A curated list of resources dedicated to Natural Language Processing (NLP)
https://github.com/keon/awesome-nlp
#nlp #deep_learning
https://github.com/keon/awesome-nlp
#nlp #deep_learning
GitHub
GitHub - keon/awesome-nlp: :book: A curated list of resources dedicated to Natural Language Processing (NLP)
:book: A curated list of resources dedicated to Natural Language Processing (NLP) - keon/awesome-nlp
A fascinating Youtube Channel for learning various mathematical concepts:
https://www.youtube.com/channel/UCFe6jenM1Bc54qtBsIJGRZQ
#mathematics
https://www.youtube.com/channel/UCFe6jenM1Bc54qtBsIJGRZQ
#mathematics
YouTube
Patrick J
Howdy!
I've been creating free Mathematics videos since 2007 and continue to do so.
Teachers please feel free to reach out if I can help you although I do get a lot of emails! You can find my email below next to the 'For Business Inquiries' box!
If you…
I've been creating free Mathematics videos since 2007 and continue to do so.
Teachers please feel free to reach out if I can help you although I do get a lot of emails! You can find my email below next to the 'For Business Inquiries' box!
If you…
Lectures Slides of Signal Processing for Machine Learning Course by Stanfrod University
https://web.stanford.edu/class/ee269/slides.html
#mathematics #machine_learning
https://web.stanford.edu/class/ee269/slides.html
#mathematics #machine_learning
A great course about Digital Signal Processing, presented by EPFL
https://www.coursera.org/learn/dsp
#mathematics #electrical_engineering
https://www.coursera.org/learn/dsp
#mathematics #electrical_engineering
Coursera
Digital Signal Processing 1: Basic Concepts and Algorithms
Offered by École Polytechnique Fédérale de Lausanne. ... Enroll for free.
Good tips for multiprocessing in Python:
https://www.cloudcity.io/blog/2019/02/27/things-i-wish-they-told-me-about-multiprocessing-in-python/
#python #parallel_programming
https://www.cloudcity.io/blog/2019/02/27/things-i-wish-they-told-me-about-multiprocessing-in-python/
#python #parallel_programming
Cloud City Development
Things I Wish They Told Me About Multiprocessing in Python
Framing the problem
“Some people, when confronted with a problem, think ‘I know, I’ll use multithreading’. Nothhw tpe yawrve o oblems.” (Eiríkr Åsheim, 2012)
If multithreading is so problematic, though, how do we take advantage of systems with 8, 16, 32…
“Some people, when confronted with a problem, think ‘I know, I’ll use multithreading’. Nothhw tpe yawrve o oblems.” (Eiríkr Åsheim, 2012)
If multithreading is so problematic, though, how do we take advantage of systems with 8, 16, 32…
The Math of Machine Learning - Berkeley University Textbook
The mathematical skills you need for starting your journey into the field of Machine Learning
Note: It should be noted that It doesn't cover all the mathematical skills you need for doing ML during your life, It's just a brief textbook which could help you to start learning more complicated mathematical concepts in ML
https://www.datasciencecentral.com/profiles/blogs/tutorial-the-math-of-machine-learning-berkeley-university
#mahine_learning #mathematics
The mathematical skills you need for starting your journey into the field of Machine Learning
Note: It should be noted that It doesn't cover all the mathematical skills you need for doing ML during your life, It's just a brief textbook which could help you to start learning more complicated mathematical concepts in ML
https://www.datasciencecentral.com/profiles/blogs/tutorial-the-math-of-machine-learning-berkeley-university
#mahine_learning #mathematics
Data Science Central
The Math of Machine Learning - Berkeley University Textbook - DataScienceCentral.com
This document is an attempt to provide a summary of the mathematical background needed for an introductory class in machine learning, which at UC Berkeley is known as CS 189/289A. Our assumption is that the reader is already familiar with the basic concepts…
Pytest Library
The pytest framework makes it easy to write small tests, yet scales to support complex functional testing for applications and libraries.
https://doc.pytest.org/en/latest/#
#python #programming
The pytest framework makes it easy to write small tests, yet scales to support complex functional testing for applications and libraries.
https://doc.pytest.org/en/latest/#
#python #programming
Solving Rubik’s Cube with a Robot Hand
This is fascinating, make sure you read it.
