Awesome List Updates on Mar 12, 2016

6 awesome lists updated today.

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1. Awesome Fp Js

Libraries

• claire (⭐79) – A property-based testing library for clearly specifying code invariants and behaviour.

Libraries / Data Structures

• immutable-sequence.js (⭐16) – High performance implementation of Immutable Sequence in JavaScript, based on Finger Trees (⭐45).

• Timm – Immutability helpers with fast reads and acceptable writes.

• Lazy.js (⭐6k) – A utility library with a lazy engine under the hood that strives to do as little work as possible while being as flexible as possible.

Libraries / Algebraic Data Types

• union-type (⭐475) – A small JavaScript library for defining and using union types.

• freeky (⭐175) – A collection of Free monads.

Resources / Articles

• Functional programming – Many articles on various aspects of functional programming in JavaScript by Gleb Bahmutov.

Resources / Videos

• Classroom Coding with Prof. Frisby – A series that builds a “practical” web application with React and functional programming in JavaScript.

2. Awesome Microservices

Python / Scala

• Flask - Python framework for microservices based on Werkzeug and Jinja 2.

3. Js Must Watch

2015

• Dan Abramov: Live React: Hot Reloading with Time Travel [30:40]

2016

• Brendan Eich: JavaScript in 2016: Beyond Harmony [15:31]

4. Awesome Ruby

Third-party APIs

• simple-slack-bot (⭐157) - You can easily make Slack Bot.

5. Awesome Git Addons

git-secret

git secret init

$ git secret init
'.gitsecret/' created.

git secret add

$ git secret add hideme.txt
1 items added.

git secret list

$ git secret list
hideme.txt

git secret hide

$ git secret hide
done. all 1 files are hidden.

6. Awesome Courses

Courses / Machine Learning

• CS 4786 Machine Learning for Data Science Cornell University
  • An introductory course in machine learning, with a focus on data modeling and related methods and learning algorithms for data sciences. Tentative topic list:
    • Dimensionality reduction, such as principal component analysis (PCA) and the singular value decomposition (SVD), canonical correlation analysis (CCA), independent component analysis (ICA), compressed sensing, random projection, the information bottleneck. (We expect to cover some, but probably not all, of these topics).
    • Clustering, such as k-means, Gaussian mixture models, the expectation-maximization (EM) algorithm, link-based clustering. (We do not expect to cover hierarchical or spectral clustering.).
    • Probabilistic-modeling topics such as graphical models, latent-variable models, inference (e.g., belief propagation), parameter learning.
    • Regression will be covered if time permits.
  • Assignments
  • Lectures

• EECS E6893 & EECS E6895 Big Data Analytics & Advanced Big Data Analytics Columbia University
  • Students will gain knowledge on analyzing Big Data. It serves as an introductory course for graduate students who are expecting to face Big Data storage, processing, analysis, visualization, and application issues on both workplaces and research environments.
  • Taught by Dr. Ching-Yung Lin
  • Course Site
  • Assignments - Assignments are present in the Course Slides

• Info 290 Analyzing Big Data with Twitter UC Berkeley school of information
  • In this course, UC Berkeley professors and Twitter engineers provide lectures on the most cutting-edge algorithms and software tools for data analytics as applied to Twitter's data. Topics include applied natural language processing algorithms such as sentiment analysis, large scale anomaly detection, real-time search, information diffusion and outbreak detection, trend detection in social streams, recommendation algorithms, and advanced frameworks for distributed computing.
  • Lecture Videos
  • Previous Years coursepage

Courses / Computer Graphics

• CS 378 (⭐77) 3D Reconstruction with Computer Vision UTexas
  • In this lab-based class, we'll dive into practical applications of 3D reconstruction, combining hardware and software to build our own 3D environments from scratch. We'll use open-source frameworks like OpenCV to do the heavy lifting, with the focus on understanding and applying state-of-the art approaches to geometric computer vision
  • Lectures (⭐77)

• CS 4620 Introduction to Computer Graphics Cornell University
  • The study of creating, manipulating, and using visual images in the computer.
  • Assignments
  • Exams

• CS 4670 Introduction to Computer Vision Cornell University
  • This course will provide an introduction to computer vision, with topics including image formation, feature detection, motion estimation, image mosaics, 3D shape reconstruction, and object and face detection and recognition. Applications of these techniques include building 3D maps, creating virtual characters, organizing photo and video databases, human computer interaction, video surveillance, automatic vehicle navigation, and mobile computer vision. This is a project-based course, in which you will implement several computer vision algorithms throughout the semester.
  • Assignments
  • Lectures

• CS 6670 Computer Vision Cornell University
  • Introduction to computer vision. Topics include edge detection, image segmentation, stereopsis, motion and optical flow, image mosaics, 3D shape reconstruction, and object recognition. Students are required to implement several of the algorithms covered in the course and complete a final project.
  • Syllabus
  • Lectures
  • Assignments

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• Next: Mar 11, 2016