#Machine Learning

Explore a hands-on resource for Sklearn and TensorFlow, complete with straightforward instructions for setup via Docker, PYPI, and NPM. This guide is suitable for those looking for practical insights, supporting output formats like HTML, PDF, EPUB, and MOBI. Also includes resources for data analysis in Python from ApacheCN for a comprehensive learning experience.

This repository provides a full PyTorch adaptation of the 'Dive Into Deep Learning' book, shifting from the original MXNet base. It spans key concepts in deep learning including data manipulation, linear regression, attention mechanisms, and computer vision, across various detailed chapters. The project welcomes contributions for continuous enhancement, serving as a valuable resource for developers aiming to advance their PyTorch deep learning skills. It is recommended to clone the repository or employ nbviewer for optimal notebook viewing.

Access a plethora of learning resources around Generative AI on Google Cloud's Vertex AI, including notebooks and code samples for developing and managing generative AI workflows. This extensive repository highlights the use of Vertex AI for creating solutions in image, speech processing, and conversational models.

Discover in-depth notes and summaries from DeepLearning.ai courses, focused on key topics like neural networks, optimization, and machine learning applications. Access expert reviews and insights, perfect for advancing skills in AI across different sectors.

Delve into an extensive collection of transfer learning resources, featuring academic papers, tutorials, and code implementations. Stay informed about cutting-edge developments and foundational theories in domain adaptation, deep transfer learning, and multi-task learning. Access various educational materials, including video tutorials, profiles of eminent scholars, and prominent research papers, offering essential insights for both newcomers and experienced researchers.

FLAML is a lightweight Python library that excels at AutoML and hyperparameter tuning across various tasks, such as classification and regression. With minimal computational requirements, it offers extensive customization for optimizing machine learning models and next-gen GPT-X applications using automated multi-agent frameworks. The library is perfectly designed to handle complex constraints while integrating seamlessly with MLflow and Microsoft Fabric Data Science for comprehensive MLOps/LMOps solutions.

The project offers an in-depth look into AI concepts ranging from basic to advanced levels, covering areas like machine learning, deep learning, and BERT-based natural language processing. It includes extensive tutorials and datasets, making it suitable for learners at different stages. The curriculum spans key areas such as foundational mathematics, machine learning competitions, the basics of deep learning, and a user-friendly Transformer guide. The materials are regularly refreshed to reflect the latest in AI development, providing a clear and thorough understanding of AI models.

The project provides a comprehensive collection of baseline strategies for data competitions, aimed at guiding beginners and enthusiasts. The repository focuses on practical and simple solutions, facilitating easy learning and application. Updated competition details and baseline strategies are accessible through related platforms and files, offering broad resources to improve participation and expertise in data challenges.

The project provides a well-organized collection of tutorials, articles, and resources relevant to Machine Learning and Deep Learning. It serves as a hub for educational content across various topics, including Artificial Intelligence, Natural Language Processing, and Statistical Learning. Contributors are encouraged to help expand this list by following the guidelines. The repository also offers targeted resources in languages like R and Python, enhancing skills in Data Science, NLP, and Machine Learning. This structured content spans basic introductions to advanced topics like Reinforcement Learning and Bayesian Machine Learning, offering resources suitable for both newcomers and experienced users.

Discover a comprehensive selection of AI repositories, tools, websites, papers, and tutorials, refreshed bi-hourly. This resource delivers noteworthy insights and straightforward access to advanced AI technologies, supporting developers, researchers, and enthusiasts to enhance their understanding and practical application. Learn to leverage open-source projects like TensorFlow, PyTorch, and Transformer models, improve skills through project-based tutorials, and benefit from interactive guides. This catalog serves as a rich resource for those aiming to deepen their machine learning knowledge, develop AI solutions, or explore the forefront of AI advancement.

