimizing and evaluating models, and scaling up to the cloud.

...It provides a collection of notebooks that walk attendees from basic machine-learning concepts into practical modeling using the scikit-learn library. The tutorial covers data preparation, model fitting, evaluation, and common algorithms such as classification, regression, clustering, and dimensionality reduction. It is designed for people who already have a working Python environment and some familiarity with NumPy, SciPy, and Matplotlib. The repository specifies a clear list of dependencies so that participants can reproduce the environment used in the tutorial, and many downstream forks keep the content updated for newer versions of scikit-learn. ...

Deep-Learning-for-Medical-Applications is a repository that compiles deep learning methods, code implementations, and examples applied to medical imaging and healthcare data. The project addresses domain-specific challenges like segmentation, classification, detection, and multimodal data (e.g. MRI, CT, X-ray) using state-of-the-art architectures (e.g. U-Net, ResNet, GAN variants) tailored to medical constraints (small datasets, annotation costs, class imbalance). It includes Jupyter notebooks, model architectures, data preprocessing pipelines, and evaluation scripts specific to medical imaging tasks. ...

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...

New version moved to http://github.com/ctuning/ck Collective Mind framework (cM) is an open-source plugin-based schema-free repository and infrastructure for collaborative, systematic and reproducible research and experimentation. This 3rd version (started in 2006) helps to implement, preserve, share and reproduce the whole experimental setup as connected modules and data. cM uses crowdsourcing to leverage knowledge and computational resources of multiple users. For example, it includes multi-objective GCC, LLVM and ICC auto-tuning scenarios using shared benchmarks, codelets, data sets, tools, and combined with classification and predictive models. cM includes OpenME interactive interface to open up and expose internals of various third-party tools such as GCC, LLVM, run-time systems, etc. and connect them to cM through dynamic plugins that allows online analysis and tuning of programs and architectures. ...

Semtinel is an application framework for the semi-automatic creation, maintenance and analysis of hierarchical concept schemes (thesaurus, classification or ontology). Semtinel supports the development of new analysis methods and visualization techniques

Facilitates data mining/natural language processing experiments to be executed on weblogs, such as classification, clustering and rating. As part of these experiments, it is possible to apply Latent Semantic Analysis.

Pattern recognition software package. It includes several classification and clustering algorithms. It can read data from a set of images, an ASCII file or a JDBC connection. A small TCP data server with its corresponding JDBC driver is included.

Data Mining Platform is a platform for data mining and analysis. It contains many of the new and sophisticated methods such as kernel-based classification, two-way clustering, bayesian networks, pattern recognition for time series analysis and many other