컴퓨터 과학

Problem solving with algorithms and data structures using python

• GitLab[Link]

• 교재 [Link]

Test driven development with python

• GitLab[Link]

• 교재 [Link]

3. Data Science

Python for everybody exploring data in Python 3

• GitLab[Link]

• 교재 [Link]

Think stats - exploratory data analysis

• GitLab[Link]

• 교재 [Link]

Think Bayes - Bayesian statistics in Python

• GitLab[Link]

• 교재 [Link]

Ace the Data Science Interview: 201 Real Interview Questions Asked By FAANG, Tech Startups, & Wall Street

• GitLab[Link]

Rust Design Patterns

• GitLab[Link]

Rust Crash Courses

• GitLab[Link]

The Rustonomicon

• GitLab[Link]

• 연습 - Exercism 문제(61~ 70)

Asynchronous Programming

• GitLab[Link]

• 참고 교재

  • Asynchronous Programming in Rust

  • Async programming in Rust with async-std

Rust Standard Library Cookbook

• GitLab[Link]

The Rust Unstable Book

• GitLab[Link]

• 연습 - Exercism 문제(71~80)

Mastering Rust

• GitLab[Link]

Rust in Action

• GitLab[Link]

• 연습 - Exercism 문제(61~80)

MDN References & Guides

• GitLab[Link]

• 참고 교재 - Programming WebAssembly with Rust

Creative Projects for Rust Programmers

• GitLab[Link]

Hands-On Concurrency with Rust

• GitLab[Link]

• 연습 - Exercism 문제(81~103)

Microservices

• GitLab[Link]

• 참고 교재 - Hands-On Microservices with Rust

System Programming

• GitLab[Link]

• 참고 교재 - Practical System Programming for Rust Developers

Network Programming

• GitLab[Link]

• 참고 교재 - Network Programming

Machine Learning

• GitLab[Link]

• 참고 교재 - Practical Machine Learning With Rust

Web Assembly

• GitLab[Link]

• 참고 교재 - Web Assembly In Action

Rust Projects

• GitLab[Link]

• 참고 교재 - Practical Rust Projects: Building Game, Physical Computing, and Machine Learning Applications

Designing a language

• GitLab[Link]

• 참고 교재 - Designing a language(Gleam)

Fullstack Rust

• GitLab[Link]

• 참고 교재 - Fullstack Rust

High Performance

• GitLab[Link]

• 참고 교재 - Rust High Performance

Job Interview

• GitLab[Link]

• 참고 교재 - Rust Job Interview

Craft GraphQL APIs in Elixir with Absinthe: Flexible, Robust Services for Queries, Mutations, and Subscriptions

• GitLab[Link]

• YouTube[Link]

Real-Time Phoenix: Build Highly Scalable Systems with Channels

• GitLab[Link]

• YouTube[Link]

Adopting Elixir: From Concept to Production

• GitLab[Link]

• YouTube[Link]

Designing Elixir Systems With OTP: Write Highly Scalable, Self-healing Software with Layers

• GitLab[Link]

• YouTube[Link]

Testing Elixir

• GitLab[Link]

• YouTube[Link]

Property-Based Testing with PropEr, Erlang, and Elixir: Find Bugs Before Your Users Do

• GitLab[Link]

• YouTube[Link]

Domain Modeling Made Functional

• GitLab[Link]

• YouTube[Link]

Learn you some erlang for great good

• GitLab[Link]

• YouTube[Link]

Programming Phoenix LiveView

• GitLab[Link]

• YouTube[Link]

Elixir Meta Programming

• GitLab[Link]

• YouTube[Link]

Software Telemetry

• GitLab[Link]

• YouTube[Link]

Metaprogramming Elixir: Write Less Code, Get More Done (and Have Fun!) 1st Edition

• GitLab[Link]

• YouTube[Link]

Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow[Link]

• GitLab[Link]

Introduction to Statistical Learning[Link]

• GitLab[Link]

A Brief Introduction to Machine Learning without Deep Learning[Link]

• GitLab[Link]

• A First Course in Machine Learning

• GitLab[Link]

• Machine Learning for Beginners 2019: The Ultimate Guide to Artificial Intelligence, Neural Networks,

• GitLab[Link]

• Pattern Recognition and Machine Learning[Link]