BotSharp is a comprehensive open-source framework that simplifies the integration of AI features into business applications. It supports multi-agents and conversation state management, integrating with top LLM providers such as ChatGPT and PaLM. Developed in C# and running on .Net Core, the framework's modular design ensures easy customization through decoupled plugins for tasks like natural language processing and computer vision. Tailored for enterprise developers, it boosts operational efficiency by supporting AI project life cycles from development to deployment.

The project comprises daily updates on recent studies in computer vision, AI, and related disciplines, compiling a comprehensive repository of high-caliber papers from renowned conferences such as CVPR, IJCAI, and ICLR. The CVer community offers opportunities to explore advancements in AI applications across areas like object detection, semantic segmentation, GAN, and NeRF. Discover a multitude of studies to remain informed on cutting-edge computer vision and AI developments.

Delve into a thorough collection of TensorFlow 2.0 tutorials, winner of the PoweredByTF 2.0 Challenge, covering basic to advanced topics including CycleGAN, GPT, and BERT with video guides. This resource serves learners of all levels with practical examples on MNIST, CIFAR10, and ResNet using the latest TensorFlow features.

Mosec is a high-performance framework for serving ML models in the cloud. It integrates Rust for high speed and Python for ease of use, supporting dynamic batching, pipelined processing, and Kubernetes. Designed for both CPU and GPU workloads, it enables efficient online serving and scalable deployment. Suitable for developers focused on backend service development.

Discover a set of TensorFlow tutorials created specifically for beginners with clear examples for both TF v1 and v2. Learn traditional and modern practices, such as layers, estimators, and dataset APIs. The project includes instructions on basic operations, linear and logistic regressions, word embedding models, gradient boosting, and various neural network architectures. Additional guides cover data management, multi-GPU training, and customized layers. Keep up-to-date with the latest TensorFlow methods and enhance your skills with practical, hands-on experiences.

This comprehensive repository includes 35 Python notebooks focusing on data manipulation, classical machine learning, computer vision, and NLP. It offers practical guidance on MLOps, TensorFlow, and Scikit-Learn, enhanced by hands-on exercises. Topics encompass deep learning architectures, data analysis, visualization, and model deployment. Regular updates include transfer learning methods and advanced neural network techniques, beneficial for data scientists and machine learning engineers.

Discover the implementation of a neural network using Swift in a macOS environment, focusing on interpreting emoji inputs through machine learning. This open-source project features a three-layer architecture that processes 64 binary inputs from pixel data, using a sigmoid function for enhanced computational performance. Aiming to make neural networks approachable, this project showcases efficient binary number processing and fosters innovation. The project is accessible on GitHub under an MIT License.

Discover the basics of Machine Learning with Python using practical examples and interactive notebooks. This project accompanies the first edition of 'Hands-On Machine Learning with Scikit-Learn and TensorFlow,' including exercises and code solutions. The notebooks can be accessed online through Colaboratory, Binder, or Deepnote, and executed locally with an Anaconda setup recommended for Python 3.7. It offers a thorough educational experience for those interested in applying machine learning principles through practical scenarios.

Explore an objective roadmap detailing key machine learning concepts, processes, and tools, relevant for learning in 2023. It outlines problem identification, solution formulation with appropriate tools, and insights into the mathematical underpinnings of machine learning code. Inspired by Daniel Formoso's mindmaps, it offers resources for self-study, along with an interactive version and a detailed video walkthrough, ideal for enhancing one's knowledge of machine learning.

Discover a well-curated selection of data labeling tools tailored for a variety of domains including images, text, audio, video, 3D, and more. These tools offer distinct functionalities that boost the efficiency of data annotation processes, essential for machine learning projects. Notable tools such as labelImg and CVAT make image annotation easier, while YEDDA and ML-Annotate are ideal for text labeling tasks. Tools like EchoML and UltimateLabeling enhance audio and video annotations. This collection also addresses needs for Lidar, time series, and multidomain data labeling, providing critical resources for building robust AI solutions.

