Search Results for "dynamicreports-examples"
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92 projects for "dynamicreports-examples" with 2 filters applied:
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python-small-examples
Focus on creating classic Python small examples and cases
...The repository includes examples covering topics such as file processing, JSON manipulation, data visualization, and library usage. The examples are intentionally short and easy to read, making them useful for beginners who want to understand Python syntax and programming logic step by step. The repository is organized as a large collection of small scripts and notes that can be browsed individually without needing to study a full project. -
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machine_learning_examples
A collection of machine learning examples and tutorials
...Many of the examples are accompanied by tutorials and educational materials that explain how the algorithms work and how they can be applied in real-world projects. The code is organized into small independent experiments so that learners can explore specific algorithms or techniques without needing to understand the entire codebase. -
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qxresearch-event-1
Python hands on tutorial with 50+ Python Application
...The project emphasizes practical experimentation, allowing beginners to modify and extend the example programs to explore new ideas. Many of the examples are accompanied by video explanations that guide learners through the code and demonstrate how the programs work in practice. -
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MEDIUM_NoteBook
Repository containing notebooks of my posts on Medium
MEDIUM_NoteBook is an open-source repository that contains a collection of Jupyter notebooks and code examples originally developed to accompany technical articles published on Medium. The project provides practical demonstrations of machine learning algorithms, data analysis workflows, and visualization techniques. Each notebook typically focuses on explaining a specific concept through step-by-step examples that combine explanatory text, code, and visual outputs. -
Go from Code to Production URL in SecondsSkip the Kubernetes configs. Cloud Run handles HTTPS, scaling, and infrastructure automatically. Two million requests free per month.
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Amazing-Python-Scripts
Curated collection of Amazing Python scripts
...The project includes scripts ranging from beginner-level utilities to more advanced applications involving machine learning, data processing, and system automation. Its goal is to provide developers with useful coding examples that can solve everyday problems, automate repetitive tasks, or serve as learning exercises. The repository encourages community contributions, allowing developers to add their own scripts and improve existing ones through pull requests. Examples include scripts for sentiment analysis, data scraping, web automation, log analysis, and interactive applications such as games or voice-controlled tools. ... -
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Machine Learning Study
This repository is for helping those interested in machine learning
...Many examples include dataset preparation, visualization of results, and experimentation with different modeling approaches. -
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Python Programming Hub
Learn Python and Machine Learning from scratch
...The repository emphasizes hands-on learning by demonstrating real programming tasks such as data manipulation, statistical analysis, visualization, and automation. It also includes examples of commonly used libraries such as NumPy, Pandas, and other tools used in data science workflows. -
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Python Code Tutorials
The Python Code Tutorials
...The repository covers a wide range of programming topics including cybersecurity, networking, web scraping, machine learning, GUI development, and automation scripts. Each tutorial typically includes complete Python code examples and explanations that demonstrate how to build real tools and applications step by step. Many tutorials focus on practical implementations such as building network scanners, web scraping tools, object detection systems, and automation utilities using Python libraries. The repository is organized into thematic directories that group tutorials by topic, allowing learners to navigate easily between areas such as ethical hacking, multimedia processing, or machine learning. -
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OpenVINO Notebooks
Jupyter notebook tutorials for OpenVINO
...The tutorials also illustrate how OpenVINO integrates with models from frameworks like PyTorch, TensorFlow, and ONNX to accelerate inference workloads. Many notebooks include end-to-end examples that show how to prepare input data, load optimized models, run inference, and visualize results. The project is particularly useful for developers who want to learn how to optimize machine learning inference pipelines for production environments. -
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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Made With ML
Learn how to develop, deploy and iterate on production-grade ML
...The project focuses on bridging the gap between experimental machine learning notebooks and real-world software systems that can be deployed, monitored, and maintained at scale. It provides structured lessons and practical code examples that demonstrate how to design machine learning workflows, manage datasets, train models, evaluate performance, and deploy inference services. The repository organizes these concepts into modular Python scripts that follow software engineering best practices such as testing, configuration management, logging, and version control. ... -
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AI-Tutorials/Implementations Notebooks
Codes/Notebooks for AI Projects
...The codebase acts as a hands-on learning resource, allowing users to experiment with new frameworks, architectures, and machine learning workflows through guided examples. -
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Machine Learning Foundations
Machine Learning Foundations: Linear Algebra, Calculus, Statistics
...The materials cover essential topics such as linear algebra, calculus, statistics, and probability, which form the theoretical basis of many machine learning algorithms. The repository includes Jupyter notebooks with explanations and examples that demonstrate how these mathematical principles relate to real machine learning applications. Each section introduces theoretical concepts and then illustrates them through practical coding examples to reinforce understanding. The project is designed for students and practitioners who want to strengthen their foundational knowledge before working with more advanced machine learning frameworks. -
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Advanced NLP with spaCy
Advanced NLP with spaCy: A free online course
...The course is designed to teach developers how to build real-world NLP systems by combining rule-based techniques with machine learning models. The repository includes lessons, exercises, and examples that guide learners through tasks such as tokenization, named entity recognition, text classification, and training custom NLP models. It also demonstrates how spaCy pipelines work and how developers can extend them with custom components and training data. The course is structured as a hands-on learning environment where students can run code examples, experiment with NLP techniques, and build practical language processing applications. ... -
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Model Zoo
Please do not feed the models
