Search Results for "technical help customers"
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Showing 22 open source projects for "technical help customers"
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Build Agents and Models on One PlatformGemini Enterprise Agent Platform is Google Cloud's comprehensive platform for developers to build, scale, govern, and optimize agents and models. Choose from Google's most advanced models and third-party models like Anthropic's Claude Model Family.
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MongoDB Atlas runs apps anywhereMongoDB Atlas gives you the freedom to build and run modern applications anywhere—across AWS, Azure, and Google Cloud. With global availability in over 115 regions, Atlas lets you deploy close to your users, meet compliance needs, and scale with confidence across any geography.
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AIGC-Interview-Book
AIGC algorithm engineer interview secrets
AIGC-Interview-Book is a large educational repository designed to help engineers prepare for technical interviews related to artificial intelligence and generative AI roles. The project compiles knowledge from industry practitioners and researchers into a structured reference covering the AI ecosystem. Topics included in the repository span large language models, generative AI systems, traditional deep learning methods, reinforcement learning, computer vision, natural language processing, and machine learning theory. ... -
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AI_Tutorial
A selection of learning materials, search, recommendation, advertising
AI_Tutorial is a large curated repository that aggregates high-quality learning resources related to artificial intelligence, machine learning, deep learning, natural language processing, and data engineering. The project functions as a centralized knowledge base designed to help engineers and researchers discover tutorials, technical articles, algorithm explanations, and architecture discussions from across the AI ecosystem. Rather than focusing on a single framework or course, the repository collects materials from many sources such as open-source projects, technical blogs, research papers, and industry engineering posts. ... -
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learning
A log of things I'm learning
...The content is organized into categories that cover both core engineering skills and adjacent technologies, enabling readers to follow a practical roadmap for developing strong technical foundations. The repository emphasizes clear explanations, curated resources, and concise notes designed to help developers learn complex topics efficiently. Because it is updated regularly, it reflects evolving trends in software engineering and emerging technologies such as modern AI systems. -
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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. The repository also includes example implementations and explanatory materials that help readers understand the mechanics behind machine learning and NLP algorithms. ... -
Train ML Models With SQL You Already KnowBuild and deploy ML models using familiar SQL. Automate data prep with built-in Gemini. Query 1 TB and store 10 GB free monthly.
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Deep Learning Interviews book
Hundreds of fully solved job interview questions
The interviews.ai repository hosts the open materials for the book Deep Learning Interviews, a comprehensive collection of technical questions and fully solved problems covering many aspects of artificial intelligence. The project was created to help students, researchers, and engineers prepare for machine learning and deep learning interviews by providing structured explanations of key concepts. The repository organizes problems across topics such as neural networks, optimization, probabilistic models, and mathematical foundations of machine learning. ... -
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DATA SCIENCE ROADMAP
Data Science Roadmap from A to Z
...Topics typically include programming with Python, statistics, mathematics, machine learning algorithms, data visualization, and big data technologies. The roadmap also includes links to courses, tutorials, and external resources that help learners study each topic in more depth. By organizing these subjects into a logical sequence, the repository helps beginners understand how different technical skills connect within the broader data science workflow. The roadmap format makes it easy for learners to track their progress as they move from foundational concepts to more advanced techniques. -
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cracking-the-data-science-interview
A Collection of Cheatsheets, Books, Questions, and Portfolio
...The project organizes content across many fundamental areas of data science, including statistics, probability, SQL, machine learning, and deep learning. It includes cheat sheets that summarize important technical concepts commonly discussed during technical interviews. The repository also provides links to recommended books, tutorials, practice platforms, and blog posts that help learners strengthen their theoretical and practical skills. In addition to conceptual study materials, the project includes interview question banks and case study prompts that simulate real hiring scenarios. ... -
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The Machine & Deep Learning Compendium
List of references in my private & single document
...The project functions as a comprehensive compendium that organizes hundreds of topics covering algorithms, frameworks, research areas, and practical machine learning workflows. Originally created as a personal knowledge base, the repository evolved into a public educational resource designed to help learners explore the rapidly expanding machine learning ecosystem. The compendium includes explanations of concepts across multiple domains such as natural language processing, computer vision, time-series analysis, anomaly detection, and graph learning. In addition to technical algorithms, the project also covers practical topics related to data science workflows, engineering practices, and product development in AI systems. -
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Data-Science-Interview-Questions-Answers
Curated list of data science interview questions and answers
Data-Science-Interview-Questions-Answers is a curated educational repository designed to help data science candidates prepare for technical interviews by organizing a large bank of questions and answers in one place. It began as a daily interview question initiative and was later consolidated into GitHub so learners could review the material more easily and revisit it over time. The repository focuses on core data science fundamentals rather than acting as a software framework, which makes it especially useful as a study and revision resource. ... -
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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Datasets
Hub of ready-to-use datasets for ML models
Datasets is a library for easily accessing and sharing datasets, and evaluation metrics for Natural Language Processing (NLP), computer vision, and audio tasks. Load a dataset in a single line of code, and use our powerful data processing methods to quickly get your dataset ready for training in a deep learning model. Backed by the Apache Arrow format, process large datasets with zero-copy reads without any memory constraints for optimal speed and efficiency. We also feature a deep... -
