컬렉션을 사용해 정리하기
내 환경설정을 기준으로 콘텐츠를 저장하고 분류하세요.
TensorFlow를 활용한 이론 및 고급 머신러닝
아래의 학습 자료를 시작하기 전에 다음 사항을 확인하세요.
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TensorFlow를 활용한 머신러닝의 기초 커리큘럼을 완료했거나 상응하는 지식이 있습니다.
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소프트웨어 개발, 특히 Python 개발 경험이 있습니다.
이 커리큘럼은 다음을 원하는 사용자를 위한 출발점입니다.
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ML 이해도 개선
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TensorFlow 관련 자료 이해 및 구현 시작
ML의 작동 방식에 관한 배경지식이 있거나 초보자 커리큘럼인 TensorFlow를 활용한 머신러닝의 기초의 학습 자료를 완료해야 계속 진행할 수 있습니다. 아래 콘텐츠는 더 이론적이고 고급 수준인 머신러닝 콘텐츠를 소개합니다. 많은 리소스에서 TensorFlow를 사용하지만 관련 지식은 다른 ML 프레임워크에도 적용할 수 있습니다.
ML에 관한 이해를 심화하려면 Python 프로그래밍 경험과 미적분, 선형 대수, 확률 및 통계에 관한 배경지식이 있어야 합니다. ML 지식 심화를 위해 다양한 추천 리소스와 대학 강의, 몇 가지 교과서를 나열해 두었습니다.
1단계: 수학 개념에 관한 이해 되살리기
선형 대수의 핵심
- 3Blue1Brown 제공
행렬, 행렬식, 고유 항목 등의 기하학적 의미를 설명하는 3blue1brown의 짧은 시각적 동영상 시리즈입니다.
미적분학의 핵심
- 3Blue1Brown 제공
방정식의 원리와 기본 정리에 관해 제대로 이해할 수 있도록 미적분학의 기초를 설명하는 3blue1brown의 짧은 시각적 동영상 시리즈입니다.
MIT 18.06: 선형 대수
MIT의 이 입문 과정에서는 행렬 이론과 선형 대수를 다룹니다. 방정식, 벡터 공간, 행렬식, 고윳값, 유사성, 양정치 행렬 등 다른 분야에도 유용한 주제가 강조됩니다.
2단계: 강의와 책으로 딥 러닝에 관한 이해 심화하기
강의 듣기:
DeepLearning.AI
딥 러닝 특화 과정
5개 과정에서는 딥 러닝의 기초, 신경망 빌드 방법, 머신러닝 프로젝트를 성공으로 이끌고 AI 관련 경력을 쌓는 방법 등을 알아봅니다. 이론뿐 아니라 이론이 산업에 어떻게 적용되는지도 배울 수 있습니다.
⬆ 및 ⬇
도서 추천:
딥 러닝
이안 굿펠로우, 요슈아 벤지오, 애런 쿠르빌 공저
이 딥 러닝 교과서는 학생과 실무자들이 일반적인 머신러닝, 그리고 그 중에서도 딥 러닝 분야에 입문하는 데 도움이 되도록 만들어진 리소스입니다.