...The repository provides ready-to-run implementations across multiple domains, including computer vision, natural language processing, and reinforcement learning. Each model is organized into its own project folder with pinned package versions, ensuring reproducibility and stability. The examples serve both as educational tools for learning Flux and as practical starting points for building new models. GPU acceleration is supported for most models through CUDA integration, enabling efficient training on compatible hardware. With community contributions encouraged, the Model Zoo acts as a hub for sharing and exploring diverse machine learning applications in Julia. -
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machine-learning-refined
Master the fundamentals of machine learning, deep learning
machine-learning-refined is an educational repository designed to help students and practitioners understand machine learning algorithms through intuitive explanations and interactive examples. The project accompanies a series of textbooks and teaching materials that focus on making machine learning concepts accessible through visual demonstrations and simple code implementations. Instead of presenting algorithms purely through mathematical derivations, the repository emphasizes geometric intuition, visualization, and step-by-step experimentation. ... -
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Book6_First-Course-in-Data-Science
From Addition, Subtraction, Multiplication, and Division to ML
...The goal of the project is to make complex topics such as statistics, algorithms, and data analysis more accessible to learners by breaking concepts into clear explanations supported by code examples and diagrams. The material emphasizes a learning approach that combines theoretical knowledge with hands-on experimentation, often recommending interactive tools such as Jupyter notebooks to explore the ideas presented in the book. -
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RL with PyTorch
Clean, Robust, and Unified PyTorch implementation
...It includes code for popular deep reinforcement learning techniques such as Deep Q-Networks, policy gradient methods, actor-critic architectures, and other modern RL approaches. The repository is structured so that users can easily experiment with different algorithms and training environments. Many examples demonstrate how agents learn to interact with simulated environments through trial and error using reinforcement learning principles. The codebase emphasizes clarity and modular design so that researchers can extend the implementations or use them for experimentation and benchmarking. -
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Data Science Articles from CodeCut
Collection of useful data science topics along with articles
The Data-science repository from CodeCutTech is a curated collection of educational content focused on practical tools and workflows used in modern data science projects. Instead of providing a single software package, the repository aggregates articles, tutorials, and examples covering many topics within the data science ecosystem. The materials address areas such as MLOps, data management, project organization, testing practices, visualization techniques, and productivity tools used by data scientists. Each topic often includes references to code repositories, demonstrations, and video tutorials that show how the tools can be applied in real projects. ... -
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MLOps Zoomcamp
Free MLOps course from DataTalks.Club
...The course is designed to teach data scientists and engineers how to move machine learning models from experimentation environments into scalable production services. The repository provides lessons, code examples, and assignments that cover the entire MLOps lifecycle, including model training, experiment tracking, deployment, monitoring, and infrastructure management. Students learn to use widely adopted tools such as MLflow, orchestration frameworks, and cloud platforms to manage machine learning pipelines. The curriculum emphasizes hands-on projects so learners gain practical experience building automated ML pipelines and maintaining deployed models. -
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ML-NLP
This project is a common knowledge point and code implementation
ML-NLP is a large open-source repository that collects theoretical knowledge, practical explanations, and code examples related to machine learning, deep learning, and natural language processing. The project is designed primarily as a learning resource for algorithm engineers and students preparing for technical interviews in machine learning or NLP roles. It compiles important concepts that frequently appear in machine learning discussions, including neural network architectures, training methods, and common algorithmic techniques. ... -
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OpenMLSys-ZH
Machine Learning Systems: Design and Implementation
...The repository includes scripts or tooling to keep translation synchronized with upstream changes, versioning, and possibly translation metadata (contributors, timestamp). Users can browse or clone the translated documentation to follow along with the original content, deploy examples, or understand system internals in their preferred language. -
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Machine Learning and Data Science Apps
A curated list of applied machine learning and data science notebooks
...The repository organizes resources by industry categories such as finance, healthcare, agriculture, manufacturing, government, and retail, allowing practitioners to explore domain-specific machine learning use cases. Most examples are written in Python and frequently use Jupyter notebooks to present practical implementations and experiments. The project encourages contributions from data scientists and domain experts who want to share applied analytics projects and techniques that address real business challenges. -
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handson-ml
Teaching you the fundamentals of Machine Learning in python
handson-ml hosts the notebooks for the first edition of the same hands-on ML book, reflecting the tooling and idioms of its time while teaching durable concepts. It walks through supervised and unsupervised learning with scikit-learn, then introduces deep learning using the earlier TensorFlow 1 graph-execution style. The examples underscore fundamentals like bias-variance trade-offs, regularization, and proper validation, grounding learners before they move to deep nets. Even though the deep learning stack evolved, the classical ML sections remain highly relevant for production data problems. The code is crafted to be clear rather than clever, prioritizing readability for newcomers. ... -
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AiLearning-Theory-Applying
Quickly get started with AI theory and practical applications
...The repository provides extensive tutorials covering mathematical foundations, machine learning algorithms, deep learning concepts, and modern large language model architectures. It includes well-commented notebooks, datasets, and implementation examples that allow learners to reproduce experiments and understand the inner workings of various algorithms. The project also introduces important concepts such as probability theory, linear algebra, regression models, clustering methods, and neural network architectures. Advanced sections explore modern AI topics including transformers, BERT-based natural language processing systems, and practical competition-style machine learning workflows. -
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Machine learning algorithms
Minimal and clean examples of machine learning algorithms
Machine learning algorithms is an open-source repository that provides minimal and clean implementations of machine learning algorithms written primarily in Python. The project focuses on demonstrating how fundamental machine learning methods work internally by implementing them from scratch rather than relying on high-level libraries. This approach allows learners to study the mathematical and algorithmic details behind widely used models in a transparent and readable way. The repository...