Jupyter Notebooks
Showing 36 of 54 projects
A comprehensive guide to Python's essential data science libraries, available as free Jupyter notebooks.
A comprehensive collection of TensorFlow tutorials and examples for beginners, covering both TF v1 and v2 with clear explanations.
An uncompromising, opinionated Python code formatter that enforces consistent style automatically.
120+ interactive Python coding interview challenges with Anki flashcards, focusing on algorithms and data structures.
A comprehensive collection of data science Python notebooks covering deep learning, machine learning, big data, visualization, and essential tools.
An introduction to Bayesian inference and probabilistic programming using Python and PyMC, with a computational-first approach.
Jupyter notebooks with example code and exercises from the first edition of Hands-on Machine Learning with Scikit-Learn and TensorFlow.
The fastai book, published as Jupyter Notebooks, provides an introduction to deep learning, fastai, and PyTorch.
Companion materials and IPython notebooks for the 'Python for Data Analysis' book, covering pandas, NumPy, and data science workflows.
A collection of Python programs, usually short and challenging, designed to perfect specific programming skills.
A comprehensive Python library for creating static, animated, and interactive visualizations and publication-quality figures.
A repository of examples, utilities, and best practices for building and deploying production-ready recommendation systems.
An interactive, open-source graphing library for Python that creates browser-based visualizations with over 30 chart types.
A collection of hands-on tutorials and practical examples for using Google's Gemini API across text, image, video, audio, and robotics applications.
A composable, modular, and scalable machine learning toolkit for building AI platforms on Kubernetes.
A high-performance CPU, GPU, and memory profiler for Python with AI-powered optimization suggestions.
A comprehensive collection of machine learning and deep learning models, trading agents, and simulations for stock market forecasting.
A collection of beginner-friendly TensorFlow tutorials with accompanying YouTube videos covering deep learning fundamentals and advanced topics.
A collection of ready-to-run Docker images containing Jupyter applications and interactive computing tools.
A collection of ready-to-run Docker images containing Jupyter applications and interactive computing tools.
Code and Jupyter notebooks for the book 'Introduction to Machine Learning with Python' by Andreas Mueller and Sarah Guido.
A collection of Jupyter notebooks with real-world examples for learning Python's pandas data analysis library.
A Python tool for parameterizing, executing, and analyzing Jupyter Notebooks at scale.
A Python library for performance and risk analysis of financial portfolios, generating comprehensive tear sheets.
An archived experiment integrating TensorFlow's machine learning capabilities directly into the Swift programming language with first-class differentiable programming.
A curated collection of Python tutorials and resources for data science, machine learning, and natural language processing.
A notebook-driven development platform that creates high-quality documentation, tests, CI, and packaging from Jupyter notebooks.
Code repository for the 'Machine Learning with PyTorch and Scikit-Learn' book, providing practical examples and notebooks.
A collection of self-paced tutorials and programming exercises for learning quantum computing and Q#.
A collection of self-paced tutorials and programming exercises for learning Q# and quantum computing concepts.
A community-driven open-source project that structures threat hunting workflows using MITRE ATT&CK, Jupyter notebooks, and AI-augmented planning.
A free course teaching diffusion models theory and hands-on implementation using Hugging Face's Diffusers library.
An open-source tool for building publication-quality books and documents from computational content like Jupyter notebooks.
A comprehensive collection of tutorials, examples, and resources for understanding and solving machine learning and pattern classification problems.
A Python library for visualizing missing data in pandas DataFrames using matrix, bar, heatmap, and dendrogram plots.
A collection of sample applications demonstrating quantum programming concepts and algorithms using Microsoft's Quantum Development Kit.
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