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科学之路：人、机器与未来 当机器思考时，人类会怎样？
“图灵奖”得主、“深度学习三巨头”之一、“卷积神经网络之父”……由于在人工智能领域的突出贡献，杨立昆被中国计算机科学界和企业界所熟知。 杨立昆的科学之路，谱写了一段关于勇气的宣言。他为了知识本身求学，而不是文凭，他用自己的经历，证明了通过激烈的考试竞争进入名校不是科学成功的窄门。他广泛阅读，为他科学思维的形成奠定了坚实的理论基础。他特立独行，做自己感兴趣的事情，即便那件事在短时间里不被人看好。在人工神经网络研究的低谷期，他寂寞地坚持，终于取得了举世瞩目的成就。 人工智能正在颠覆人类社会，未来机器能思考吗？杨立昆的这部著作，讲述正是人工智能在我们面前崛起——这个历史上绝无仅有的时刻发生的故事。
学术生涯x励志人生x硬核科普
★人工智能行业大佬重磅作品 “卷积网络之父”，2018年度图灵奖得主，Facebook首 席人工智能专家，纽约大学教授、纽约大学数据科学中心的创始主任作品。
★国内外各界人士好评如潮 特伦斯•谢诺夫斯基（美国四院院士）、贾扬清（阿里巴巴集团副总裁）、韦青（微软中国首 席技术官）、吴甘沙（驭势科技董事长、CEO）吴军（计算机科学家）张宏江（智源研究院理事长）、黄铁军（北京大学数字媒体研究所主任）、杨强（国际人工智能学会IJCAI理事长）、马毅（加州大学伯克利分校教授）、郭毅可（欧洲科学院院士）、稚晖君（本名彭志辉，华为天才少年;AI工程师;科技博主）诚挚推荐
★人人都能读，超燃超励志的学术传记 不走寻常路，特立独行，执着做自己，终成深度学习发明人之一，开辟人工智能新领域。激励无数同样普通的人们，《科学之路》堪称人人都能读的励志经典
★站在巨人的肩上，看未来人工智能发展大趋势 机器学习大潮来临，人类是否会被替代？ 人工智能将会对社会和产业结构带来什么改变？ 你，将要如何应对？
杨立昆是法国人，名字原文为Yann;Le;Cun，姓氏为Le;Cun，因在美国常被误缩写为Y.L.Cun，于是把LeCun合写在一起。中文译名原为扬•勒丘恩，华人AI圈一直亲切地称他为“杨乐康”，他本人听闻后，干脆自己给自己取了中文名字——杨立昆。
他被誉为;“卷积神经网络之父”，2019年3月，因在人工智能深度学习方面的贡献获得2018年度图灵奖。此外，他还获得了2014年IEEE神经网络先锋奖（Neural;Network;Pioneer;Award）、2015;年;IEEE;PAMI;杰出研究奖和2016;年;Lovie;终身成就奖。
他为卷积神经网络（CNN，Convolutional;Neural;Networks）和图像识别领域做出了重要贡献，以手写字体识别、图像压缩和人工智能硬件等主题发表过190多份论文，研发了很多关于深度学习的项目，并且拥有14项相关的美国专利。研究领域包括人工智能、机器学习、机器感知、移动机器人学和计算神经学。
学术生涯x励志人生x硬核科普
★人工智能行业大佬重磅作品 “卷积网络之父”，2018年度图灵奖得主，Facebook首 席人工智能专家，纽约大学教授、纽约大学数据科学中心的创始主任作品。
★国内外各界人士好评如潮 特伦斯•谢诺夫斯基（美国四院院士）、贾扬清（阿里巴巴集团副总裁）、韦青（微软中国首 席技术官）、吴甘沙（驭势科技董事长、CEO）吴军（计算机科学家）张宏江（智源研究院理事长）、黄铁军（北京大学数字媒体研究所主任）、杨强（国际人工智能学会IJCAI理事长）、马毅（加州大学伯克利分校教授）、郭毅可（欧洲科学院院士）、稚晖君（本名彭志辉，华为天才少年;AI工程师;科技博主）诚挚推荐
★人人都能读，超燃超励志的学术传记 不走寻常路，特立独行，执着做自己，终成深度学习发明人之一，开辟人工智能新领域。激励无数同样普通的人们，《科学之路》堪称人人都能读的励志经典
★站在巨人的肩上，看未来人工智能发展大趋势 机器学习大潮来临，人类是否会被替代？ 人工智能将会对社会和产业结构带来什么改变？ 你，将要如何应对？
杨立昆是法国人，名字原文为Yann;Le;Cun，姓氏为Le;Cun，因在美国常被误缩写为Y.L.Cun，于是把LeCun合写在一起。中文译名原为扬•勒丘恩，华人AI圈一直亲切地称他为“杨乐康”，他本人听闻后，干脆自己给自己取了中文名字——杨立昆。
他被誉为;“卷积神经网络之父”，2019年3月，因在人工智能深度学习方面的贡献获得2018年度图灵奖。此外，他还获得了2014年IEEE神经网络先锋奖（Neural;Network;Pioneer;Award）、2015;年;IEEE;PAMI;杰出研究奖和2016;年;Lovie;终身成就奖。
他为卷积神经网络（CNN，Convolutional;Neural;Networks）和图像识别领域做出了重要贡献，以手写字体识别、图像压缩和人工智能硬件等主题发表过190多份论文，研发了很多关于深度学习的项目，并且拥有14项相关的美国专利。研究领域包括人工智能、机器学习、机器感知、移动机器人学和计算神经学。
340 pages, Kindle Edition
First published October 16, 2019
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176 people want to read
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Displaying 1 - 13 of 13 reviews
