Reference material
Content: Networking, Programming, Learning
- Intent Based Network Programming NetKAT, Cornell,Tierless Programming, Slick Control Plane - Jennifer Rexford, SoftCell, StEERING, MeasuRouting, Logic Programming: Princeton, FRESCO: Texas A & M, Kinetic, Network Intent Composition: Open Day Light
- The Outage Archives
- NANOG Mailing List
- Routing Table Report - BGP Routing Table Analysis
- Lets build a simple interpreter
- [Let's build a compiler'] (http://compilers.iecc.com/crenshaw/) - Jack Crenshaw
- Compiler Fundamentals
- Compilers Marc Feeley - 300 lines C, GitHub Resources
- CPython internals: A ten-hour codewalk through the Python interpreter source code
- Mini: Interpreter and language implemented in Python
- Compiler Construction Resources - GitHub, Stack Overflow
- DIY: Make Your Own Programming Language - Mattias Appelgren
- Crafting Interpreters
- CS Fundamentals - DSA, System Design links
- Neetcode.io - Problems by data structure, with links to relevant videos
- Kotlin Algorithms Videos
- [Kotlin Algorithm Club] (https://github.com/bmaslakov/kotlin-algorithm-club) - Code for various data structures and algorithms
- Comparisons of text books on algorithms - Sedgewick is not included in the comparison - however, it should be an easy read.
- Algorithms etc - Jeff Erickson, UIUC
- Visualization of algorithms, Visualization - UoSF
- Algorithm Visualizer
- Quora: Algorithms and Data Structures Learning Resources - 1, 2
- DsAlgo, daqwest, Geeks for Geeks
- Big O Notations Stack Overflow, Big O Cheat Sheet
- Courses on Algorithms - Videos - Course by Shai Simonsom, with videos, MIT OCW Videos, Udacity Udacity
- Data Structures - Yale CPSC 223, Visualization - UoSF
- Links to above books on DS and Algorithms on GitHub
- Google Hiring, GitHub Resources, The 30-minute guide to rocking your next coding interview
- Top 30 Data Structures Problems for Technical Interview Preparation, Algorithmica Syllabus
- Top 10 algorithms in Interview Questions Interview Experiences - by company
- 350+ Problems - Algorithms and Data Structures, 500 Data Structures & Algorithms
- Languages on GitHub - Number of active repositories, push per repository etc...
- Comparison of framework performance from TechEmpower
- Language Hackr.io,Welcome to Python for you & me
- Learn X in Y minutes - Python, Go, JavaScript, C, C++, Java, ...
- Decorators Stack Over Flow, Decorators in 12 Easy Steps
- List Comprehensions in Python
- 10 Common Mistakes
- Elements of Python Style
- Python Coding Reddit
- Text Classifier in Python
- [Natural Language Processing with Python] (http://www.nltk.org/book/) - NLTK/Text Processing book
- Statistical Natural Language Processing in Python - Peter Norvig's "How To Do Things With Words. And Counters."
- Overview of Python Visualization Tools Also includes samples for seaborn, usage of powerpoint and excel files
- Python Resources: Awesome Python on GitHub, PyCrumbs on GitHub, DuckDuckGo Cheatsheet, Quick Reference
- Python Virtual Environments
- Why is Python slow
- Why should I use Python 3?
- Understanding logging in Python
- CS 109 Data Science Pandas, Scraping, Probability, Regression, ML, Clustering, Text using Python
- Django Girls, ShowMeDo Videos, Lanyrd 1,2, Tango with Django
- Deploy Django on DO
- 11 Things I Wish I Knew About Django Development Before I Started My Company - HN comments, 11 Killer Features I use in Every Django Project - talks about unit testing, updates to admin model, RESTful interfaces, ...
- Extend Django's built-in User model
- Top 10 mistakes that Django developers make
- Pandas Tutorial from PyDataNYC 2015
- Cheat sheet - Data Wrangling with pandas, Cheatsheet - Data Analysis with Pandas, Cheatsheet - Python For Data Science, Cheatsheet: Plotting with Pandas series and DataFrames, Pandas notebook
- Baby steps in Python – Exploratory analysis in Python (using Pandas) Uses data set from Kaggle
- Scipy Lecture Notes - Tutorial on Python, NumPy, Matplotlib, Scipy, Scikit-learn...
