DeepMind Deep Reinforcement Learning course 2018

03 - Markov Decision Processes and Dynamic Programming

YouTube

Slides

⚠️ Download all lectures and slides in zipfiles here: (part1) , (part2) , (part3)

#DeepReinforcementLearning
#DeepMind

🔭 @DeepGravity

DeepMind Deep Reinforcement Learning course 2018

04 - Model-Free Prediction and Control

YouTube

Slides

⚠️ Download all lectures and slides in zipfiles here: (part1) , (part2) , (part3)

#DeepReinforcementLearning
#DeepMind

🔭 @DeepGravity

DeepMind Deep Reinforcement Learning course 2018

05 - Function Approximation and Deep Reinforcement Learning

YouTube

Slides

⚠️ Download all lectures and slides in zipfiles here: (part1) , (part2) , (part3)

#DeepReinforcementLearning
#DeepMind

🔭 @DeepGravity

DeepMind Deep Reinforcement Learning course 2018

06 - Policy Gradients and Actor Critics

YouTube

Slides

⚠️ Download all lectures and slides in zipfiles here: (part1) , (part2) , (part3)

#DeepReinforcementLearning
#DeepMind

🔭 @DeepGravity

DeepMind Deep Reinforcement Learning course 2018

07 - Planning and Models

YouTube

Slides

⚠️ Download all lectures and slides in zipfiles here: (part1) , (part2) , (part3)

#DeepReinforcementLearning
#DeepMind

🔭 @DeepGravity

DeepMind Deep Reinforcement Learning course 2018

08 - Advanced Topics in Deep RL

YouTube

Slides

⚠️ Download all lectures and slides in zipfiles here: (part1) , (part2) , (part3)

#DeepReinforcementLearning
#DeepMind

🔭 @DeepGravity

DeepMind Deep Reinforcement Learning course 2018

09 - A Brief Tour of Deep RL Agents

YouTube

⚠️ Download all lectures and slides in zipfiles here: (part1) , (part2) , (part3)

#DeepReinforcementLearning
#DeepMind

🔭 @DeepGravity

DeepMind Deep Reinforcement Learning course 2018

10 - Classic Games Case Study

YouTube

⚠️ Download all lectures and slides in zipfiles here: (part1) , (part2) , (part3)

#DeepReinforcementLearning
#DeepMind

🔭 @DeepGravity

DeepMind Deep Reinforcement Learning course 2018 (all lectures and slides) - part1

#DeepReinforcementLearning
#DeepMind

🔭 @DeepGravity

DeepMind Deep Reinforcement Learning course 2018 (all lectures and slides) - part2

#DeepReinforcementLearning
#DeepMind

🔭 @DeepGravity

DeepMind Deep Reinforcement Learning course 2018 (all lectures and slides) - part3

#DeepReinforcementLearning
#DeepMind

🔭 @DeepGravity

Linear Algebra

A Free text for a standard US undergraduate course, by Jim Hefferon

Download the book

Download the solution manual

Link to the reference page

#LinearAlgebra

🔭 @DeepGravity

Grandmaster level in #StarCraft II using multi-agent #ReinforcementLearning

A new paper by #DeepMind is going to be on the cover of #Nature this week:

Abstract:
Many real-world applications require artificial agents to compete and coordinate with other agents in complex environments. As a stepping stone to this goal, the domain of StarCraft has emerged as an important challenge for artificial intelligence research, owing to its iconic and enduring status among the most difficult professional esports and its relevance to the real world in terms of its raw complexity and multi-agent challenges. Over the course of a decade and numerous competitions, the strongest agents have simplified important aspects of the game, utilized superhuman capabilities, or employed hand-crafted sub-systems. Despite these advantages, no previous agent has come close to matching the overall skill of top StarCraft players. We chose to address the challenge of StarCraft using generalpurpose learning methods that are in principle applicable to other complex domains: a multi-agent reinforcement learning algorithm that uses data from both human and agent games within a diverse league of continually adapting strategies and counter-strategies, each represented by deep neural networks. We evaluated our agent, AlphaStar, in the full game of StarCraft II, through a series of online games against human players. AlphaStar was rated at Grandmaster level for all three StarCraft races and above 99.8% of officially ranked human players.

Link to the paper

🔭 @DeepGravity

Convolutional Neural Networks for Visual Recognition course by Stanford University

⚠️ Bofore downloading: check the course syllabus

Download all lectures and slides here (part1) or watch them on YouTube

Find the other materials (assignments and codes, ...) in the course page

#ConvolutionalNeuralNetworks
#DeepLearning
#Stanford

🔭 @DeepGravity

Convolutional Neural Networks for Visual Recognition course by Stanford University

⚠️ Bofore downloading: check the course syllabus

Download all lectures and slides here (part2) or watch them on YouTube

Find the other materials (assignments and codes, ...) in the course page

#ConvolutionalNeuralNetworks
#DeepLearning
#Stanford

🔭 @DeepGravity

Convolutional Neural Networks for Visual Recognition course by Stanford University

⚠️ Bofore downloading: check the course syllabus

Download all lectures and slides here (part3) or watch them on YouTube

Find the other materials (assignments and codes, ...) in the course page

#ConvolutionalNeuralNetworks
#DeepLearning
#Stanford

🔭 @DeepGravity

Computers Evolve a New Path Toward Human Intelligence

Neural networks that borrow strategies from biology are making profound leaps in their abilities. Is ignoring a goal the best way to make truly intelligent machines?

Link to the article

🔭 @DeepGravity

Topics Extraction and Classification of Online Chats

This article provides covers how to automatically identify the topics within a corpus of textual data by using unsupervised topic modelling, and then apply a supervised classification algorithm to assign topic labels to each textual document by using the result of the previous step as target labels.

Link to the article

🔭 @DeepGravity

#Python programming language creator retires, saying: 'It's been an amazing ride'

Guido van #Rossum, the creator of the hugely popular Python programming language, is leaving cloud file storage firm Dropbox and heading into retirement.

That ends his six and half years with the company, which hired in him in 2013 because so much of its functionality was built on Python. And, after last year stepping down from his leadership role over Python decision making, that means the Python creator is officially retiring.

His recruitment at Dropbox made sense for the tech company. Dropbox has about four million lines of Python code and it's the most heavily used language for its back-end services and desktop app.

Read the article here

🔭 @DeepGravity

#MIT Technology Review:
A #NeuralNet solves the three-body problem 100 million times faster

#MachineLearning provides an entirely new way to tackle one of the classic problems of applied #mathematics.

Link to the article

Link to the paper

🔭 @DeepGravity

Ali Ghodsi (a CS prof. at University of Waterloo) is absolutely a great #AI teacher. Download some of his lectures on #DeepLearning here (part1) (part2).
If you are interested you might find all his lectures on his YouTube channel.

⚠️ Bofore downloading: the zipfiles include:

02 - Feedforward neural network
03 - Overfitting
04 - Introduction to Keras
05 - Regularization
06 - Batch Normalization
07 - Convolutional neural network and a simple implementation in Keras
08 - Recurrent neural network
09 - LSTM, GRU
10 - Variational Autoencoder
11 - Generative Adversarial Network

🔭 @DeepGravity