#Python developers survey 2019

Hey #Pythonista,

This is the third iteration of the official Python Developers Survey. With this survey, we aim to identify how the Python development world looks today and how it compares to last year. In 2018 we received 20,000 responses from Python developers, who shared their experience to help us map out an accurate landscape of the Python community.

The results of this survey serve as a major source of knowledge about the current state of the Python community, so we encourage you to participate and take this 10-minute survey and make an invaluable contribution to the community.

After the survey is over, we will publish the aggregated results and randomly choose 100 winners (from those who complete the survey in its entirety), who will each receive an amazing Python Surprise Gift Pack.

Thank you for contributing to this community effort!

Let’s get started with the survey!

🔭 @DeepGravity

Disney's Magic Highway - 1958

See how Walt #Disney in 1958 magically predicted future highways!

Link to the video in YouTube

Link to a related article

#Magic!
#SelfDrivingCars

🔭 @DeepGravity

A new very cool paper by Google:

Self-training with Noisy Student improves ImageNet classification

Abstract

We present a simple self-training method that achieves
87.4% top-1 accuracy on ImageNet, which is 1.0% better
than the state-of-the-art model that requires 3.5B weakly labeled Instagram images. On robustness test sets, it improves
ImageNet-A top-1 accuracy from 16.6% to 74.2%, reduces
ImageNet-C mean corruption error from 45.7 to 31.2, and
reduces ImageNet-P mean flip rate from 27.8 to 16.1.
To achieve this result, we first train an EfficientNet model
on labeled ImageNet images and use it as a teacher to generate pseudo labels on 300M unlabeled images. We then
train a larger EfficientNet as a student model on the combination of labeled and pseudo labeled images. We iterate
this process by putting back the student as the teacher. During the generation of the pseudo labels, the teacher is not
noised so that the pseudo labels are as good as possible.
But during the learning of the student, we inject noise such
as data augmentation, dropout, stochastic depth to the student so that the noised student is forced to learn harder from
the pseudo labels.

Link to the paper

#ComputerVision
#Google

🔭 @DeepGravity

DeepMind Deep Reinforcement Learning course 2018

01 - Introduction to Reinforcement Learning

YouTube

Slides

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

#DeepReinforcementLearning
#DeepMind

🔭 @DeepGravity

DeepMind Deep Reinforcement Learning course 2018

02 - Exploration and Exploitation

YouTube

Slides

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

#DeepReinforcementLearning
#DeepMind

🔭 @DeepGravity

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