May 6, 2020
La lecture est très intéressante. Il y a un réel effort de rendre accessible le propos. C'est, je pense, plutôt réussi.
Le chapitre 3 expliquant la rétropropagation n'est pas des plus évidents. La notation type informatique n'est pas un très bon choix (pour les dérivées partielles notamment, c'est particulièrement indigeste).
J'aurais aimé que l'employeur de l'auteur soit un peu moins cité.
Vers la fin du livre Le Cun aborde tout un tas de sujets ayant trait au futur de l'AI. Il y expose son opinion sans vraiment d'argumentation.
Malgré tout ça, c'est un excellent livre qui demande un réel effort de la part du lecteur.
Le chapitre 3 expliquant la rétropropagation n'est pas des plus évidents. La notation type informatique n'est pas un très bon choix (pour les dérivées partielles notamment, c'est particulièrement indigeste).
J'aurais aimé que l'employeur de l'auteur soit un peu moins cité.
Vers la fin du livre Le Cun aborde tout un tas de sujets ayant trait au futur de l'AI. Il y expose son opinion sans vraiment d'argumentation.
Malgré tout ça, c'est un excellent livre qui demande un réel effort de la part du lecteur.
December 29, 2025
très bon livre sur l‘IA
ne soyons pas prétentieux, je n'ai pas tout compris... mais ce que j'ai compris est passionnant!
ne soyons pas prétentieux, je n'ai pas tout compris... mais ce que j'ai compris est passionnant!
February 13, 2020
(At the moment this is only available in the original French, but I'm sure a translation to English is on its way.)
This book is many things at once.
Firstly, it is a solid, fairly accessible, and very up-to-date introduction to deep learning for people with a STEM background. This said, I may be underestimating the difficulty of certain parts of the book, because I am specialised in machine learning. Some parts were much too easy for me; but LeCun proceeds to go through a wide range of mature AI techniques, some of which I knew very little about, in a simple and intuitive way. So people with some elementary knowledge of maths and programming, and a professional or amateur interest in deep learning, will find that worth their time.
Secondly, it is a professional autobiography: "me, Yann Lecun, my life and work". This will be interesting to those looking for a career in AI or another high-tech field, and probably awfully boring to anybody else.
