Search Results for "lectures" - Page 2
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Showing 104 open source projects for "lectures"
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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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Statistical Rethinking 2024
This course teaches data analysis
...This version is designed for students following the 2024 lecture series, offering the most current set of examples, exercises, and teaching material aligned with the Statistical Rethinking framework. Online, flipped instruction. I will pre-record the lectures each week. We'll meet online once a week for an hour to discuss the material. The discussion time (3-4pm Berlin Time) should allow people in the Americas to join in their morning. -
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ML YouTube Courses
Discover the latest machine learning / AI courses on YouTube
ML YouTube Courses is a curated index of high-quality machine learning and AI courses available on YouTube, designed to make open education easier to discover and navigate. The repository organizes lectures from top universities and educators into a structured list so learners can quickly find reputable material. It covers a wide range of topics including deep learning, NLP, probabilistic modeling, reinforcement learning, and computer vision. The project reflects DAIR.AI’s broader mission to democratize access to AI education for the global community. ... -
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Statistical Rethinking 2023
Statistical Rethinking Course for Jan-Mar 2023
The 2023 edition modernizes and expands on the same curriculum, adjusting exercises and code for newer versions of R, Stan, and supporting packages. It continues to provide scripts for lectures and tutorials, while integrating refinements to examples, notation, and computational workflows introduced that year. Compared with 2022, some models are rewritten for clarity, and teaching materials reflect refinements in McElreath’s evolving presentation of Bayesian data analysis. Students following the 2023 lecture videos use this repository as their coding reference. ... -
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DevOps-Bootcamp
This repository consists of the code samples, assignments, and notes
...The repository organizes topics around practical DevOps foundations such as Linux, terminal commands, networking, YAML, Docker, GitHub Actions, and Kubernetes. It is designed as a companion resource for learners following the lectures and course website. The structure makes it easier to revisit examples, practice assignments, and connect theory with hands-on tooling. It is best understood as an educational workspace for DevOps beginners who want exposure to modern infrastructure, automation, and cloud-native practices. -
$300 Free Credits to Build on Google CloudPut your $300 in credit toward real workloads, then keep building with free monthly usage for 20+ products. No commitment and no charge until you upgrade.
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Draft Notes
Grab screen areas and note it with a mouse, pen & keyboad quickly.
Especially for learners watching some lectures online or using video capturing devices. Grab screen areas with a click (or tap), automatically save it and allows adding notes on it using mouse, pen and keyboard - very quick (and draft). Then it is possible to view grabbed screenshots and annotate it additionally with the same set of tools, easy and convenient. May be help to somebody these sad days. -
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ML Course Notes
Collaborative machine learning lecture notes from top AI courses
...These notes cover subjects such as supervised learning, deep learning, neural networks, natural language processing, and reinforcement learning. ML-Course-Notes organizes content according to specific courses and lectures, allowing learners to navigate through structured educational material more easily. Some sections include summaries of lectures from widely known machine learning and deep learning courses, while other sections are still marked as work in progress as contributors continue expanding the content. It aims to make complex AI and machine learning topics more accessible by providing concise written explanations and structured notes. -
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VSCode-LaTeX-Inkscape
A way to integrate LaTeX, VS Code, and Inkscape in macOS
...I use LaTeX heavily for both academic work and professional work, and I think I'm quite proficient in terms of typing things out in LaTeX. But when I see the mind-blowing blog posts from Gilles Castel (RIP)-How I'm able to take notes in mathematics lectures using LaTeX and Vim and also How I draw figures for my mathematical lecture notes using Inkscape, I realize that I'm still far from fast, so I decided to adapt the whole setup from Linux-Vim to macOS-VS Code. -
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TeachYourselfCS-CN
A Chinese translation of TeachYourselfCS
...Topics include programming, computer architecture, algorithms, mathematics, operating systems, networking, databases, compilers, and distributed systems. Each subject explains why it matters and recommends books and video lectures for deeper study. The repository also retains the English version, sequencing advice, FAQs, and guidance for building a long-term self-study plan. -
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Statistical Rethinking 2022
Statistical Rethinking course winter 2022
...The code emphasizes Bayesian data analysis using R, the rethinking package, and Stan models. It includes lecture code files, example datasets, and structured exercises that parallel the topics covered in the lectures (probability, regression, model comparison, Bayesian updating). The repo functions as a direct hands-on reference for students following the 2022 recorded lecture series. There are 10 weeks of instruction. Links to lecture recordings will appear in this table. Weekly problem sets are assigned on Fridays and due the next Friday, when we discuss the solutions in the weekly online meeting. -
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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Deep Learning course
Slides and Jupyter notebooks for the Deep Learning lectures
Slides and Jupyter notebooks for the Deep Learning lectures at Master Year 2 Data Science from Institut Polytechnique de Paris. This course is being taught at as part of Master Year 2 Data Science IP-Paris. Note: press "P" to display the presenter's notes that include some comments and additional references. This lecture is built and maintained by Olivier Grisel and Charles Ollion. -
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Awesome Conformal Prediction
A professionally curated list of awesome Conformal Prediction videos
awesome-conformal-prediction is a curated “awesome list” repository on GitHub collecting high-quality resources related to conformal prediction: tutorials, books, papers, theses, open-source libraries, videos, and other educational material. It is not a software library itself but a directory of resources for those wanting to learn or work with conformal prediction and uncertainty quantification. This exceptional resource is the culmination of my PhD journey in Machine Learning, specializing... -
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go-fundamental-programming
Set of video and voice tutorials for the Go language
...The author positions it explicitly as a way to share the knowledge and pitfalls accumulated while learning Go, so that new learners can avoid common mistakes and weird corner cases. The course is delivered as a series of lectures, and each lecture has a dedicated lectureX.md file that serves as classroom notes, listing the knowledge points covered and the timestamp at which each segment starts. This structure allows students to quickly jump to specific topics in the video without scrubbing blindly through the timeline. The syllabus goes from environment setup and basic syntax to types and variables, constants and operators, control statements, arrays, slices, maps, functions, structs, methods, interfaces, reflection, and concurrency, before finishing with a “projects and pitfalls” session. -
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mlcourse.ai
Open Machine Learning Course
...Having both a Ph.D. degree in applied math and a Kaggle Competitions Master tier, Yury aimed at designing an ML course with a perfect balance between theory and practice. Thus, the course meets you with math formulae in lectures, and a lot of practice in a form of assignments and Kaggle Inclass competitions. Currently, the course is in a self-paced mode. Here we guide you through the self-paced mlcourse.ai. -
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College Time Table (CTT)
A small and powerful utility to generate school/college time table.
