François Chollet

Mountain View, CA ·

Software engineer and AI researcher. Currently a Staff Software Engineer at Google.

Primary interests:

  • Understanding the nature of abstraction and developing algorithms capable of autonomous abstraction (i.e. general intelligence)
  • Democratizing the development and deployment of AI technology, by making it easier to use and explaining it clearly
  • Leveraging technology, in particular AI, to help people gain greater agency over their circumstances and reach their full potential (e.g. EdTech, steerable recommendation engines, personal productivity tech, etc.)
  • Understanding and simulating the early stages of human cognitive development (e.g. developmental psychology, cognitive developmental robotics)


Deep Learning With Python

F. Chollet


Deep Learning with Python introduces the field of deep learning using the Python language and the powerful Keras library. This book builds your understanding through intuitive explanations and practical examples. You'll explore challenging concepts and practice with applications in computer vision, natural-language processing, and generative models. By the time you finish, you'll have the knowledge and hands-on skills to apply deep learning in your own projects.

Publisher link
Amazon link


Deep Learning With R

F. Chollet, J. J. Allaire


Deep Learning With R is the R language version of Deep Learning With Python.

Publisher link
Amazon link


Keras - Deep learning library


Creator, project lead, 2015-present.

TensorFlow - Machine learning platform


Contributor, 2015-present.

Keras Tuner - Hyperparameter tuning for Keras


Project lead, contributor, 2019-present.

Keras Applications - Premade deep learning models


Original author, contributor, 2017-present.

Keras Preprocessing - Data preprocessing utilities


Original author, contributor, 2017-present.

Wysp - Social network and learning engine


Creator, maintainer, 2011-2014.

Wysp is a social network for artists, featuring tools to learn to draw. It peaked at 25,000 MAU around 2014-2015. (retired)


Creator, maintainer, 2014-2015.

QuickAnswers was an online question-answering engine and dialogue system.



Tensor2Tensor for Neural Machine Translation

A. Vaswani, S. Bengio, E. Brevdo, F. Chollet, A. N. Gomez, S. Gouws, L. Jones, L. Kaiser, N. Kalchbrenner, N. Parmar, R. Sepassi, N. Shazeer, J. Uszkoreit


DeepMath: Deep Sequence Models for Premise Selection

A. A. Alemi, F. Chollet, N. Een, G. Irving, C. Szegedy, J. Urban


Tracing Commodities in Indoor Environments for Service Robotics

O. M. Mozos, F. Chollet, K. Murakami, K. Morooka, T. Tsuji, R. Kurazume, T. Hasegawa


The Memories Around Us

December 30, 2018

Every place has a memory. Some places are a memory, a record, layer after layer of history.

Notes to Myself on Software Engineering

September 8, 2018

Design for ethics. Bake your values into your creations.

What Worries Me About AI

March 28, 2018

Don’t use AI as a tool to manipulate your users; instead, give AI to your users as a tool to gain greater agency over their circumstances.

The Implausibility of Intelligence Explosion

November 27, 2017

The notion of intelligence explosion comes from a profound misunderstanding of both the nature of intelligence and the behavior of recursively self-augmenting systems.

User Experience Design for APIs

November 21, 2017

Like most things, API design is not complicated, it just involves following a few basic rules. They all derive from a founding principle: you should care about your users.

The Future of Deep Learning

July 18, 2017

We will move away from having on one hand "hard-coded algorithmic intelligence" (handcrafted software) and on the other hand "learned geometric intelligence" (deep learning). We will have instead a blend of formal algorithmic modules that provide reasoning and abstraction capabilities, and geometric modules that provide informal intuition and pattern recognition capabilities. The whole system would be learned with little or no human involvement.

The Limitations of Deep Learning

July 17, 2017

Despite our progress on machine perception, we are still very far from human-level AI: our models can only perform local generalization, adapting to new situations that must stay very close from past data, while human cognition is capable of extreme generalization, quickly adapting to radically novel situations, or planning very for long-term future situations.

On the Importance of Democratizing Artificial Intelligence

July 6, 2016

It's not a given that every technological revolution should turn out as a net positive for humanity, empowering individuals and bringing us higher potential for learning and creating, for self-direction and self-actualization. It's our responsibility to make sure that this new revolution turns out all right.

The Singularity Is Not Coming

August 10, 2012

Despite exponential resource investment, the pace of scientific progress has been linear.

The Good Librarian

April 30, 2011

The web has become a huge distraction. But it doesn't have to be that way.

The Piano-Playing Cat Paradigm

December 5, 2010

Our use of the Internet is conditioned by its infrastructure. By the underlying logic of current social networks and browsing tools.