Summary: OpenAI team trained a pair of neural networks to solve the Rubik’s Cube with a human-like robot hand. The neural networks are trained entirely in simulation, using the same reinforcement learning code as OpenAI Five paired with a new technique called Automatic Domain Randomization (ADR). The system can handle situations it never saw during training, such as being prodded by a stuffed giraffe. This shows that reinforcement learning isn’t just a tool for virtual tasks, but can solve physical-world problems requiring unprecedented dexterity.
https://openai.com/blog/solving-rubiks-cube/
#reinforcement_learning #machine_learning #robotics
This is fascinating, make sure you read it.
Summary: OpenAI team trained a pair of neural networks to solve the Rubik’s Cube with a human-like robot hand. The neural networks are trained entirely in simulation, using the same reinforcement learning code as OpenAI Five paired with a new technique called Automatic Domain Randomization (ADR). The system can handle situations it never saw during training, such as being prodded by a stuffed giraffe. This shows that reinforcement learning isn’t just a tool for virtual tasks, but can solve physical-world problems requiring unprecedented dexterity.
https://openai.com/blog/solving-rubiks-cube/
#reinforcement_learning #machine_learning #robotics
Openai
Solving Rubik’s Cube with a robot hand
We’ve trained a pair of neural networks to solve the Rubik’s Cube with a human-like robot hand. The neural networks are trained entirely in simulation, using the same reinforcement learning code as OpenAI Five paired with a new technique called Automatic…
Proximal Policy Optimization
Paper:
https://openai.com/blog/openai-baselines-ppo/
YouTube Video:
https://www.youtube.com/watch?v=5P7I-xPq8u8&list=PLLO4N3-FoY3feUsA3_XZvn5sXy9Ms8ayE&index=2
#reinforcement_learning #optimization
Paper:
https://openai.com/blog/openai-baselines-ppo/
YouTube Video:
https://www.youtube.com/watch?v=5P7I-xPq8u8&list=PLLO4N3-FoY3feUsA3_XZvn5sXy9Ms8ayE&index=2
#reinforcement_learning #optimization
Openai
Proximal Policy Optimization
We’re releasing a new class of reinforcement learning algorithms, Proximal Policy Optimization (PPO), which perform comparably or better than state-of-the-art approaches while being much simpler to implement and tune. PPO has become the default reinforcement…
PyTorch tutorial of various RL algorithms:
actor critic / proximal policy optimization / acer / ddpg / twin dueling ddpg / soft actor critic / generative adversarial imitation learning / hindsight experience replay
https://github.com/higgsfield/RL-Adventure-2
#reinforcement_learning #pytorch
actor critic / proximal policy optimization / acer / ddpg / twin dueling ddpg / soft actor critic / generative adversarial imitation learning / hindsight experience replay
https://github.com/higgsfield/RL-Adventure-2
#reinforcement_learning #pytorch
GitHub
GitHub - higgsfield-ai/higgsfield: Fault-tolerant, highly scalable GPU orchestration, and a machine learning framework designed…
Fault-tolerant, highly scalable GPU orchestration, and a machine learning framework designed for training models with billions to trillions of parameters - higgsfield-ai/higgsfield
Forwarded from Machine Learning World
Michio Kaku: Future of Humans, Aliens, Space Travel & Physics | Artificial Intelligence (AI) Podcast
Michio Kaku is a theoretical physicist, futurist, and professor at the City College of New York. He is the author of many fascinating books on the nature of our reality and the future of our civilization. This conversation is part of the Artificial Intelligence podcast.
