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TensorFlow 機器學習基本知識

This curriculum is for people who are:

  • New to ML, but who have an intermediate programming background

此內容旨在引導剛接觸機器學習的開發人員完成機器學習旅程的入門階段。你會發現許多資源都使用 TensorFlow,但這項知識也可以轉移到其他機器學習架構。

步驟 1:瞭解機器學習

TensorFlow 2.0 is designed to make building neural networks for machine learning easy, which is why TensorFlow 2.0 uses an API called Keras. The book Deep Learning with Python by Francois Chollet, creator of Keras, is a great place to get started. Read chapters 1-4 to understand the fundamentals of ML from a programmer's perspective. The second half of the book delves into areas like Computer Vision, Natural Language Processing, Generative Deep Learning, and more. Don't worry if these topics are too advanced right now as they will make more sense in due time.

AI and Machine Learning for Coders
by Laurence Moroney

這本介紹書提供以程式碼為優先的方法,讓你瞭解如何導入最常見的機器學習情境,例如電腦視覺、自然語言處理 (NLP),以及建立用於網路、行動、雲端和嵌入執行階段的序列模型。

Deep Learning with Python
by Francois Chollet

本書是使用 Keras 進行深度學習的實作入門指南。

⬆ 或 ⬇

參加線上課程,例如 Coursera 的 Introduction to TensorFlow 或 Udacity 的 Intro to TensorFlow for Deep Learning,這兩門課程講授的基礎知識都與 Francois 的書相同。3blue1brown 的這些影片從數學的角度簡要說明了類神經網路的運作方式,可能也會對你有幫助。

完成此步驟後,你會對機器學習的運作方式有基本認識,可再學習更深入的內容。

Intro to TensorFlow for AI, ML, and Deep Learning

Developed in collaboration with the TensorFlow team, this course is part of the TensorFlow Developer Specialization and will teach you best practices for using TensorFlow.

Intro to TensorFlow for Deep Learning

在這門由 TensorFlow 團隊和 Udacity 合作開發的線上課程中,你將瞭解如何使用 TensorFlow 建構深度學習應用程式。

步驟 2:更深入瞭解

Take the TensorFlow Developer Specialization, which takes you beyond the basics into introductory Computer Vision, NLP, and Sequence modelling.

Completing this step continues your introduction, and teaches you how to use TensorFlow to build basic models for a variety of scenarios, including image classification, understanding sentiment in text, generative algorithms, and more.

TensorFlow Developer Specialization

在這四門由 TensorFlow 開發人員講授的專項課程中,你將探索開發人員在 TensorFlow 中使用哪些工具和軟體來打造可擴充的 AI 技術演算法。

步驟 3:練習

Try some of our TensorFlow Core tutorials, which will allow you to practice the concepts you learned in steps 1 and 2. When you're done, try some of the more advanced exercises.

Completing this step will improve your understanding of the main concepts and scenarios you will encounter when building ML models.

步驟 4:更熟悉 TensorFlow

Now it's time to go back to Deep Learning with Python by Francois and finish chapters 5-9. You should also read the book Hands-on Machine Learning with Scikit-Learn, Keras, and TensorFlow, by Aurelien Geron. This book introduces ML and deep learning using TensorFlow 2.0.

Completing this step will round out your introductory knowledge of ML, including expanding the platform to meet your needs.

Hands-on Machine Learning with Scikit-Learn, Keras, and TensorFlow
by Aurélien Géron

本書使用具體範例、極小理論和兩個可用於實際工作環境的 Python 架構 (Scikit-Learn 及 TensorFlow),讓你輕鬆掌握建構智慧系統的概念和工具。