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原始链接: https://news.ycombinator.com/item?id=40974193

要成为 FAANG 公司的机器学习工程师,以下是一些可供学习的有用资源: 线性代数:在通勤时间观看 3Blue1Brown 的《线性代数精髓》系列。 对于多元微积分,请参阅可汗学院的多元微积分课程,主要关注单元 1 - 简介和单元 2 - 导数。 要温习与机器学习和反向传播特别相关的微积分,请观看链接提供的动画视频。 信息论:阅读题为“信息论:教程简介”的书。 对于统计和概率,请通过 StatQuest YouTube 频道学习,并利用斯坦福大学的 CS229(斯坦福机器学习简介)课程材料,包括讲座 1、2、3、4、8、9、11、12 和 13。 该课程还提供了一套全面的笔记。 使用斯坦福大学 CS231n 课程中的材料深入研究神经网络,该课程是探索深度学习概念的基础。 通过观看滑铁卢大学和密歇根大学的讲座,或阅读 Jay Alammar 的 The Illustrated Transformer 指南,了解 Transformer 和语言模型。 您可能会发现像 LLM 可视化工具这样的交互式体验特别吸引人。 通过 2023 年未来科学奖获得者的演讲仔细了解剩余学习。 通过各种在线课程和教程获取 CUDA 知识并熟悉高效的 ML 技术。 通过参加网络研讨会或浏览在线资源,了解有关 TinyML 和高效深度学习计算的更多信息。 观看“微芯片是如何制造的?”,了解 CPU 技术的发展 观看 Branch Education 的视频或阅读畅销书“芯片战争”。 这些资源将帮助您获得在机器学习工程领域取得优异成绩所需的基本技能,从而在 Google、Amazon、Facebook、Apple、Netflix、Uber、LinkedIn、Airbnb、Dropbox、Twitter 等顶级科技公司取得成功, Palantir、Snapchat、Square、Stripe、Lyft、Asana

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原文
I made a list of all the free resources I used to study ML and deep learning to become an ML engineer at FAANG, so I think it'll be helpful to follow these resources: https://www.trybackprop.com/blog/top_ml_learning_resources (links in the blog post)

Fundamentals Linear Algebra – 3Blue1Brown's Essence of Linear Algebra series, binged all these videos on a one hour train ride visiting my parents

Multivariable Calculus – Khan Academy's Multivariable Calculus lessons were a great refresher of what I had learned in college. Looking back, I just needed to have reviewed Unit 1 – intro and Unit 2 – derivatives.

Calculus for ML – this amazing animated video explains calculus and backpropagation

Information Theory – easy-to-understand book on information theory called Information Theory: A Tutorial Introduction.

Statistics and Probability – the StatQuest YouTube channel

Machine Learning Stanford Intro to Machine Learning by Andrew Ng – Stanford's CS229, the intro to machine learning course, published their lectures on YouTube for free. I watched lectures 1, 2, 3, 4, 8, 9, 11, 12, and 13, and I skipped the rest since I was eager to move onto deep learning. The course also offers a free set of course notes, which are very well written.

Caltech Machine Learning – Caltech's machine learning lectures on YouTube, less mathematical and more intuition based

Deep Learning Andrej Karpathy's Zero to Hero Series – Andrej Karpathy, an AI researcher who graduated with a Stanford PhD and led Tesla AI for several years, released an amazing series of hands on lectures on YouTube. highly highly recommend

Neural networks – Stanford's CS231n course notes and lecture videos were my gateway drug, so to speak, into the world of deep learning.

Transformers and LLMs Transformers – watched these two lectures: lecture from the University of Waterloo and lecture from the University of Michigan. I have also heard good things about Jay Alammar's The Illustrated Transformer guide

ChatGPT Explainer – Wolfram's YouTube explainer video on ChatGPT

Interactive LLM Visualization – This LLM visualization that you can play with in your browser is hands down the best interactive experience with an LLM.

Financial Times' Transformer Explainer – The Financial Times released a lovely interactive article that explains the transformer very well.

Residual Learning – 2023 Future Science Prize Laureates Lecture on residual learning.

Efficient ML and GPUs How are Microchips Made? – This YouTube video by Branch Education is one of the best free educational videos on the internet, regardless of subject, but also, it's the best video on understanding microchips.

CUDA – My FAANG coworkers acquired their CUDA knowledge from this series of lectures.

TinyML and Efficient Deep Learning Computing – 2023 lectures on efficient ML techniques online.

Chip War – Chip War is a bestselling book published in 2022 about microchip technology whose beginning chapters on the invention of the microchip actually explain CPUs very well

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