Thirdly, it's a potpourri of LeCun's opinions on a variety of topics related to AI. Some are clever and interesting. Others are already widely held in the AI community and beyond, and therefore not interesting. Some are, I think, plain wrong, reflecting LeCun's own biases. Finally, some are just rushed sloppily. So for instance Lecun, who works for Facebook, thinks Facebook is great and that the problems caused by AI in social media have been solved. Likewise, there are snippets about machine consciousness, military usage, etc., that reflect a lack of engagement with (or a misunderstanding of) these issues.
All in all, this is definitely worth a read if you work in AI or if you're passionate about the topic. Otherwise, I'm not so sure.
This book is many things at once.
Firstly, it is a solid, fairly accessible, and very up-to-date introduction to deep learning for people with a STEM background. This said, I may be underestimating the difficulty of certain parts of the book, because I am specialised in machine learning. Some parts were much too easy for me; but LeCun proceeds to go through a wide range of mature AI techniques, some of which I knew very little about, in a simple and intuitive way. So people with some elementary knowledge of maths and programming, and a professional or amateur interest in deep learning, will find that worth their time.
Secondly, it is a professional autobiography: "me, Yann Lecun, my life and work". This will be interesting to those looking for a career in AI or another high-tech field, and probably awfully boring to anybody else.
Thirdly, it's a potpourri of LeCun's opinions on a variety of topics related to AI. Some are clever and interesting. Others are already widely held in the AI community and beyond, and therefore not interesting. Some are, I think, plain wrong, reflecting LeCun's own biases. Finally, some are just rushed sloppily. So for instance Lecun, who works for Facebook, thinks Facebook is great and that the problems caused by AI in social media have been solved. Likewise, there are snippets about machine consciousness, military usage, etc., that reflect a lack of engagement with (or a misunderstanding of) these issues.
All in all, this is definitely worth a read if you work in AI or if you're passionate about the topic. Otherwise, I'm not so sure.
June 7, 2021
As a PhD student in deep learning, I really enjoyed reading this book. I think many aspects of it complement what we're usually taught or what we are usually confronted with, and it's inspiring to know the past and possible future of the field.
The only thing I found disappointing was that the explanations of complex algorithms like backpropagation were not very clear, and the "pythonish" notation only complicated things further.
Nevertheless, this is a wonderful book to read for anyone interested or even already working on artificial intelligence.
The only thing I found disappointing was that the explanations of complex algorithms like backpropagation were not very clear, and the "pythonish" notation only complicated things further.
Nevertheless, this is a wonderful book to read for anyone interested or even already working on artificial intelligence.
June 20, 2024
This is yet another book by one of the most renowned AI scientists, and it's somewhat surprising to find that the book (originally written in French) has a Chinese translation but not an English one.
While it was expected to be a biography, the book actually consists mostly of short essays about his personal history and career milestones, along with some introductions to ConvNet and other models. The sections feel somewhat disjointed, and although some personal anecdotes are included, they often feel detached and similar to what you might find in a Wikipedia article. Midway through the book, the discussion on learning models also comes across as a bit scattered. The last chapters don't quite deliver either, with few topics being thoroughly explored.
That said, the book does shed light on the vicissitudes of artificial intelligence research. It outlines how researchers endured the "AI winter" from a personal perspective, recovering from being considered outliers and heretics to becoming some of the most recognized scientists today. Though not recommended for the general audience, if you're a researcher with some preliminary knowledge of deep learning, you may still find some inspiration from this.
While it was expected to be a biography, the book actually consists mostly of short essays about his personal history and career milestones, along with some introductions to ConvNet and other models. The sections feel somewhat disjointed, and although some personal anecdotes are included, they often feel detached and similar to what you might find in a Wikipedia article. Midway through the book, the discussion on learning models also comes across as a bit scattered. The last chapters don't quite deliver either, with few topics being thoroughly explored.
