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Artificial Intelligence 101: The First World-Class Overview of AI for the General Public

Eventbrite - Montréal.AI Academy: AI 101

Artificial Intelligence 101: World-Class Overview of AI for All

A Well-Crafted Actionable 75 Minutes Tutorial.

For the purpose of entrusting all sentient beings with powerful AI tools to learn, deploy and scale AI in order to enhance their prosperity, to settle planetary-scale problems and to inspire those who, with AI, will shape the 21st Century, Montréal.AI introduces the “VIP AI 101 CheatSheet for All”.

VIP AI 101 CheatSheet for All
Encompassing all facets of AI, the General Secretariat of MONTREAL.AI presents, with authority and from insider knowledge: “Artificial Intelligence 101: The First World-Class Overview of AI for the General Public“.

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.

You are qualified for a career in machine learning!


Language: Course given in English.
Location: Montreal (Québec), Canada.

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

Curated Open-Source Codes, Implementations and Science

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

Curated Open-Source Codes, Implementations and Science

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

When you first study a field, it seems like you have to memorize a zillion things. You don’t. What you need is to identify the 3-5 core principles that govern the field. The million things you thought you had to memorize are various combinations of the core principles.“ — J. Reed

1. Multiply things together 2. Add them up 3. Replaces negatives with zeros 4. Return to step 1, a hundred times.“ — Jeremy Howard

Artificial Intelligence 101: The First World-Class Overview of 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

  • Long Short-Term Memory — Sepp Hochreiter, Jürgen Schmidhuber
  • Understanding LSTM Networks — Christopher Olah
  • Attention and Augmented RNN — Olah & Carter, 2016
  • Computer, respond to this email — Post by Greg Corrado
  • How do Mixture Density RNNs Predict the Future? — Kai Olav Ellefsen, Charles Patrick Martin, Jim Torresen
  • Reversible Recurrent Neural Networks — Matthew MacKay, Paul Vicol, Jimmy Ba, Roger Grosse
  • Recurrent Relational Networks Blog | arXiv | Code — Rasmus Berg Palm, Ulrich Paquet, Ole Winther
  • 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

3.6 PhysX

  • PhysX SDK, an Open-Source Physics Engine GitHub | Blog — NVIDIA

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

Artificial Intelligence 101: The First World-Class Overview of AI for the General Public

Eventbrite - Montréal.AI Academy: AI 101

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