- Introduction to Scientific Python - Stanford Course - Intro to Python, files, NumPy, Scipy, Pyplot
- Kotlin: An Illustrated Guide
- Caster.io: Kotlin Videos
- YouTube: Microservices with Spring Boot and Spring Cloud
- Kotlin, meet gRPC: a new open-source project for modern apps, code on github
- Kotlin and Android Samples
- Developing Spring Boot applications with Kotlin
- Building web applications with Spring Boot and Kotlin
- A thorough introduction to eBPF
- eBPF- Rethinking the Linux kernel
- Inside Kinvolk Labs: Investigating Kubernetes performance issues with BPF
- bpftrace: High-level tracing language for Linux eBPF - Video
- Data Visualization and D3.js
- Scott Murray Comments
- Teaching a Semester of D3.js - Course Material - Lynn Cherny
- One Chart, Twelve Charting Libraries
- Free Data Visualization Books
- Modern JavaScript
- Awesome Interactive Journalism
- Bringing D3.JS to Jupyter Notebook with Py-D3
- Getting Started in Electronics Handwritten notes by Forrest Mims III
- The Art Of Electronics by Paul Horowitz, Winfield Hill
- 1150 Podcasts/Videos across many STEM and other subjects
- Coding the Matrix: Linear Algebra through Computer Science Applications
- Computer Science Courses at Universities Video Lectures, Open Source Society University - GitHub links to CS resources
- Programming books you might want to consider reading
- MetAcademy - Data Structures & Algorithms, Linear Algebra, Bayes, Set Theory, Probabily, AI, Multivariate Calculus, ...
- 40+ Python Statistics For Data Science Resources
- Programming Languages, Tools, Algorithms, Compilers, ML, Math - GitHub
- Biology, Business, Chemistry, Classics, Computer Science, Earth Science, Economics, Education, Electrical Engineering, History, Mathematics, Physics, Political Science, Sociology and more...
- Statistics
- Probability Theory : The Logic of Science
- The Elements of Statistical Learning Data Mining, Inference and Prediction
- A Programmer's Guide to Data Mining Practical Data Mining, Collective Intelligence and Recommendation Systems by Ron Zacharski
- CNX Text Books - Algebra & Trigonometry, Introductory Statistics, Pre Algebra, College Algebra, Pre calculus, Calculus, FFT, Physics, Economics, ...
- You don't know JS - Book series on JavaScript
- Mining Massive Datasets - Stanford course and text
- How to Learn Math & Physics - Links to free books for both subjects
- Economics - CORE e-book was produced by a large team of collaborators. More than twenty economists from both sides of the Atlantic and from India, Colombia, Chile, and Turkey contributed to it.
- ML with interactive notebooks (ML & Python via notebooks Nyandwi, Videos and notebooks from Stanford, Cornell and UCB and Kaggle Programming, Python, Intro ML, Intermediate ML)
- Machine Learning in a Week, a year Per Harald Borgen writes up his ML bootstrap experience, Crash Course
- Introduction to Machine Learning by Quentin de Laroussilhe
- ML is fun, ML for humans
- Dive into Machine Learning with iPython and sci-kit
- ML: An in-depth non technical guide, Honest Guide to ML - From Axiom Zen Team
- ML Sabbatical - Karl Rosaen
- Lessons learned while studying Machine Learning, Programmers Can Get Into Machine Learning
- How to become a Data Scientist
- Your First Machine Learning Project in Python Step-By-Step
- How to learn Machine Learning, 150 of the best ML,NLP, Python tutorials
- AWS ML Videos
- Data Quest - How to learn Data Science?
- Machine Learning iPython Notebook
- ML for developers - Mike de Waard
- ML Cheat Sheet
- Deep Learning Deep Learning Text from MIT - work in progress
- Machine Learning Tutorials in Azure
- Statistical Learning Introductory course in supervised learning from Stanford. Starts Jan 12, 2016. Uses An Introduction to Statistical Learning text, from the same authors.
- ML Data Set Repository for UC Irvine
- Statistical Data Mining Tutorials Slides on ML by Andrew Moore
- ML in a week Blog post by Per Harald Borgen
- Will it Python Conversion of code from "ML for Hackers" book into Python
- Understanding Machine Learning - From Theory To Algorithms Text by Shai Shalev-Shwartz and Shai Ben-David
- word2vec: A word is worth a thousand vectors Intro - code.google
- Machine Learning @ Coursera by Andrew Ng. Unofficial Notes, Python Implementation- 1, 2
- 7 steps to mastering ML with python - Collection of related links organized in the proper sequence to aid in ML learning.
- General Assembly's Data Science course
- Data School - Blog and tutorial content, related to ML
- Free Programming Books - ML
- Boosting Sales With Machine Learning
- Applying ML to InfoSec
- Text: A Course in Machine Learning - Hal Daume III, 2015 Edition
- Deep Learning Book, Guide to DL by YN - includes Linear Algebra, Probability, Scipy, ML, ...