3단계: TensorFlow 관련 자료 읽고 구현하기
[null,null,[],[],[],null,["# Theoretical and Advanced Machine Learning\n\n[TensorFlow](/tutorials) › [Resources](/resources/models-datasets) › [Learn ML](/resources/learn-ml) › [Guide](/resources/learn-ml/theoretical-and-advanced-machine-learning) › \n\nTheoretical and advanced machine learning with TensorFlow\n=========================================================\n\nBefore starting on the learning materials below, be sure to:\n\n1. Complete our curriculum [Basics of machine learning with TensorFlow](/resources/learn-ml/basics-of-machine-learning), or have equivalent knowledge\n\n2. Have software development experience, particularly in Python\n\nThis curriculum is a starting point for people who would like to:\n\n1. Improve their understanding of ML\n\n2. Begin understanding and implementing papers with TensorFlow\n\nYou should already have background knowledge of how ML works or completed the learning materials in the beginner curriculum [Basics of machine learning with TensorFlow](/resources/learn-ml/basics-of-machine-learning) before continuing. The below content is intended to guide learners to more theoretical and advanced machine learning content. You will see that many of the resources use TensorFlow, however, the knowledge is transferable to other ML frameworks.\n\nTo further your understanding of ML, you should have Python programming experience as well as a background in calculus, linear algebra, probability, and statistics. To help you deepen your ML knowledge, we have listed a number of recommended resources and courses from universities, as well as a couple of textbooks. \n\nStep 1: Refresh your understanding of math concepts\n---------------------------------------------------\n\nML is a math heavy discipline. If you plan to modify ML models, or build new ones from scratch, familiarity with the underlying math concepts is important. You don't have to learn all the math upfront, but instead you can look up concepts you are unfamiliar with as you come across them. If it's been a while since you've taken a math course, try watching the [Essence of linear algebra](https://www.youtube.com/playlist?list=PLZHQObOWTQDPD3MizzM2xVFitgF8hE_ab) and the [Essence of calculus](https://www.youtube.com/playlist?list=PLZHQObOWTQDMsr9K-rj53DwVRMYO3t5Yr) playlists from 3blue1brown for a refresher. We recommend that you continue by taking a class from a university, or watching open access lectures from MIT, such as [Linear Algebra](https://ocw.mit.edu/courses/mathematics/18-06-linear-algebra-spring-2010/) or [Single Variable Calculus](https://ocw.mit.edu/courses/mathematics/18-01-single-variable-calculus-fall-2006/). \n[Essence of Linear Algebra](https://www.youtube.com/playlist?list=PLZHQObOWTQDPD3MizzM2xVFitgF8hE_ab) \nby 3Blue1Brown \nA series of short, visual videos from 3blue1brown that explain the geometric understanding of matrices, determinants, eigen-stuffs and more. \nFree [View series](https://www.youtube.com/playlist?list=PLZHQObOWTQDPD3MizzM2xVFitgF8hE_ab) \nMath \n[Essence of Calculus](https://www.youtube.com/playlist?list=PLZHQObOWTQDMsr9K-rj53DwVRMYO3t5Yr) \nby 3Blue1Brown \nA series of short, visual videos from 3blue1brown that explain the fundamentals of calculus in a way that give you a strong understanding of the fundamental theorems, and not just how the equations work. \nFree [View series](https://www.youtube.com/playlist?list=PLZHQObOWTQDMsr9K-rj53DwVRMYO3t5Yr) \nMath \n[MIT 18.06: Linear Algebra](https://ocw.mit.edu/courses/mathematics/18-06-linear-algebra-spring-2010/) \nThis introductory course from MIT covers matrix theory and linear algebra. Emphasis is given to topics that will be useful in other disciplines, including systems of equations, vector spaces, determinants, eigenvalues, similarity, and positive definite matrices. \nFree [View course](https://ocw.mit.edu/courses/mathematics/18-06-linear-algebra-spring-2010/) \nMath \n[MIT 18.01: Single Variable Calculus](https://ocw.mit.edu/courses/mathematics/18-01-single-variable-calculus-fall-2006/) \nThis introductory calculus course from MIT covers differentiation and integration of functions of one variable, with applications. \nFree [View course](https://ocw.mit.edu/courses/mathematics/18-01-single-variable-calculus-fall-2006/) \nMath \n\nStep 2: Deepen your understanding of deep learning with these courses and books\n-------------------------------------------------------------------------------\n\nThere is no single course that will teach you everything you need to know about deep learning. One approach that may be helpful is to take a few courses at the same time. Although there will be overlap in the material, having multiple instructors explain concepts in different ways can be helpful, especially for complex topics. Below are several courses we recommend to help get you started. You can explore each of them together, or just choose the ones that feel the most relevant to you.