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Published directory entries matching your search.
H2O is an open-source, distributed, fast, and scalable machine learning platform hosted on GitHub by the h2oai organization. It supports a wide range of machine learning methods including deep learning, gradient boosting, random forest, generalized linear modeling, clustering, dimensionality reduction, rule fitting, support vector machines, stacked ensembles, and automated machine learning.
H2O Sparkling Water is an open-source GitHub repository maintained by the h2oai organization. It provides H2O machine learning functionality that can run inside Apache Spark clusters. The repository includes getting started guides, user documentation, binary download options, and resources for testing the tool.
HackerRank is described as a coding test and interview solution for hiring developers. Its offerings include an AI Add-on with features such as proctor mode, which monitors sessions for suspicious activities via webcam, screen capture and other sources and presents findings in a report.
Hackr.io is a tech learning hub offering step-by-step tutorials, AI-powered mentors and a personal dashboard. It includes expert-led project walkthroughs, online code editors, an AI interviewer for preparation, and a blog, YouTube channel and weekly newsletter. A help page covers common questions about accounts, code editors and submitting tutorials.
A hands-on course to train and deploy a serverless API that predicts crypto prices.
Health Data Nexus is an open data platform for AI research and education in medicine. It offers federated data search, allowing users to search partner platform PhysioNet resources without leaving the site. Datasets are listed under credentialed access, and access zones impose requirements including a data use agreement and, for Zone 3, REB approval.
Heights Platform is an AI online course creation and community software platform. It lets creators build and sell online courses, communities, coaching programs, and digital products, with unlimited videos and modules, lesson editor tools, drip feed, and payment plans via Stripe or PayPal. It also provides a website builder and landing pages.
Is a personal, portable Hadoop environment that comes with a dozen interactive Hadoop tutorials.
This resource offers a downloadable, printable poster outlining 15 common data fallacies as part of data literacy lessons, with a monthly random giveaway for free mailed copies of the poster.
Materials for the Hugging Face Diffusion Models Course, a free course on diffusion models. It covers theory, generating images and audio with Diffusers, training and fine-tuning models, and conditional generation. Prerequisites include Python skills and basics in deep learning and PyTorch.
A free LLM course from Hugging Face that teaches large language models and natural language processing. It covers the Transformers, Datasets, Tokenizers, and Accelerate libraries and the Hugging Face Hub, along with some libraries outside the ecosystem. The course is without ads.
Image to 3D AI generates 3D models from images or text. It is presented as useful for game development, 3D printing, interior design and education. Paid subscription plans include Pro at 700 credits per month and Max at 1600 credits per month; one-time credit purchases are also offered.
Open-source project that provides AI-assisted learning, courses, tutorials, or study support.
An open source, MIT-licensed Python library imported as imblearn that relies on scikit-learn. It provides tools for classification with imbalanced classes. The documentation includes installation guides, a user guide, an API reference and a gallery of examples.
A free, self-paced beginner course on data science foundations. It covers the data science life cycle, tasks, programming languages, machine learning, and the analytics landscape, with an assessment. Study materials are provided after enrollment, and a certificate of completion is offered.
IPython notebooks from Data School's video tutorials on scikit-learn.
An archived educational course repository for learning about LLMs, LLMOps and vector databases by designing, training and deploying a real-time financial advisor LLM system. It includes source code, video and reading materials, and covers a training pipeline using QLoRA and a streaming pipeline ingesting financial news. The repository was archived by its owner and is read-only.
Access all JetBrains IDEs, plugins, and courses for your studies and future tech career.
A course on generative modelling, currently unfinished, with the first approximately five lessons mostly done. It covers topics such as PyTorch basics, representations, CLIP, GANs, sequence modelling and transformers. No prior deep learning knowledge is assumed, though comfort with some Python and Jupyter Notebooks is expected.
Supports teaching, studying or skills practice with AI-guided learning tools.
Open-source Java scientific computing library for JVM and Android platforms, with SciPy-like APIs for use in machine learning and data science workflows. It provides functionality for signal processing, FFT, filters, PSD, and DCT operations.
JSMLT is an open source JavaScript library for education in machine learning. It implements supervised learning algorithms, including SVM, random forest, decision tree, perceptron, neural networks, k-nearest neighbors and logistic regression, and provides visualization examples for exploring how the algorithms work.
A Clojure wrapper for deeplearning4j with added syntactic sugar, aiming to provide a declarative API for machine learning on the JVM. It translates Clojure keywords into Java method calls, making many deeplearning4j configuration options accessible. The project is a work in progress, and configurations have been tested on basic feedforward nets, RNNs and CNNs.
A beginner-friendly guide on using Keras to implement a simple neural network in Python. It walks through MNIST handwritten digit classification, covering setup, data preparation, building the model, compiling, training, testing and using the model. It is intended for complete beginners to Keras who have a basic background knowledge of neural networks.