- Mathematics of Machine Learning - List of Math topics, required for ML.
- Machine Learning Top 10 Articles for the Past Month - September 2016
- Top-down learning path: Machine Learning for Software Engineers - Comments/Links to other resources
- Classifiers Reddit classifier, Amazon Reviews
- Tutorial for readers new to ML and TensorFlow
- TensorFlow examples Tutorials & examples for beginners, Udacity and other examples, Examples by Parag Mital
- How I Learned To Code Neural Networks In 2015 by Per Harald Borgen
- Python Programming for the Humanities Focuses on data extraction, cleaning and ML
- Introduction to Deep Neural Networks
- Neural Networks and Deep Learning - Online Textbook, uses Python
- Hacker's guide to Neural Networks - Start here for learning Neural Networks, by Andrej Karpathy
- Deep Learning - Book from MIT Press, work in progress, covers Linear Algebra etc...
- Paul's Online Math Notes - Cheat Sheets on Algebra, Trignonetry, Calculus, common derivates... - Hugely popular site
- Math Videos Patrick JMT - Limits, Derivatives, Integration Just Match Tutorials, Professor Leonard
- An Interactive Guide To The Fourier Transform
- Better Explained Refresher on Mathematics
- Calculus - Gilbert Strang, MIT Open CourseWare, 2, Calculus Made Easy - Silvanus Thompson
- Free Programming Books includes texts on Linear Algebra, Statistics using Python
- Math without university - Books, Courses, MOOCs, Course list
- Math Texts 1, 2
- The Mathematics of Machine Learning, Reddit
- Immersive Linear Algebra - Online book with fully interactive figures
- YouTube: Essence of Linear Algebra - 3Blue1Brown
- Approved Textbooks by American Institute of Mathematics - for Precalculus, Calculus, Linear Algebra, Probability, Statistics and more ...
- Math for finance - Probability, Linear Algebra, ..., Reddit Recommendations,
- Mathematics for Computer Science - Eric Lehman, F Thomas Leighton, Albert Meyer
- Algebra VITUTOR - polynomials, linear/quadratic equations, matrics, ...
- Trigonometry
- SSDNodes - 4GM RAM, 4 CPU cores for $4/month
- Seven Free Data Wrangling Tools
- Libpostal- a fast, multilingual, international street address parser
- PROSE - Text Extraction & Transformation
- YouTube
- [YouTube : Statistics 110: Probability] - Joe Blitzstein, Professor of the Practice in Statistics, Harvard Table of Contents
- SSL Certificates - LetsEncrypt - Certificate issuance and life cycle management
- Web Server Security Checklist, SSL Server Test
- A practical security guide for web developers - User Comments
- Ben Evans Trends and analysis of Tech
- 25iq - Tren Griffin's blog on Markets, Tech etc.
- Darren Rush
- Research News
- StraTechEry
- Machine Learning Trends and the Future of Artificial Intelligence 2016
- Fund Picker Mutual Funds in India
- Indian Mutual Fund Recommendations
- Sensex Rolling Performance - from yours truly
- S & P - Investing Returns on the S&P 500 - Charts with returns, chances of losses Historical Return Calculator
- Investment Returns in Retirement - Simulator
- Is it time to buy? - Is timing the market good for investments over a 10 year horizon? For a 20 year horizon?
- Stock Market Data Analysis with Python
- Robo Advisors India, MoneyFront - Invest in direct plans
- Taxes - ET: Look beyond 80C
- Portfolio Visualizer - Monte Carlo Simulation - Backtest, Factor, Asset Correlations, Portfolio Optimization, Timing Models
- ValuePickr - Individual stock portfolios for Indian market
- ETF Stock Exposure Tool - List of ETFs that have invested in a stock
- Cost of healthcare in early retirement from Physician on FIRE
- What every CS major should know write-up by Matt Might. Good advice for CS interns or recent majors
- The Absolute Minimum Every Software Developer Absolutely, Positively Must Know About Unicode and Character Sets
- 10 rules for web startups
- SaaS Metrics 2.0 – A Guide to Measuring and Improving what Matters
- 156 Startup Failure Post-Mortems
- FounderKit: Everything you need to build/grow your startup - Reviews of various services
- Indian Startups 2017
- Capital & Growth - Stack Overflow for marketing
- Product Pricing Surveys Gabor-Granger, Van Westendorp ..., Survey Template
- Food Rules: Your Dietary Dos and Don't - NY Times
- Dharma