This project offers a fresh perspective on deep learning concepts through detailed mathematical derivations and fundamental principles, with code implementations using Python and NumPy. It delves into subjects such as deep feedforward networks, convolutional networks, and sequence modeling, providing practical insights and applications in natural language processing and computer vision. The work, complete with annotated code, aids students and practitioners without the dependency on existing frameworks. Frequent updates maintain exhaustive coverage and transparency for all audiences.

Explore Kubernetes-native AutoML that seamlessly automates processes like hyperparameter tuning, early stopping, and neural architecture search across multiple frameworks such as TensorFlow, PyTorch, and MXNet. This open-source project integrates efficiently with Kubernetes resources and tools such as Kubeflow Training Operator and Argo Workflows, supporting algorithms including Random Search and Bayesian Optimization. Discover framework compatibility with Goptuna, Hyperopt, and Optuna, and initiate efficient model tuning with the Python SDK.

This guide offers a multitude of solved problems covering various AI and deep learning topics, aiding data scientists and job seekers in mastering AI concepts necessary for interviews. With detailed explanations and problem-solving strategies, it serves as a valuable reference for enhancing technical knowledge and interview readiness.

This comprehensive list highlights popular open-source deep learning projects available on GitHub, including prominent tools such as TensorFlow, PyTorch, and OpenCV. These libraries and frameworks are vital for machine learning professionals, offering solutions tailored to applications in areas like neural networks and computer vision. With easy-to-use interfaces, strong community support, and robust functionalities, they cater to both beginners and advanced users focused on developing and enhancing machine learning skills and solutions.

Gorgonia provides a suite of tools for developing and testing machine learning models using graph computations in Go language. It competes in speed with TensorFlow and Theano while supporting CUDA for GPU computations, and aims to support distributed systems. The library is suited for developers familiar with Go who want to build effective ML systems, offering features like automatic differentiation, symbolic differentiation, and gradient descent optimization. Gorgonia fosters experimentation with alternative deep learning approaches and is supported by a committed community to assist developers.

Explore a comprehensive compilation of over 180 YouTube channels providing insights into data science, machine learning, AI, programming, and software engineering. This collection serves as a valuable resource for students, professionals, and hobbyists seeking to broaden their knowledge with content ranging from tutorials to podcast discussions. Regular updates and community input ensure the list's accuracy and relevance, appealing to those interested in data engineering, statistics, web development, and cybersecurity. Discover specialized content in programming languages such as Python, R, and C++ to enhance your learning experience.

Access comprehensive lecture notes and resources from leading courses like Andrew Ng's Machine Learning and MIT's Deep Learning. Ideal for AI enthusiasts seeking insights into advanced and foundational AI topics.

Discover an extensive AI vocabulary database designed to facilitate English-Chinese translation in AI domains. The platform includes approximately 2442 terms with standard translations, aiding in both learning and expertise refinement. Initially developed for technical article translations, the database is continually updated with expert oversight to ensure accuracy and expansion of content. Users can review each term's English and Chinese variations, abbreviations, and detailed explanations from authoritative sources, encouraging community contributions for ongoing improvements and achieving a comprehensive linguistic resource.

Explore a rich collection of AI resources spanning tools, courses, and literature, aimed at providing professionals and enthusiasts with a deep dive into key AI fields like machine learning and natural language processing. The guide offers segmentations into chat, image, and video creation tools, supported by educational material from renowned institutions to facilitate comprehensive understanding and application of AI innovations. Discover ongoing insights through curated journals and blogs, further enriched by free access to a variety of content.

This GitHub repository is a rich source of machine learning examples and tutorials aimed at boosting learning efficiency. The materials are neatly organized by course folders, connecting directly to educational content. Some newer examples utilize Google Colab, but the repository provides essential groundwork in areas like Natural Language Processing, Time Series Analysis, and Financial Engineering. These resources complement courses from deeplearningcourses.com, and offer practical insights into deep learning and AI. Cloning the repository is advised to stay updated with the latest content.

