Amazon董事吴恩达新书封神!吹爆这份最真实的人工智能求职指南

盼了又盼,AI的风终于吹到了秋招

拉表一算

发现今年科技大厂开给应届生的AI岗位

简直爆炸💥多

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Microsoft

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Tiktok

留学生究竟该如何把握全新的职业风向?

斯坦福教授吴恩达的全新书籍

《How to Build Your Career in AI》

或许能给你答案👇

01

斯坦福教授兼Amazon董事新作:

这本AI职业指南简直太神了…

本书于今年年初发布,是吴恩达特别为想要进入AI行业的新手所写的职业Guide Book

指南内容也和名字一样单刀直入,包含*如何培养AI所需技能、如何选择&做项目为简历增色、如何选择岗位/公司…*

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整本指南共41页,虽是全英文版本,但胜在读起来简单易懂、小白友好,含金量更是高的没话说👇

首先,从基础到进阶,依次罗列出进入AI行业所需的技能,并说明每个技能需要掌握到何种程度。

例如很多非理工科背景同学想入行,提出最多的问题就是:我需不需要学数学?需要学到什么程度?

吴恩达就在书中给出了解答:AI中的数学可以有,但更需要有针对性的进行学习

在这里插入图片描述

《How to Build Your Career in AI》,扫码可免费领取

不像市面上其他的指南一样,在提到Machine Learning中相关的线性代数库时,会让你把每个库都深入学习一遍。

吴恩达会建议由于随着线性代数库的成熟,对其拥有浅层理解或许就足以用于工作中了。

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光这一点,就比其他Guide Book要强多了

其次,在学习了基本技术后,想要进入到工作中,吴恩达还手把手教你如何选择/创建一个靠谱的AI项目,让你能放进简历中作为强有力的项目经验。

小到你的日常homework,大到教授、学校所组织的相关Project,如何一步步去参与这些项目,并实现相关职业目标。

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这对0经验背景的同学来说,无疑是可直接套用的方法。

最后,根据AI行业不同的职业,如数据分析师、机器学习工程师、AI研究员等,分析了如何选择适合自己的岗位以及所在公司/行业该如何选择

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这本书不仅胜在够新,符合当下AI行业的职业趋势,也是新手准备的checklist,只需对照准备,就可稳打稳扎入门。

·吴恩达 | AI行业入门指南大礼包·

高清版PDF《How to Build Your Career in AI》

☑本书由吴恩达亲撰给想进入AI的萌新,今年全新发布

☑包括技能、项目、求职等版块,覆盖面广。

☑每个版块都有具体使用建议,可执行性非常强

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《How to Build Your Career in AI》,扫码可免费领取

Table of Contents 1 Why Machine Learning Strategy 2 How to use this book to help your team 3 Prerequisites and Notation 4 Scale drives machine learning progress 5 Your development and test sets 6 Your dev and test sets should come from the same distribution 7 How large do the dev/test sets need to be? 8 Establish a single-number evaluation metric for your team to optimize 9 Optimizing and satisficing metrics 10 Having a dev set and metric speeds up iterations 11 When to change dev/test sets and metrics 12 Takeaways: Setting up development and test sets 13 Build your first system quickly, then iterate 14 Error analysis: Look at dev set examples to evaluate ideas 15 Evaluating multiple ideas in parallel during error analysis 16 Cleaning up mislabeled dev and test set examples 17 If you have a large dev set, split it into two subsets, only one of which you look at 18 How big should the Eyeball and Blackbox dev sets be? 19 Takeaways: Basic error analysis 20 Bias and Variance: The two big sources of error 21 Examples of Bias and Variance 22 Comparing to the optimal error rate 23 Addressing Bias and Variance 24 Bias vs. Variance tradeoff 25 Techniques for reducing avoidable bias Page 3 Machine Learning Yearning-Draft Andrew Ng26 Error analysis on the training set 27 Techniques for reducing variance 28 Diagnosing bias and variance: Learning curves 29 Plotting training error 30 Interpreting learning curves: High bias 31 Interpreting learning curves: Other cases 32 Plotting learning curves 33 Why we compare to human-level performance 34 How to define human-level performance 35 Surpassing human-level performance 36 When you should train and test on different distributions 37 How to decide whether to use all your data 38 How to decide whether to include inconsistent data 39 Weighting data 40 Generalizing from the training set to the dev set 41 Addressing Bias and Variance 42 Addressing data mismatch 43 Artificial data synthesis 44 The Optimization Verification test 45 General form of Optimization Verification test 46 Reinforcement learning example 47 The rise of end-to-end learning 48 More end-to-end learning examples 49 Pros and cons of end-to-end learning 50 Learned sub-components 51 Directly learning rich outputs Page 4 Machine Learning Yearning-Draft Andrew Ng52 Error Analysis by Parts 53 Beyond supervised learning: What’s next? 54 Building a superhero team - Get your teammates to read this 55 Big picture 56 Credits
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