\n\nRemember, the more you learn, and reinforce these concepts through practice, the more adept you will be at building and evaluating your own ML models. \n\n##### Take these courses:\n\n[MIT course 6.S191: Introduction to Deep Learning](http://introtodeeplearning.com/) is an introductory course for Deep Learning with TensorFlow from MIT and also a wonderful resource.\n\nAndrew Ng's [Deep Learning Specialization at Coursera](https://www.coursera.org/specializations/deep-learning) also teaches the foundations of deep learning, including convolutional networks, RNNS, LSTMs, and more. This specialization is designed to help you apply deep learning in your work, and to build a career in AI. \n[MIT 6.S191: Introduction to Deep Learning](http://introtodeeplearning.com/) \nIn this course from MIT, you will gain foundational knowledge of deep learning algorithms and get practical experience in building neural networks in TensorFlow. \nFree [View course](http://introtodeeplearning.com/) \nCode \nMath \nTheory \nBuild \n\nDeepLearning.AI\n[Deep Learning Specialization](https://www.coursera.org/specializations/deep-learning) \nIn five courses, you will learn the foundations of Deep Learning, understand how to build neural networks, and learn how to lead successful machine learning projects and build a career in AI. You will master not only the theory, but also see how it is applied in industry. \n[View course](https://www.coursera.org/specializations/deep-learning) \nCode \nMath \nTheory \nBuild \n\n##### ⬆ And ⬇\nRead these books:\n\nTo complement what you learn in the courses listed above, we recommend that you dive deeper by reading the books below. Each book is available online, and offers supplementary materials to help you practice.\n\nYou can start by reading [Deep Learning: An MIT Press Book](https://www.deeplearningbook.org/) by Ian Goodfellow, Yoshua Bengio, and Aaron Courville. The Deep Learning textbook is an advanced resource intended to help students deepen their understanding. The book is accompanied by [a website](http://www.deeplearningbook.org/), which provides a variety of supplementary materials, including exercises, lecture slides, corrections of mistakes, and other resources to give you hands on practice with the concepts.\n\nYou can also explore Michael Nielsen's online book [Neural Networks and Deep Learning](http://neuralnetworksanddeeplearning.com/). This book provides a theoretical background on neural networks. It does not use TensorFlow, but is a great reference for students interested in learning more. \n[Deep Learning](https://www.deeplearningbook.org/) \nby Ian Goodfellow, Yoshua Bengio, and Aaron Courville \nThis Deep Learning textbook is a resource intended to help students and practitioners enter the field of machine learning in general, and deep learning in particular. \nFree [View book](https://www.deeplearningbook.org/) \nMath \nTheory \nBuild \n[Neural Networks and Deep Learning](http://neuralnetworksanddeeplearning.com/) \nby Michael Nielsen \nThis book provides a theoretical background on neural networks. It does not use TensorFlow, but is a great reference for students interested in learning more. \nFree [View book](http://neuralnetworksanddeeplearning.com/) \nCode \nMath \nTheory \nBuild \n\nStep 3: Read and implement papers with TensorFlow\n-------------------------------------------------\n\nAt this point, we recommend reading papers and trying the [advanced tutorials](/tutorials) on our website, which contain implementations of a few well known publications. The best way to learn an advanced application, [machine translation](/tutorials/text/transformer), or [image captioning](/tutorials/text/image_captioning), is to read the paper linked from the tutorial. As you work through it, find the relevant sections of the code, and use them to help solidify your understanding. \n[Previous\nBasics of machine learning with TensorFlow](/resources/learn-ml/basics-of-machine-learning) [Next\nSpecialization: Basics of TensorFlow for Javascript development](/resources/learn-ml/basics-of-tensorflow-for-js-development) \n\nLearn, develop and build with TensorFlow\n----------------------------------------\n\n[Get started](/learn)"]]
