Awesome List Updates on Oct 19, 2022

13 awesome lists updated today.

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1. Awesome Dotnet

Template Engine / GUI - other

• Handlebars.Net (⭐1.4k) - A real .NET Handlebars engine

2. Awesome Jquery

Miscellaneous Resources / Paid Books

• HTML Tutorial by Scaler

3. Awesome Icons

Archive of Icons

• iconarchive - Professional tag based icon search engine with more than 600,000 icons.

Generic

• Lucide - A fork of Feather Icons with more than 500 additional icons.

4. Awesome Gatling

Videos / Talks

• Load Testing Crash Course with Gatling - Stéphane Landelle @ Devoxx Belgium 2022.

5. Awesome Zsh Plugins

Plugins / superconsole - Windows-only

• psgrep (⭐0) - Makes ps grep hide its own process from the results of a ps aux | grep.

6. Awesome Vscode

Rust / Smarty Template Support

• rust-analyzer - Linting, auto-completion, code formatting, snippets and more

7. Awesome Docker

Container Composition

• box (⭐242) 💀 - Build Dockerfile images with a mruby DSL, includes flattening and layer manipulation

8. Awesome Vue

Components & Libraries / Utilities

• vue-vroom (⭐11) - A plugin for REST APIs, that lets you quickly generate type safe stores and a mock API with minimal config.

9. Awesome Terraform

Tools / Community providers

• Coder - Coder provisions software development environments on your infrastructure via Terraform.

10. Awesome Courses

Courses / Systems

• CS 162 Operating Systems and Systems Programming UC Berkeley
  • The purpose of this course is to teach the design of operating systems and operating systems concepts that appear in other advanced systems. Topics we will cover include concepts of operating systems, systems programming, networked and distributed systems, and storage systems, including multiple-program systems (processes, interprocess communication, and synchronization), memory allocation (segmentation, paging), resource allocation and scheduling, file systems, basic networking (sockets, layering, APIs, reliability), transactions, security, and privacy.
    • Operating Systems course by the Chair of EECS, UC Berkeley David Culler
    • Lecture Videos Spring 2015 lectures
    • Lecture Notes Spring 2015 lectures

• CS 168 Introduction to the Internet: Architecture and Protocols UC Berkeley
  • This course is an introduction to the Internet architecture. We will focus on the concepts and fundamental design principles that have contributed to the Internet's scalability and robustness and survey the various protocols and algorithms used within this architecture. Topics include layering, addressing, intradomain routing, interdomain routing, reliable delivery, congestion control, and the core protocols (e.g., TCP, UDP, IP, DNS, and HTTP) and network technologies (e.g., Ethernet, wireless).
  • Lecture Notes & Assignments
  • Discussion Notes

• CSCI-UA.0202: Operating Systems (Undergrad) Operating Systems NYU
  • NYU's operating system course. It's a fundamental course focusing basic ideas of operating systems, including memory management, process scheduling, file system, ect. It also includes some recommended reading materials. What's more, there are a series of hands-on lab materials, helping you easily understand OS.
  • Assignments
  • Lectures
  • Old Exams

Courses / Machine Learning

• CS 287 Advanced Robotics UC Berkeley
  • The course introduces the math and algorithms underneath state-of-the-art robotic systems. The majority of these techniques are heavily based on probabilistic reasoning and optimization---two areas with wide applicability in modern Artificial Intelligence. An intended side-effect of the course is to generally strengthen your expertise in these two areas.
  • Lectures Notes
  • Assignments

• Machine Learning: 2014-2015 University of Oxford
  • The course focusses on neural networks and uses the Torch (⭐9.1k) deep learning library (implemented in Lua) for exercises and assignments. Topics include: logistic regression, back-propagation, convolutional neural networks, max-margin learning, siamese networks, recurrent neural networks, LSTMs, hand-writing with recurrent neural networks, variational autoencoders and image generation and reinforcement learning
  • Lectures and Assignments
  • Source code

• 10-708 Probabilistic Graphical Models Carnegie Mellon University
  • Many of the problems in artificial intelligence, statistics, computer systems, computer vision, natural language processing, and computational biology, among many other fields, can be viewed as the search for a coherent global conclusion from local information. The probabilistic graphical models framework provides a unified view for this wide range of problems, enabling efficient inference, decision-making and learning in problems with a very large number of attributes and huge datasets. This graduate-level course will provide you with a strong foundation for both applying graphical models to complex problems and for addressing core research topics in graphical models.
  • Lecture Videos
  • Assignments
  • Lecture notes
  • Readings

