DRL.part3.rar
245.4 MB
DeepMind Deep Reinforcement Learning course 2018 (all lectures and slides) - part3
#DeepReinforcementLearning
#DeepMind
🔭 @DeepGravity
#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
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
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
Nature
Grandmaster level in StarCraft II using multi-agent reinforcement learning
AlphaStar uses a multi-agent reinforcement learning algorithm and has reached Grandmaster level, ranking among the top 0.2% of human players for the real-time strategy game StarCraft II.
@DeepGravity - Stanford cs231n.part1.rar
1000 MB
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
⚠️ 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
@DeepGravity - Stanford cs231n.part2.rar
1000 MB
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
⚠️ 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
@DeepGravity - Stanford cs231n.part3.rar
229.5 MB
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
⚠️ 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
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
Quanta Magazine
Computers Evolve a New Path Toward Human Intelligence
By ignoring their goals, evolutionary algorithms have solved longstanding challenges in artificial intelligence.
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
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
KDnuggets
Topics Extraction and Classification of Online Chats - KDnuggets
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…
#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
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
ZDNet
Python programming language creator retires, saying: 'It's been an amazing ride'
The creator of the world's most popular programming language goes into retirement.
#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
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
MIT Technology Review
A neural net solves the three-body problem 100 million times faster
Machine learning provides an entirely new way to tackle one of the classic problems of applied mathematics.
@DeepGravity - Ali Ghodsi Lectures.part1.rar
1000 MB
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
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
@DeepGravity - Ali Ghodsi Lectures.part2.rar
772.6 MB
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
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
#Tensorflow 2.0 coding workshop notebooks
At our meetup Data Science for Internet of Things, Dan Howarth conducted a workshop on tensorflow 2.0
we plan to convert it into another book on data science central. for a set of all previous free books see free datascience books. The notebooks are
tensorflow 2.0: Notebook 1: 'Hello World' Deep Learning with Tensor...
tensorflow 2.0: Notebook 2: Computer Vision with CNNs
tensorflow 2.0: Notebook 3: Transfer Learning
You can see the tensorflow 2.0 roadmap and overall features of tensorflow 2.0. Comments welcome. We hope you like them.
Link to the paper
🔭 @DeepGravity
At our meetup Data Science for Internet of Things, Dan Howarth conducted a workshop on tensorflow 2.0
we plan to convert it into another book on data science central. for a set of all previous free books see free datascience books. The notebooks are
tensorflow 2.0: Notebook 1: 'Hello World' Deep Learning with Tensor...
tensorflow 2.0: Notebook 2: Computer Vision with CNNs
tensorflow 2.0: Notebook 3: Transfer Learning
You can see the tensorflow 2.0 roadmap and overall features of tensorflow 2.0. Comments welcome. We hope you like them.
Link to the paper
🔭 @DeepGravity
Google
Google Colaboratory
#Netflix Open Sources Polynote to Make #DataScience Notebooks Better
The new notebook environment provides substantial improvements to streamline experimentation in #MachineLearning workflows.
Link to the paper
🔭 @DeepGravity
The new notebook environment provides substantial improvements to streamline experimentation in #MachineLearning workflows.
Link to the paper
🔭 @DeepGravity
Medium
Netflix Open Sources Polynote to Make Data Science Notebooks Better
Notebooks are the data scientist best friend and can also be a nightmare to work with. For someone accustomed to work with modern…
Don’t Ever Ignore #ReinforcementLearning Again
#Supervised or #unsupervised learning is not everything. Everyone knows that. Get started with #OpenAI #Gym.
Link to the article
🔭 @DeepGravity
#Supervised or #unsupervised learning is not everything. Everyone knows that. Get started with #OpenAI #Gym.
Link to the article
🔭 @DeepGravity
Medium
Don’t Ever Ignore Reinforcement Learning Again
Supervised or unsupervised learning is not everything. Everyone knows that. Get started with OpenAI Gym.
Top 10 roles in AI and data science
0 Data Engineer
1 Decision-Maker
2 Analyst
3 Expert Analyst
4 Statistician
5 Applied Machine Learning Engineer
6 Data Scientist
7 Analytics Manager / Data Science Leader
8 Qualitative Expert / Social Scientist
9 Researcher
10+ Additional personnel
Read the article here
🔭 @DeepGravity
0 Data Engineer
1 Decision-Maker
2 Analyst
3 Expert Analyst
4 Statistician
5 Applied Machine Learning Engineer
6 Data Scientist
7 Analytics Manager / Data Science Leader
8 Qualitative Expert / Social Scientist
9 Researcher
10+ Additional personnel
Read the article here
🔭 @DeepGravity
Hackernoon
Top 10 roles in AI and data science | HackerNoon
When you think of the perfect data science team, are you imagining 10 copies of the same professor of computer science and statistics, hands delicately stained with whiteboard marker? I hope not!
The Fundamentals of #Matplotlib
Having a good grasp of these basics will greatly ease your foray into the expansive world of data visualization.
Link to the article
🔭 @DeepGravity
Having a good grasp of these basics will greatly ease your foray into the expansive world of data visualization.
Link to the article
🔭 @DeepGravity
Medium
The Fundamentals of Matplotlib
Having a good grasp of these basics will greatly ease your foray into the expansive world of data visualization.
Postdoctoral / Research Staff Member Position at IBM Research (Zurich) on Unifying Learning and Reasoning
Post-doc position in machine listening/Inria Nancy -- Grand Est, France
PhD Position in Deep Learning for Robotics at Istanbul Technical University (Turkey), in collaboration with Halmstad University (Sweden)
Positions in Machine Learning and Game Playing
Robot/Reinforcement/Deep Learning Research Associate Positions in Cyprus
#Job
🔭 @DeepGravity
Post-doc position in machine listening/Inria Nancy -- Grand Est, France
PhD Position in Deep Learning for Robotics at Istanbul Technical University (Turkey), in collaboration with Halmstad University (Sweden)
Positions in Machine Learning and Game Playing
Robot/Reinforcement/Deep Learning Research Associate Positions in Cyprus
#Job
🔭 @DeepGravity
Ibm
IBM Research Zurich, Careers
careers, jobs, IBM research zurich
A new tool uses #AI to spot text written by AI
AI algorithms can generate #text convincing enough to fool the average human—potentially providing a way to mass-produce fake news, bogus reviews, and phony social accounts. Thankfully, AI can now be used to identify fake text, too.
Link to the article
🔭 @DeepGravity
AI algorithms can generate #text convincing enough to fool the average human—potentially providing a way to mass-produce fake news, bogus reviews, and phony social accounts. Thankfully, AI can now be used to identify fake text, too.
Link to the article
🔭 @DeepGravity
MIT Technology Review
A new tool uses AI to spot text written by AI
AI algorithms can generate text convincing enough to fool the average human—potentially providing a way to mass-produce fake news, bogus reviews, and phony social accounts. Thankfully, AI can now be used to identify fake text, too. The news: Researchers from…