• GitLab[Link]

• Keras Github notebooks[Link]

• GitLab[Link]

• 부교재

  • Deep Learning in Python

  • Teaching Machines: The History of Personalized Learning

• The Hundred-Page Machine Learning Book

• GitLab[Link]

• Introduction to Machine Learning with Python

• GitLab[Link]

• 주교재 - [Link]

• 부교재 - Machine Learning: A Quantitative Approach

• Deep Learning Book[Link]

• GitLab[Link]

• 부교재 - Mathematics for Machine Learning

• Understanding Machine Learning: From Theory to Algorithms[Link]

• GitLab[Link]

• 부교재 - Machine Learning with Python: A Practical Beginners’ Guide

• Seven Steps to Success: Machine Learning in Practice[Link]

• GitLab[Link]

• Rules of Machine Learning: Best practices for Machine Learning Engineering[Link]

• GitLab[Link]

• 부교재 - Machine Learning for Time Series Forecasting with Python

• A Brief Introduction to Machine Learning for Engineers[Link]

• GitLab[Link]

• 부교재 - Building Machine Learning Pipelines: Automating Model Life Cycles with TensorFlow

• Neural Networks and Deep Learning[Link]

• GitLab[Link]

• 부교재 - Python for Data Science: The Ultimate Step-by-Step Guide to Python Programming. Discover How to Master

• Introductory Machine Learning notes[Link]

• GitLab[Link]

• 부교재 - Machine Learning Foundations: Supervised, Unsupervised, and Advanced Learning

• Foundations of Machine Learning[Link]

• GitLab[Link]

• 부교재 - AI and Machine Learning for Coders

• An Introduction to Variable and Feature Selection[Link]

• GitLab[Link]

• 부교재 - Python Machine Learning: Machine Learning and Deep Learning with Python, scikit-learn, and TensorFlow 2, 3rd Edition

• AutoML Book[Link]

• GitLab[Link]

• 부교재 - Machine Learning With Python For Beginners: A Step-By-Step Guide with Hands-On Projects

• Deep Learning with Pytorch[Link]

• GitLab[Link]

• 부교재 - Introduction to Machine Learning, fourth edition

• Dive Into Deep Learning[Link]

• GitLab[Link]

• 부교재 - Python Machine Learning By Example: Build intelligent systems using Python, TensorFlow 2, PyTorch, and scikit-

• Model-based Machine Learning[Link]

• GitLab[Link]

• 부교재 - Machine Learning: The complete Guide to Master Data Science with Python and Developing Artificial

• Bayesian Models for Machine Learning[Link]

• GitLab[Link]

• 부교재 - Introduction to Statistical and Machine Learning Methods for Data Science

• Reinforcement Learning – Sutton and BartoLink]

• GitLab[Link]

• 부교재 - Programming Machine Learning: From Coding to Deep Learning

• Gaussian Processes for Machine Learning[Link]

• GitLab[Link]

• 부교재 - The Alignment Problem: Machine Learning and Human Values

• Machine Learning Interviews Machine Learning Systems Design Chip Huyen[Link]

• GitLab[Link]

• 부교재 - Machine Learning Using TensorFlow Cookbook: Create powerful machine learning algorithms with TensorFlow

• Algorithmic Aspects of Machine Learning[Link]

• GitLab[Link]

• 부교재 - Human-in-the-Loop Machine Learning: Active learning and annotation for human-centered AI

• Causality for Machine Learning[Link]

• GitLab[Link]

• 부교재 - Machine Learning: An Algorithmic Perspective, Second Edition

• Think Stats[Link]

• GitLab[Link]

• 부교재 - Data Science and Machine Learning: Mathematical and Statistical Methods

• Bayesian Methods for Hackers:Probabilistic Programming for Bayesian Inference[Link]

• GitLab[Link]

• 부교재 - Automated Machine Learning with Microsoft Azure: Build highly accurate and scalable end-to-end AI solutions wit...