This extensive repository of AI courses provides free educational materials for learners at all levels, including topics such as Generative AI, Natural Language Processing, and Deep Learning. Featuring resources from renowned institutions like MIT, Stanford, and Harvard, it serves as a valuable tool for anyone looking to deepen their understanding of artificial intelligence. Contributions are welcome to continually expand this growing collection.

Explore a structured 100-day journey into machine learning, covering both supervised and unsupervised techniques. This project involves practical coding exercises in algorithms such as linear regression, k-NN, and support vector machines (SVMs). With resources from Coursera and YouTube, enhance your understanding of key concepts like decision trees and neural networks, fostering a comprehensive grasp of machine learning.

Explore a vast collection of over 500 projects in AI, machine learning, deep learning, computer vision, and natural language processing, all complete with source code. Continuously updated and tested, each project offers valuable insights into practical implementations and the latest advancements. Ideal for professionals aiming to enhance skills or engage in open-source contributions, this repository covers diverse domains like Python projects, healthcare, data analysis, and more. Discover projects ranging from basic to advanced levels, providing substantial learning opportunities and hands-on experience with real-world applicability. Contributions from numerous developers ensure a rich and expansive resource perfect for those interested in innovative AI development.

Engage in a 100-day program designed to deepen understanding of machine learning algorithms, covering initial topics like data preprocessing and linear regression, progressing to advanced subjects such as SVM and deep learning. Daily lessons include practical exercises, theoretical insights, and resources, aimed at professionals and enthusiasts seeking to enhance their skills in algorithms like decision trees and neural networks.

Access a rich collection of prompts tailored for a variety of large language models such as Siri and GPT-4o. This repository offers practical solutions for text manipulation, medical queries, programmatic support, and prompt creation. Ideal for developers and researchers interested in enhancing AI model interactions. Connect with a thriving Discord community for collaboration and support, and explore detailed use cases for improved AI outcomes.

This project offers a clear overview of AI ethics guidelines, principles, and regulations, simplifying the navigation of extensive resources. It supports practitioners in tackling the complex ethical challenges posed by widespread AI use, providing streamlined access to frameworks, tools, and standards, as well as insights into courses and policy developments, facilitating the adoption of responsible AI practices.

Kubeflow Training Operator offers a Kubernetes-based system for scalable, distributed training of machine learning models. Compatible with frameworks like PyTorch, TensorFlow, and XGBoost, it also supports HPC tasks through MPI. It simplifies model training via Kubernetes Custom Resources API and a Python SDK, aiding in efficient resource management. Explore integration and performance enhancement with comprehensive guides and community resources.

Discover key machine learning concepts with detailed guides created for Stanford's CS 229. This resource offers vital refreshers on prerequisites and extensive coverage of supervised, unsupervised, and deep learning, including practical tips for model training. Enhance your knowledge with comprehensive compilations and multi-language downloadable PDFs. Ideal for students and professionals aiming for a deep understanding of machine learning. Access the materials on any device via the dedicated site and assist with translations for broader accessibility.

Comprehensive TensorFlow tutorials for deep learning beginners, covering from linear models to advanced concepts. Updated for TensorFlow 2, includes YouTube videos and Google Colab support for easy access, serving both theoretical and practical needs.

Access essential cheat sheets offering insights and quick references for engineers in machine learning and deep learning. These resources cover tools like TensorFlow, Keras, PyTorch, and Numpy, and are tailored to support both novices and experienced professionals in improving their skills and efficiency. Each cheat sheet is crafted to present clear and informative content, enabling swift comprehension of key methodologies. Explore a variety of frameworks and libraries that facilitate modern AI and data science applications.