Most of the time table applications use complex design to generate time table but as a result often lose simplicity. This software uses the most basic, simple and popular Spreadsheet design. This small desktop application can save hundreds of hours of teachers of any small school or college. It will never ask information required but collects information on the fly as you type. Start building time table instantly as you recollect names, subjects etc. Separate executables for Windows... -
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MIT-6.824
Basic Sources for MIT 6.824 Distributed Systems Class
MIT-6.824 is a study repository containing source code and supporting material for MIT's distributed systems course. Its materials are based on the 2021 version of the class and organize resources around lectures and practical exercises. Topics include MapReduce, remote procedure calls, threads, the Google File System, primary-backup replication, and Raft consensus. The repository includes Go-based exercises such as crawler, key-value, and MapReduce labs. It also collects lecture questions intended to reinforce distributed systems concepts and paper discussions. ... -
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SLAMBook-en
The English version of 14 lectures on visual SLAM
This project is the English version of “14 Lectures on Visual SLAM: From Theory to Practice,” a text and teaching resource about visual simultaneous localization and mapping (SLAM). It provides the full LaTeX source (formerly Markdown) for all 14 chapters, letting readers compile and study the material systematically. Within the repository you’ll find organized subfolders (e.g. chapters, latex, resources) containing the lecture contents, references, figures, and supporting assets for each part of the course. ... -
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jekyll-course-website-template
Feature-rich and easy-to-use Jekyll website template
Feature-rich and easy-to-use Jekyll website template for academic courses. Individual page for assignments, lectures, course material, and course schedule. Auto-generated course updates section (for each new lecture and assignment) + custom/manual announcements. -
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deep2Read
This website includes a (growing) list of papers and lectures
As a group, we need to improve our knowledge of the fast-growing field of deep learning. To educate students in our graduate programs, to help new members in my team with basic tutorials, and to help current members understand advanced topics better, this website includes a (growing) list of tutorials and papers we survey for such a purpose. We hope this website helps people who share similar research interests or those interested in learning advanced topics about deep learning. -
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Deep Learning Drizzle
Drench yourself in Deep Learning, Reinforcement Learning
Drench yourself in Deep Learning, Reinforcement Learning, Machine Learning, Computer Vision, and NLP by learning from these exciting lectures! Optimization courses which form the foundation for ML, DL, RL. Computer Vision courses which are DL & ML heavy. Speech recognition courses which are DL heavy. Structured Courses on Geometric, Graph Neural Networks. Section on Autonomous Vehicles. Section on Computer Graphics with ML/DL focus. -
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Deep Learning with PyTorch
Latest techniques in deep learning and representation learning
This course concerns the latest techniques in deep learning and representation learning, focusing on supervised and unsupervised deep learning, embedding methods, metric learning, convolutional and recurrent nets, with applications to computer vision, natural language understanding, and speech recognition. The prerequisites include DS-GA 1001 Intro to Data Science or a graduate-level machine learning course. To be able to follow the exercises, you are going to need a laptop with Miniconda (a... -
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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... -
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HIERDENC
Clustering of categorical data sets with locality-sensitive hashing
...The clustering is achieved via a locality-sensitive hashing of categorical datasets for speed and scalability. The locality-sensitive hashing method implemented is described in the video lectures under www.mmds.org (Chapter 3). Information needed for LSH, such as shingles/tokens, MinHash signatures, band hashes to buckets are stored in several database tables. Information needed for clustering purposes, such as the most significant pairwise object similarities and density-based similarities are also stored in tables. ... -
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Learn_Data_Science_in_3_Months
This is the Curriculum for "Learn Data Science in 3 Months"
This project lays out a 12-week plan to go from basics to a portfolio-ready understanding of data science. It breaks the journey into clear stages: Python fundamentals, data wrangling, visualization, statistics, machine learning, and end-to-end projects. The schedule mixes learning and doing, encouraging you to build small deliverables each week—like notebooks, dashboards, and model demos—to reinforce skills. It also includes suggestions for datasets and problem domains so you aren’t stuck... -
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Automobile Engineering
Get more animated videos of automobile Engineering through Magic Marks
1. When the ignition switch is turned on, the current flows from the battery through the primary winding, ballast register and contact breaker. 2. The flowing current induces a magnetic field directly proportional to it. 3. As the contact breaker opens, the current collapses resulting in high voltage induction in the secondary winding. 4. The high voltage current generated in the secondary winding is transferred to the distributor via a cable with high tension. 5. The distributor... -
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TunesViewer
Easy podcast access in Linux, Android
A small, easy-to-use program to access itunesU media & podcasts in Linux and Android.