• Natural Language Processing with Python[Link]

• GitLab[Link]

• 부교재 - Deep Learning Illustrated: A Visual, Interactive Guide to Artificial Intelligence

• Machine Learning Yearning[Link]

• GitLab[Link]

• 부교재 - Becoming a Data Head: How to Think, Speak and Understand Data Science, Statistics and Machine

• Forecasting: Principles and Practice[Link]

• GitLab[Link]

• 부교재 - Data Mining: Concepts and Techniques

• Practical Discrete Mathematics: Discover math principles that fuel algorithms for computer science and machine

• GitLab[Link]

• Natural language processing with python analyzing text with the natural language toolkit[Link]

• GitLab[Link]

• Eisenstein NLP notes[Link]

• GitLab[Link]

• Machine Learning: A Bayesian and Optimization Perspective

• GitLab[Link]

• Machine Learning Engineering

• GitLab[Link]

• Fundamentals of Machine Learning for Predictive Data Analytics

• GitLab[Link]

• Data Mining and Machine Learning: Fundamental Concepts and Algorithms

• GitLab[Link]

• Machine Learning: An Applied Mathematics Introduction

• GitLab[Link]

• Reinforcement Learning, second edition: An Introduction

• GitLab[Link]

• The Master Algorithm: How the Quest for the Ultimate Learning Machine Will Remake Our World

• GitLab[Link]

• Math for Deep Learning: What You Need to Know to Understand Neural Networks

• GitLab[Link]

• Machine Learning in Finance: From Theory to Practice

• GitLab[Link]

• Interpretable Machine Learning with Python: Learn to build interpretable high-performance models with

• GitLab[Link]

• Linear Algebra and Optimization for Machine Learning: A Textbook

• GitLab[Link]

• Approaching (Almost) Any Machine Learning Problem

• GitLab[Link]

• Linear Algebra and Learning from Data

• GitLab[Link]

• Build Your Own AI Investor: With Machine Learning and Python, Step by Step

• GitLab[Link]

• Advances in Financial Machine Learning

• GitLab[Link]

• Engineering MLOps: Rapidly build, test, and manage production-ready machine learning life cycles at scale

• GitLab[Link]

• Interpretable Machine Learning

• GitLab[Link]

• Machine Learning Design Patterns: Solutions to Common Challenges in Data Preparation, Model Building, and ..

• GitLab[Link]

• Probabilistic Machine Learning: An Introduction

• GitLab[Link]

• Machine Learning: A Probabilistic Perspective

• GitLab[Link]

• Data-Driven Science and Engineering: Machine Learning, Dynamical Systems, and Control

• GitLab[Link]

• Data Science on AWS: Implementing End-to-End, Continuous AI and Machine Learning Pipelines

• GitLab[Link]

• Building Machine Learning Powered Applications: Going from Idea to Product

• GitLab[Link]

• Data Science Projects with Python: A case study approach to gaining valuable insights from real data with machine

• GitLab[Link]

• Statistical Machine Learning: A Unified Framework

• GitLab[Link]

• Deep Learning (Adaptive Computation and Machine Learning series)

• GitLab[Link]

• Machine Learning Refined: Foundations, Algorithms, and Applications

• GitLab[Link]

• Deep Learning Interviews: Hundreds of fully solved job interview questions from a wide range of key topics in AI.

• GitLab[Link]

LAME – Linux Administration Made Easy

• 교재 [Link]

• GitLab[Link]

LFS – Linux From Scratch

• 교재 [Link]

• GitLab[Link]

Shell Scripting – Expert Recipes For Linux, Bash And More

• 교재 [Link]

• GitLab[Link]

Debian Administrator’s HandBook

• 교재 [Link]

• GitLab[Link]

Workshop: Linux Kernel Modules

• 교재 [Link]

• GitLab[Link]

Advanced Linux Programming

• 교재 [Link]

• GitLab[Link]

Linux Device Drivers

• 교재 [Link]

• GitLab[Link]

Systemd For Administrators

• 교재 [Link]

• GitLab[Link]

Advanced Linux System Administration

• 교재 [Link]

• GitLab[Link]

Linux System Administration

• 교재 [Link]

• GitLab[Link]

Linux Servers

• 교재 [Link]

• GitLab[Link]

Linux Networking

• 교재 [Link]

• GitLab[Link]

Linux Storage

• 교재 [Link]

• GitLab[Link]

Linux Security

• 교재 [Link]

• GitLab[Link]

• 교재 [Link]

• GitLab[Link]

The AWK Programming Language

• 교재 [Link]

• GitLab[Link]