That said, the book does shed light on the vicissitudes of artificial intelligence research. It outlines how researchers endured the "AI winter" from a personal perspective, recovering from being considered outliers and heretics to becoming some of the most recognized scientists today. Though not recommended for the general audience, if you're a researcher with some preliminary knowledge of deep learning, you may still find some inspiration from this.
September 1, 2023
Yann Le Cun est au machine learning ce que le pape est a l'eglise catholique. Du coup, quand il ecrit un bouquin sur le sujet, ca attire les lecteurs.
J'avais tres peur quand j'ai achete le livre. J'avais peur que l'auteur prenne soit tellement de raccourcis que ca devienne risible. Et a l'inverse, j'avais peur que l'auteur veuille rendre la partie technique trop compliquee et detaillee et ne perde les lecteurs.
J'ai ete vraiment tres agreablement surpris et le livre est un excellent melange entre (1) la carriere de Yann dans le domaine (2) les concepts et les explications techniques du machine learning et (3) l'etat de l'art et l'avenir. C'est extremement difficile d'arriver a tel equilibre et Yann a reussi l'exercice.
Je ne peux que recommender ce livre a quiconque souhaite s'informer sur le machine learning. Non seulement l'auteur est tres qualifie mais le contenu et tres accessible. Je pense que cela restera un ouvrage de reference, au moins dans la langue francaise.
J'avais tres peur quand j'ai achete le livre. J'avais peur que l'auteur prenne soit tellement de raccourcis que ca devienne risible. Et a l'inverse, j'avais peur que l'auteur veuille rendre la partie technique trop compliquee et detaillee et ne perde les lecteurs.
J'ai ete vraiment tres agreablement surpris et le livre est un excellent melange entre (1) la carriere de Yann dans le domaine (2) les concepts et les explications techniques du machine learning et (3) l'etat de l'art et l'avenir. C'est extremement difficile d'arriver a tel equilibre et Yann a reussi l'exercice.
Je ne peux que recommender ce livre a quiconque souhaite s'informer sur le machine learning. Non seulement l'auteur est tres qualifie mais le contenu et tres accessible. Je pense que cela restera un ouvrage de reference, au moins dans la langue francaise.
June 6, 2022
Piles of books have been written about AI, primarily by outsiders: philosophers, journalists, linguists, sociologists, business coaches, and the like. This book is an extremely rare case when a generally-accessible text on AI is produced by a world-class computer scientist and one of the inventors of modern neural networks. This fact alone should be a sufficient motivation to peak up the tome when it’s finally translated into English. But there’s one more important reason: being Chief AI Scientist at Meta Yann LeCun has an insider view on Facebook’s policies and infrastructure that defines which news you see or don’t see on your Facebook feed. It's sobering to know what personalities stand behind the algorithms that substituted law these days, which ethical code they subscribe to, and how they imagine our shared future.
November 20, 2019
L'auteur est sans doute un génie Dan's son domaine, mais la lecture de son ouvrage n'est pas aisée. Les concepts s'enchaînent rapidement et rapidement le lecteur, même volontaire et intéressé a de la difficulté à suivre. Rendez-vous raté en ce qui me concerne et je le regrette.
April 26, 2022
Très bon livre qui permet d'avoir une vue globale sur ce que c'est l'IA mais aussi de son histoire avec le point de vue d'un des acteurs majeurs. Quelques passages sont un peu technique mais on peut très bien les sauter.
August 11, 2022
For a non-technical person it offers a couple of chapters of AI for dummies which are very interesting. There is a lot of added value in the enthusiasm of Yann in writing about innovations where he has been a part of or close by.
January 2, 2024
Ah, now I understand why the author is on twitter arguing that books should be free downloads with no payment to authors -- they want the data for training. It seems a short-sighted position, though, still.
November 11, 2021
Ideas for the best reading order: https://mikelaud.github.io/
January 8, 2022
Some parts are quite technical and fascinating. Some are too high level and quite vague.
Displaying 1 - 13 of 13 reviews