Courses / Artificial Intelligence

• CS 188 Introduction to Artificial Intelligence UC Berkeley
  • This course will introduce the basic ideas and techniques underlying the design of intelligent computer systems. A specific emphasis will be on the statistical and decision-theoretic modeling paradigm. By the end of this course, you will have built autonomous agents that efficiently make decisions in fully informed, partially observable and adversarial settings. Your agents will draw inferences in uncertain environments and optimize actions for arbitrary reward structures. Your machine learning algorithms will classify handwritten digits and photographs. The techniques you learn in this course apply to a wide variety of artificial intelligence problems and will serve as the foundation for further study in any application area you choose to pursue.
  • Lectures
  • Projects
  • Exams

• 6.868J The Society of Mind MIT
  • This course is an introduction, by Prof. Marvin Minsky, to the theory that tries to explain how minds are made from collections of simpler processes. It treats such aspects of thinking as vision, language, learning, reasoning, memory, consciousness, ideals, emotions, and personality. It incorporates ideas from psychology, artificial intelligence, and computer science to resolve theoretical issues such as wholes vs. parts, structural vs. functional descriptions, declarative vs. procedural representations, symbolic vs. connectionist models, and logical vs. common-sense theories of learning.
  • Lectures
  • Assignments
  • Readings

Courses / Computer Graphics

• CMU 462 Computer Graphics Carnegie Mellon University
  • This course provides a comprehensive introduction to computer graphics. Focuses on fundamental concepts and techniques, and their cross-cutting relationship to multiple problem domains in graphics (rendering, animation, geometry, imaging). Topics include: sampling, aliasing, interpolation, rasterization, geometric transformations, parameterization, visibility, compositing, filtering, convolution, curves & surfaces, geometric data structures, subdivision, meshing, spatial hierarchies, ray tracing, radiometry, reflectance, light fields, geometric optics, Monte Carlo rendering, importance sampling, camera models, high-performance ray tracing, differential equations, time integration, numerical differentiation, physically-based animation, optimization, numerical linear algebra, inverse kinematics, Fourier methods, data fitting, example-based synthesis.
  • Lectures and Readings
  • Assignments and Quizes

Courses / Misc

• AM 207 Monte Carlo Methods and Stochastic Optimization Harvard University
  • This course introduces important principles of Monte Carlo techniques and demonstrates the power of these techniques with simple (but very useful) applications. All of this in Python!
  • Lecture Videos
  • Assignments
  • Lecture Notes

• CS 168 Computer Networks UC Berkeley
  • This is an undergraduate level course covering the fundamental concepts of networking as embodied in the Internet. The course will cover a wide range of topics; see the lecture schedule for more details. While the class has a textbook, we will not follow its order of presentation but will instead use the text as a reference when covering each individual topic. The course will also have several projects that involve programming (in Python).
  • You should know programming, data structures, and software engineering. In terms of mathematics, your algebra should be very solid, you need to know basic probability, and you should be comfortable with thinking abstractly. The TAs will spend very little time reviewing material that is not specific to networking. We assume that you either know the material covered in those courses, or are willing to learn the material as necessary. We won't cover any of this material in lecture.

• CS 193p Developing Applications for iOS Stanford University
  • Updated for iOS 7. Tools and APIs required to build applications for the iPhone and iPad platform using the iOS SDK. User interface designs for mobile devices and unique user interactions using multi-touch technologies. Object-oriented design using model-view-controller paradigm, memory management, Objective-C programming language. Other topics include: object-oriented database API, animation, multi-threading and performance considerations.
  • Prerequisites: C language and object-oriented programming experience
  • Recommended: Programming Abstractions
  • Updated courses for iOS8 - Swift
  • Updated courses for iOS9 - Swift

11. Awesome Agi Cocosci

Science Logology / Literature Mining

• Galex: Exploring the evolution and intersection of disciplines - IEEE Transactions on Visualization and Computer Graphics, 2019. [All Versions].

12. Awesome Datascience

General Machine Learning Packages / Deep Learning architectures

• scikit-survival

Books / Visualization Tools

• The Elements of Statistical Learning: Data Mining, Inference, and Prediction

13. Awesome Newsletters

Ruby / Svelte

• Women on Rails Newsletter. A bi-monthly newsletter about Ruby on Rails and the web. Available in English, French, Spanish and Italian.

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