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Showing 15 open source projects for "review"
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Coursera-ML-AndrewNg-Notes
Personal notes from Wu Enda's machine learning course
...The repository often expands on the original lecture material by adding additional explanations, diagrams, and formulas that clarify the theoretical foundations of the algorithms. These notes serve as a structured reference that learners can review while studying or revisiting machine learning fundamentals. -
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FSRS4Anki
A modern Anki custom scheduling based on Free Spaced Repetition
A modern spaced-repetition scheduler for Anki based on the Free Spaced Repetition Scheduler algorithm. -
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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. Its content is organized into subject-specific documents that cover machine learning, deep learning, statistics, probability, Python, SQL and databases, and resume-based interview questions. ... -
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Andrew NG Notes Collection
This is Andrew NG Coursera Handwritten Notes
...The notes aim to simplify complex mathematical explanations by organizing concepts into clear sections with diagrams, formulas, and concise descriptions. Each chapter mirrors the structure of the course curriculum, allowing students to review the material in a systematic way while following along with the lectures. The repository emphasizes conceptual clarity and practical understanding, helping learners connect mathematical foundations with real machine learning applications. -
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Data Science Interviews
Data science interview questions and answers
Data Science Interviews is an open-source repository that collects common data science interview questions along with community-provided answers and explanations. The project serves as a preparation resource for students, job seekers, and professionals who want to review the technical knowledge required for data science roles. The repository organizes questions into different categories including theoretical machine learning concepts, technical programming questions, and probability or statistics problems. Many of the questions cover fundamental machine learning topics such as linear models, decision trees, neural networks, and evaluation metrics. ... -
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Audio AI Timeline
A timeline of the latest AI models for audio generation
Audio AI Timeline is a curated project that organizes the development of audio-related artificial intelligence into a structured and accessible historical timeline. Rather than functioning as a model training framework, it serves as an informational resource that maps key papers, systems, models, datasets, and milestones across areas such as speech synthesis, music generation, audio understanding, source separation, and general audio machine learning. The project helps users understand how... -
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daily-paper-computer-vision
Document papers compiled daily in computer vision/deep learning
This repo is a running feed of computer-vision research, tracking new papers and notable results so practitioners can keep up without scouring multiple sites. It’s organized chronologically and often thematically, making it easy to scan what’s new in detection, segmentation, recognition, generative vision, 3D, and video understanding. The cadence is intentionally frequent, reflecting how quickly CV advances and how hard it is to maintain awareness while working full time. By aggregating... -
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Data Science Collected Resources
Carefully curated resource links for data science in one place
...The repository includes links to materials discussing topics such as artificial intelligence research, AWS infrastructure, machine learning algorithms, and data analysis tools. It also contains supplementary documents like cheat sheets and machine learning notes that help readers review important concepts quickly. -
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AWS Step Functions Data Science SDK
For building machine learning (ML) workflows and pipelines on AWS
...You can create machine learning workflows in Python that orchestrate AWS infrastructure at scale, without having to provision and integrate the AWS services separately. The best way to quickly review how the AWS Step Functions Data Science SDK works is to review the related example notebooks. These notebooks provide code and descriptions for creating and running workflows in AWS Step Functions Using the AWS Step Functions Data Science SDK. In Amazon SageMaker, example Jupyter notebooks are available in the example notebooks portion of a notebook instance. ... -
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Machine-Learning-Notes
Zhou Zhihua's "Machine Learning" push notes
The Machine-Learning-Notes repository contains detailed handwritten-style study notes based on the popular machine learning textbook by Zhou Zhihua. The project focuses on deriving formulas and explaining algorithms step by step so that learners can understand the mathematical foundations behind machine learning methods. The notes span sixteen chapters that cover a wide range of topics, including model evaluation, linear models, decision trees, neural networks, support vector machines,... -
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Machine Learning cheatsheets Stanford
VIP cheatsheets for Stanford's CS 229 Machine Learning
stanford-cs-229-machine-learning is an open-source educational repository that provides illustrated cheat sheets summarizing the key concepts taught in Stanford University’s CS229 machine learning course. The project compiles concise explanations of important topics in machine learning and presents them in an accessible format that helps learners review complex ideas quickly. The repository includes summaries covering areas such as supervised learning, unsupervised learning, deep learning, and optimization techniques. In addition to machine learning algorithms, it also contains refresher materials on mathematical prerequisites including probability theory, statistics, linear algebra, and calculus. ... -
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AI Cheatsheets
Essential Cheat Sheets for deep learning and machine learning research
cheatsheets-ai is an open-source repository that collects essential cheat sheets covering many tools and concepts used in machine learning, deep learning, and data science. The project aims to provide quick-reference materials that help engineers, researchers, and students review key techniques and frameworks without reading extensive documentation. It compiles cheat sheets for widely used libraries and technologies such as TensorFlow, Keras, NumPy, Pandas, Scikit-learn, Matplotlib, and PySpark. These materials summarize common functions, workflows, and best practices in a concise visual format that makes them easy to consult during development or study sessions. ... -
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Coursera Machine Learning
Coursera Machine Learning By Prof. Andrew Ng
CourseraMachineLearning is a personal collection of resources, notes, and programming exercises from Andrew Ng’s popular Machine Learning course on Coursera. It consolidates lecture references, programming tutorials, test cases, and supporting materials into one repository for easier review and practice. The project highlights fundamental machine learning concepts such as hypothesis functions, cost functions, gradient descent, bias-variance tradeoffs, and regression models. It also organizes week-by-week course schedules with links to exercises, lecture notes, and additional resources. Alongside the official coursework, the repository includes supplemental explanations, code snippets, and references to recommended textbooks and external materials. ... -
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The Deep Review
A collaboratively written review paper on deep learning, genomics, etc
This repository is home to the Deep Review, a review article on deep learning in precision medicine. The Deep Review is collaboratively written on GitHub using a tool called Manubot (see below). The project operates on an open contribution model, welcoming contributions from anyone. To see what's incoming, check the open pull requests. For project discussion and planning see the Issues. -
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Community Detection Modularity Suite
Suite of community detection algorithms based on Modularity
... - Main suite containing three community detection algorithms based on the Modularity measure containing: Geodesic and Random Walk edge Betweenness [1] and Spectral Modularity [2]. Collaborator: Theologos Kotsos. [1] M. Newman & M. Girvan, Physical Review, E 69 (026113), 2004. [2] M. Newman, Physical Review E, 74(3):036104, 2006. [3] B. Ball et al, An efficient and principled method for detecting communities in networks, 2011. The suite is based upon the fast community algorithm implemented by Aaron Clauset <aaron@cs.unm.edu>, Chris Moore, Mark Newman, and the R IGraph library Copyright (C) 2007 Gabor Csardi <csardi@rmki.kfki.hu>. ...
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