MONTRÉAL.AI | Montréal Artificial Intelligence

Montréal.AI Academy

AI 101 : Montréal.AI is the largest artificial intelligence community in Canada. Join us and learn at !

The First World-Class Overview of AI for the General Public

Curated Open-Source Codes, Implementations and Science

The First World-Class Overview of AI for the General Public

The best way to predict the future is to invent it.“ — Alan Kay

0. Getting Started

Today’s artificial intelligence is powerful, useful and accessible to all.

Tinker with Neural Networks : Neural Network Playground — TensorFlow

On a Local Machine
Install Anaconda and Launch ‘Anaconda Navigator
Update Jupyterlab and Launch the Application Under Notebook, Click on ‘Python 3

In the Cloud

In the Browser

Preliminary Readings

The First Comprehensive Overview of All (全) AI for the General Public

1. Deep Learning

DL is essentially a new style of programming–”differentiable programming”–and the field is trying to work out the reusable constructs in this style. We have some: convolution, pooling, LSTM, GAN, VAE, memory units, routing units, etc.“ — Thomas G. Dietterich

1.1 Neural Networks

Neural networks” are a sad misnomer. They’re neither neural nor even networks. They’re chains of differentiable, parameterized geometric functions, trained with gradient descent (with gradients obtained via the chain rule). A small set of highschool-level ideas put together.“ — François Chollet

I feel like a significant percentage of Deep Learning breakthroughs ask the question “how can I reuse weights in multiple places?”
– Recurrent (LSTM) layers reuse for multiple timesteps
– Convolutional layers reuse in multiple locations.
– Capsules reuse across orientation.
“ — Trask

1.2 Recurrent Neural Networks

  • Understanding LSTM Networks — Christopher Olah
  • Attention and Augmented RNN — Olah & Carter, 2016
  • Computer, respond to this email — Post by Greg Corrado
  • Reversible Recurrent Neural Networks — Matthew MacKay, Paul Vicol, Jimmy Ba, Roger Grosse
  • Massive Exploration of Neural Machine Translation Architectures arXiv | Docs | Code — Denny Britz, Anna Goldie, Minh-Thang Luong, Quoc Le
  • A TensorFlow implementation of : “Hybrid computing using a neural network with dynamic external memory” GitHub — Alex Graves, Greg Wayne, Malcolm Reynolds, Tim Harley, Ivo Danihelka, Agnieszka Grabska-Barwińska, Sergio Gómez Colmenarejo, Edward Grefenstette, Tiago Ramalho, John Agapiou, Adrià Puigdomènech Badia, Karl Moritz Hermann, Yori Zwols, Georg Ostrovski, Adam Cain, Helen King, Christopher Summerfield, Phil Blunsom, Koray Kavukcuoglu & Demis Hassabis

1.3 Convolution Neural Network

I admire the elegance of your method of computation; it must be nice to ride through these fields upon the horse of true mathematics while the like of us have to make our way laboriously on foot.“ — A. Einstein

1.4 Capsules

2. Autonomous Agents

No superintelligent AI is going to bother with a task that is harder than hacking its reward function.“ — The Lebowski theorem

2.1 Evolution Strategies

2.2 Deep Reinforcement Learning

2.3 Self Play

AlphaGo Zero : Algorithms matter much more than big data and massive amounts of computation

Self-Play is Automated Knowledge Creation.“ — Carlos E. Perez

2.4 Multi-Agent Populations

2.5 Deep Meta-Learning

2.6 Generative Adversarial Network

What I cannot create, I do not understand.“ — Richard Feynman

2.7 World Models

  • World Models — David Ha, Jürgen Schmidhuber
  • Imagination-Augmented Agents for Deep Reinforcement Learning — Théophane Weber, Sébastien Racanière, David P. Reichert, Lars Buesing, Arthur Guez, Danilo Jimenez Rezende, Adria Puigdomènech Badia, Oriol Vinyals, Nicolas Heess, Yujia Li, Razvan Pascanu, Peter Battaglia, Demis Hassabis, David Silver, Daan Wierstra

3. Environments

3.1 OpenAI Gym

3.2 Unity ML-Agents

3.3 DeepMind Control Suite

3.4 Brigham Young University | Holodeck

  • BYU Holodeck: A high-fidelity simulator for deep reinforcement learning Website | GitHub | Documentation — Brigham Young University

3.5 Facebook’s Horizon

  • Horizon: Facebook’s Open Source Applied Reinforcement Learning Platform Paper | GitHub | Blog — Jason Gauci, Edoardo Conti, Yitao Liang, Kittipat Virochsiri, Yuchen He, Zachary Kaden, Vivek Narayanan, Xiaohui Ye

4. General Readings, Ressources and Tools

ML paper writing pro-tip: you can download the raw source of any arxiv paper. Click on the “Other formats” link, then click “Download source”. This gets you a .tar.gz with all the .tex files, all the image files for the figures in their original resolution, etc.“ — Ian Goodfellow

Chief AI Officers : C-level AI | Executive Education‎

Built on over $1 Million ($1,000,000) in artificial intelligence research

Chief AI Officers : C-level AI harnesses the fundamentals of artificial intelligence on a truly global scale and put them to strategically leverage enterprises, governments and institutions with precision engineering.