Explore insights into developing essential software engineering skills with an emphasis on Python and generative AI. Updated monthly, this project explores key competencies in areas like data structures, algorithms, Linux, version control, database management, backend development, system design, frontend basics, and specialized fields such as machine learning and NLP. Designed for individuals aiming to enhance expertise in adjacent technologies methodically, from Python data tools to advanced AI techniques.

This guide delves into the core concepts and practical uses of deep learning with PyTorch, targeted towards developers. It covers essential theories and hands-on coding examples, though it omits topics like recurrent neural networks to center on helping readers navigate advanced subjects independently. Perfect for those versed in Python, it lays a robust foundation for developing deep learning skills.

This repository offers a curated selection of resources and study notes on AI, ML, and DL, aimed at engineers, developers, and data scientists. It provides easy access to key materials in areas like natural language processing and neural networks. It supports community contributions and regular updates enhance engagement. Explore practical guides, tools, and libraries designed to expand understanding of AI and ML.

Discover a broad array of Keras-based Generative Adversarial Network (GAN) implementations that simplify research paper models to highlight fundamental concepts. This repository hosts diverse GAN models like AC-GAN, CycleGAN, and DCGAN, prioritizing the essence of each over intricate layer setups. Contributions and model suggestions are welcomed. Included are all codes needed for easy installation and execution, utilizing Keras for practical deep learning applications. Note: Collaboration opportunities available for ongoing development.

The LeeDL Tutorial, based on the acclaimed 2021 course from Professor Hung-yi Lee of Taiwan University, provides an engaging introduction to deep learning. By using anime-themed examples and a humorous teaching style, it breaks down complex theories. This tutorial offers detailed formula derivations, focusing on challenging concepts and expanding beyond earlier lectures to broaden understanding. Ideal for those interested in Chinese language explanations, it aids in developing deep learning intuition and exploring specialized areas.

Gradio facilitates the swift development and sharing of machine learning demos and web applications without requiring JavaScript or hosting expertise. It operates within diverse platforms including Jupyter notebooks and Google Colab, featuring intuitive interface functions for input and output. Gradio's dynamic sharing capability generates public URLs for demo access. For advanced custom web designs, users can utilize the 'Blocks' class. Supporting Python 3.10+, Gradio is ideal for AI web application developers seeking simplicity and extensive sharing options.

Learn about the integration of machine learning in the Flappy Bird game with neural networks and genetic algorithms. This open-source project leverages the Phaser framework and Synaptic Neural Network library to guide a bird's evolution and master flying techniques over time. Highlights include a detailed neural network structure and genetic algorithm to help the bird develop intelligent navigation strategies. Understand the step-by-step journey from initializing a random population to refining their abilities through fitness assessments and genetic processes like selection, crossover, and mutation.

Utilize an efficient NLP library offering scalable annotations across 200+ languages, suitable for tasks such as tokenization and language translation. It integrates state-of-the-art transformers like BERT and GPT-2 and supports Python, R, and JVM platforms. This library facilitates model imports from frameworks including TensorFlow and ONNX, enhancing compatibility in distributed machine learning systems.

Explore advanced deep learning examples optimized for NVIDIA GPUs such as Volta, Turing, and Ampere architectures. Access updated models from the NVIDIA GPU Cloud (NGC) Container Registry for high accuracy and performance. Applications include computer vision and natural language processing, utilizing multi-GPU and multi-node configurations. These models leverage NVIDIA's CUDA-X software stack and Tensor Cores. From EfficientNet to BERT and Jasper, find examples suited for diverse deep learning tasks. Participate in contributions on GitHub and help refine these models.

This collection gathers a wide range of papers and code concerning Generative Adversarial Networks (GANs), offering insights into applications such as image translation and facial attribute manipulation. It also delves into theoretical perspectives and machine learning methods with interdisciplinary applications in fields like medicine and music. Featuring advancements in autoML, image animation, and GAN theory, this repository serves researchers and developers interested in exploring GAN technology comprehensively. This resource ensures an extensive understanding of GANs' impact across varied domains.