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Start Machine Learning in 2026
A complete guide to start and improve in machine learning
Start Machine Learning in 2026 repository is an open educational guide designed to help beginners enter the field of machine learning and artificial intelligence with little or no prior technical background. The project organizes a large collection of learning resources, including online courses, books, tutorials, research articles, and video lectures that explain fundamental AI concepts. Its structure functions as a learning roadmap that gradually introduces essential topics such as programming, mathematics, statistics, neural networks, and modern deep learning techniques. ... -
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Ai-Learn
The artificial intelligence learning roadmap compiles 200 cases
Ai-Learn is an open-source artificial intelligence learning roadmap that aggregates educational materials, tutorials, and practical projects designed to help beginners study AI and machine learning systematically. The repository was created to help learners start self-study programs in artificial intelligence without getting overwhelmed by the large number of available resources. It organizes topics such as Python programming, mathematics for machine learning, data analysis, deep learning,... -
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2020 Machine Learning Roadmap
A roadmap connecting many of the most important concepts
...It outlines the typical workflow of solving machine learning problems, starting from problem formulation and data preparation to model training and evaluation. The roadmap also highlights the major technologies and frameworks commonly used in machine learning development. In addition to describing technical tools, the project includes recommended learning resources that help users study the underlying mathematics and algorithms behind machine learning systems. The roadmap is often used as a high-level orientation tool for beginners who want to understand the broader landscape of machine learning. -
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Data Science Collected Resources
Carefully curated resource links for data science in one place
...It also contains supplementary documents like cheat sheets and machine learning notes that help readers review important concepts quickly. -
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Data Science and Machine Learning Career
Career Resources for Data Science, Machine Learning and Big Data
Data Science and Machine Learning Career is a curated career resource for people pursuing analytical and technical roles. It gathers job platforms, employer review sites, respected courses, competitions, tools, skill areas, and bootcamps in one place. The repository also organizes podcasts for different experience levels and lists common career paths across data science, analytics, engineering, and business intelligence. Community groups and professional networks help readers find peers and industry discussions. ... -
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Image Quality Assessment
Convolutional Neural Networks to predict aesthetic quality of images
...Instead of relying on simple image statistics, the system learns patterns that correlate with human judgments about image aesthetics and technical quality. The repository includes code for training models, performing inference, and evaluating predicted scores against labeled datasets. It also provides utilities for image preprocessing and data management that help prepare datasets for training deep learning models. -
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spark-ml-source-analysis
Spark ml algorithm principle analysis and specific source code
spark-ml-source-analysis is a technical repository that analyzes the internal implementation of machine learning algorithms within Apache Spark’s MLlib library. The project aims to help developers and data scientists understand how distributed machine learning algorithms are implemented and optimized inside the Spark ecosystem. Instead of providing a runnable software system, the repository focuses on explaining algorithm principles and examining the underlying source code used in Spark’s machine learning package. ... -
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Machine Learning Yearning
Machine Learning Yearning
...Professor Andrew Ng is currently writing a book on how to build machine learning projects. The point of this book is not to teach traditional machine learning algorithms, but to teach you how to make machine learning algorithms work. Some technical courses in AI will give you a tool, and this book will teach you how to use those tools. If you aspire to be a technical leader in AI and want to learn how to set a direction for your team, this book will help. This book is still a sample draft. In order to make the corresponding Chinese content available to you as soon as possible, the translation time is rushed, and some of the content is inevitably oversight. ... -
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Machine learning Resources
Some learning materials and research introduction on machine learning
Machine learning Resources is an educational GitHub repository that collects resources, tutorials, and implementation examples related to machine learning theory and practice. The project aims to help learners understand machine learning from both conceptual and practical perspectives by combining explanations, research references, and coding examples. It serves as a curated knowledge base that introduces fundamental algorithms and techniques used in modern machine learning systems. The... -
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Lihang
Statistical learning methods (2nd edition) [Li Hang]
...In addition to code examples, the project contains supplementary materials such as formula references, glossaries of technical terms, and documentation explaining mathematical notation used throughout the algorithms. The repository also provides links to related research papers and references that expand on the theoretical background presented in the book. -
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Deep Learning
Deep Learning Book Chinese Translation
With the help and proofreading of many netizens, the Chinese version was finally published. Although there are still many problems, at least 90% of the content is readable and accurate. We have preserved the meaning of the original book Deep Learning as much as possible and retained the original language of the book. However, our level is limited, and we cannot eliminate the variance of many readers. We still need everyone's advice and help to reduce translation bias together. All you have... -
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Convolution arithmetic
A technical report on convolution arithmetic in deep learning
A technical report on convolution arithmetic in the context of deep learning. The code and the images of this tutorial are free to use as regulated by the licence and subject to proper attribution. The animations will be output to the gif directory. Individual animation steps will be output in PDF format to the pdf directory and in PNG format to the png directory.
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