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Published directory entries matching your search.
ChatGPT for Jupyter is a browser extension that provides AI-powered helper functions within Jupyter Notebooks, powered by ChatGPT. The extension is compatible with Chrome, Edge, and Firefox browsers, with its code and documentation hosted in a public GitHub repository under the TiesdeKok organization.
A blog post explaining CLIP, a transformer model from OpenAI that matches text embeddings with image embeddings. It offers high-level prompt engineering tips for generative art and includes over 3500 prompt output examples, along with a section of Colab notebook links.
DAIR.AI is a GitHub organization that states it aims to democratize AI research, education, and technologies. Its repositories include a prompt engineering guide covering guides, papers, lessons, notebooks and resources for prompt engineering, context engineering, RAG, and AI Agents, and AI-Papers-of-the-Week, which highlights top machine learning papers each week.
A public GitHub template repository for data science lifecycle projects. It provides a default directory structure including folders for code, data, docs, environments, notebooks, pipelines and tests, plus issue templates and a .github labels file. The source describes it as a launch-off point with minimal opinions. One manual step is creating labels from the labels file.
deep-floyd/IF is a GitHub repository for the IF image generation model developed by DeepFloyd Lab at StabilityAI. It provides documentation for minimum model requirements, quick start guides, local notebook support, and integration with the Hugging Face Diffusers library for model use.
Frankensteinian amalgamation of notebooks, models and techniques for the generation of AI Art and Animations.
Collection of Google Colab Notebooks for ML Arts.
Facets is a project containing two visualizations, Facets Overview and Facets Dive, for understanding and analyzing machine learning datasets. The visualizations are Polymer web components backed by TypeScript that can be embedded in Jupyter notebooks or webpages. Overview produces feature-by-feature statistical analysis and compares statistics across datasets. The repository was archived on Jul 27, 2024 and is now read-only.
Flyflow is a GitHub organization with public repositories including an OpenAPI specification for the Flyflow API, a service framework, a Python library, and demo applications and notebooks. The organization page lists no public members.
Google AI Pro for Students is a Google program providing free AI training and tools for students. It offers access to Gemini and Gemini Notebook to support exam preparation, writing improvement, and career development. The program includes resources to help students integrate AI into their study workflows.
Sample code and notebooks for Generative AI on Google Cloud.
Notebook by @johnowhitaker exploring Stable Diffusion details.
A freeCodeCamp handbook for developers on organizing AI agents for real business cases. It covers prerequisites, large language models, definitions of AI agents, and a decision algorithm for creating optimal agents, including checking budget, starting with one agent, and measuring performance. It uses a Google Research, Google DeepMind, and MIT paper as its primary source and code examples in a Google Colab notebook.
A GitHub repository holding the notebooks and code for the book "Introduction to Machine Learning with Python" by Andreas Mueller and Sarah Guido, together with the mglearn library of helper functions for figures and datasets. The book requires scikit-learn 0.20.0, and the code also needs numpy, scipy, matplotlib, pandas and pillow.
IPython notebooks from Data School's video tutorials on scikit-learn.
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.
Jupyter AI is an open source extension that connects AI agents to computational notebooks in JupyterLab. It provides a native chat UI and integrates agents through the Agent Client Protocol, with a permission system requiring approval before files are written or commands executed. It is under incubation in the JupyterLab organization.
Notebook with examples of using Jupyter Widgets in Colab, allowing interactive inputs.
This page is the Simple Widget Introduction of the Jupyter Widgets documentation. It describes widgets as eventful Python objects with a browser representation, used to build interactive GUIs for notebooks and synchronize information between Python and JavaScript. It covers display, views, closing widgets, and properties.
Jupyter Notebooks as Markdown Documents, Julia, Python or R scripts.
Kubeflow is described as the cloud native AI platform, composed of modular open source projects forming a Kubernetes-native stack for data and AI workloads. Its subprojects include Kubeflow Pipelines for building and deploying portable machine learning workflows, Kubeflow Trainer for distributed AI training and LLM fine-tuning, Kubeflow Katib for automated machine learning, Kubeflow Notebooks for interactive development environments, and Kubeflow Hub for indexing models and metadata. It is a Cloud Native Computing Foundation project.
Kyso was a universal aggregator for all research formats that ceased operation on September 31, 2023, and is now maintained as an open source project. It launched in 2018 to help the data science community share insights and address collaboration challenges across varied workflows and tools, with an initial focus on Jupyter Notebooks. During its operation, it served thousands of users and hundreds of organizations.
Lucent is an adaptation of the Lucid library for PyTorch. It is in a pre-alpha phase and can be installed locally with pip install torch-lucent. Its notebooks can be run via Google Colab or locally with Jupyter.
Lux is a Python library for automated data exploration. When a pandas dataframe is printed in a Jupyter notebook, Lux recommends a set of visualizations highlighting trends and patterns, shown in an interactive widget for browsing and exploring the dataset.