Chief AI Officers : C-level AI | Executive Education‎

In a moment of technological disruption, leadership matters.“ — Andrew Ng

Success is about actively shaping the game that matters to you. This well-crafted C-level professional keynote pioneers a highly impactful understanding of transformative artificial intelligence strategies, at boardroom level, bringing to life new perspectives for state, national, and international organizations.

Participant Profile

We want to see more life-long learning opportunities, across the board matrix interoperability and the development of Chief AI Officers who possess the knowledges, the skills and the competencies to orchestrate impactful breakthroughs and tangible economic growth for Fortune 500, governments and interagency partners in full compliance with our masterplan : The Montréal AI-First Conglomerate Overarching Program.“ — Vincent Boucher, B. Sc. Physics, M. A. Policy Analysis and M. Sc. Aerospace Engineering (Space Technology), Founding Chairman at Montréal.AI

Chief AI Officers : C-level AI is designed for the :

  • Board Members;
  • Captains of Industry;
  • Chancellors;
  • Chief Executive Officers;
  • Commanders;
  • Excellences;
  • Global Chairs
  • High-Potential Executives;
  • Iconic Tech Entrepreneurs;
  • Luminaries;
  • Managing Directors;
  • Moguls;
  • Philanthropists;
  • Presidents;
  • Scholars;
  • Successful Entrepreneurs and Financiers; and
  • Visionary Founders

… who wish to strategically unleash the power of artificial intelligence on a truly global scale.

Keynote Speaker

Keynote Speaker: Vincent Boucher, Founding Chairman at Montréal.AI.

Vincent Boucher’s strategic foresight and ability to drive one of the AI industry’s most ambitious project ever has pushed this experienced and seasoned executive to the forefront of its field, earning a well-deserved reputation.

Program Benefits

AI is bringing new opportunities. This well-crafted professional keynote presentation pioneers a highly actionable understanding of artificial intelligence strategies, at boardroom level (including best practices), bringing to life new perspectives for state, national, and international organizations.

It’s springtime for AI, and we’re anticipating a long summer.“ — Bill Braun, CIO of Chevron


Course given in French


Half a day - 8:30 a.m. to 11:30 a.m.


  • Until November 30th, 2018: $ 500 + taxes

  • From December 1st, 2018 until January 31st, 2019: $ 750 + taxes

  • From February 1st, 2019 until May 3rd, 2019: $ 1250 + taxes


Friday, May 17th, 2019


Montreal (Québec), Canada. Precise location will be exclusively revealed in May 2019 to official participants.

Chief AI Officers : C-level AI is designed for that special breed of executive who will never settle for less than they can be.

Montréal.AI Academy : AI 101

AI 101 : A Well-Crafted Actionable 75 Minutes Tutorial.

AI 101 : The First Comprehensive Overview of AI for the General Public

The First Comprehensive Overview of All (全) AI for the General Public *

You are qualified for a career in machine learning!

POWERFUL & USEFUL. This actionable tutorial is designed to entrust everybody with the mindset, the skills and the tools to see artificial intelligence from an empowering new vantage point by :

— Exalting state of the art discoveries and science ;
— Curating the best open-source codes & implementations ; and
— Embodying the impetus that drives today’s artificial intelligence.

In life, you need forcing functions. You never know what you’re capable of until you have no choice but go and do it. Excessive comfort leads to unrealized potential.“ — François Chollet

The #AI101 tutorial is presently in alpha (pre-release early version), with a limited number of customers to help us refine it. As we enter beta, we’ll take on many more groups (minimum 150 persons) from the waiting list.

Group Reservation :

✉️ Email Us :
📞 Phone : +1.514.829.8269
🌐 Website :
📝 LinkedIn :
🏛 Headquarters : 350, PRINCE-ARTHUR STREET W., SUITE #2105, MONTREAL [QC], CANADA, H2X 3R4 *Administrative Head Office

#AIFirst #MontrealAI #MontrealAIAcademy #MontrealArtificialIntelligence

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