https://www.youtube.com/watch?v=kD5yc1LQrpQ
#artificial_intelligence #physics #cosmology
Michio Kaku is a theoretical physicist, futurist, and professor at the City College of New York. He is the author of many fascinating books on the nature of our reality and the future of our civilization. This conversation is part of the Artificial Intelligence podcast.
https://www.youtube.com/watch?v=kD5yc1LQrpQ
#artificial_intelligence #physics #cosmology
YouTube
Michio Kaku: Future of Humans, Aliens, Space Travel & Physics | Lex Fridman Podcast #45
Fastest way for learning a new programming language for experts
If you are already an expert in programming, you can learn a new programming language as fast as possible through this website:
https://learnxinyminutes.com/
#programming
If you are already an expert in programming, you can learn a new programming language as fast as possible through this website:
https://learnxinyminutes.com/
#programming
A must read document for deep learning & machine learning practitioners
https://www.deeplearningbook.org/contents/guidelines.html
#deep_learning #machine_learning
https://www.deeplearningbook.org/contents/guidelines.html
#deep_learning #machine_learning
A fascinating research paper in the intersection of Graph Neural Networks and Reinforcement Learning for tackling Robotics challenges
https://openreview.net/pdf?id=S1sqHMZCb
#robotics #deep_learning #geometric_deep_learning
https://openreview.net/pdf?id=S1sqHMZCb
#robotics #deep_learning #geometric_deep_learning
Self-training with Noisy Student improves ImageNet classification
New state-of-the-art supervised+unsupervised algorithm on ImageNet
https://arxiv.org/abs/1911.04252
#machine_learning #neural_networks #meta_learning
New state-of-the-art supervised+unsupervised algorithm on ImageNet
https://arxiv.org/abs/1911.04252
#machine_learning #neural_networks #meta_learning
arXiv.org
Self-training with Noisy Student improves ImageNet classification
We present Noisy Student Training, a semi-supervised learning approach that works well even when labeled data is abundant. Noisy Student Training achieves 88.4% top-1 accuracy on ImageNet, which...
A Comprehensive Survey on Graph Neural Networks
Prerequisites concepts: Graph Signal Processing | Functional Analysis | Deep Learning Architectures
https://arxiv.org/abs/1901.00596
#geometric_deep_learning #graph_neural_networks
Prerequisites concepts: Graph Signal Processing | Functional Analysis | Deep Learning Architectures
https://arxiv.org/abs/1901.00596
#geometric_deep_learning #graph_neural_networks
arXiv.org
A Comprehensive Survey on Graph Neural Networks
Deep learning has revolutionized many machine learning tasks in recent years, ranging from image classification and video processing to speech recognition and natural language understanding. The...
Distill and Transfer Learning for Robust Multitask Reinforcement Learning
"Most deep reinforcement learning algorithms are data inefficient in complex and rich environments, limiting their applicability to many scenarios. One direction for improving data efficiency is multitask learning with shared neural network parameters, where efficiency may be improved through transfer across related tasks. In practice, however, this is not usually observed, because gradients from different tasks can interfere negatively, making learning unstable and sometimes even less data efficient. Another issue is the different reward schemes between tasks, which can easily lead to one task dominating the learning of a shared model. We propose a new approach for joint training of multiple tasks, which we refer to as Distral (DIStill & TRAnsfer Learning). Instead of sharing parameters between the different workers, we propose to share a distilled policy that captures common behavior across tasks. Each worker is trained to solve its own task while constrained to stay close to the shared policy, while the shared policy is trained by distillation to be the centroid of all task policies. Both aspects of the learning process are derived by optimizing a joint objective function. We show that our approach supports efficient transfer on complex 3D environments, outperforming several related methods. Moreover, the proposed learning process is more robust and more stable---attributes that are critical in deep reinforcement learning."
https://www.youtube.com/watch?v=scf7Przmh7c
#reinforcement_learning #multi_task_learning #transfer_learning
"Most deep reinforcement learning algorithms are data inefficient in complex and rich environments, limiting their applicability to many scenarios. One direction for improving data efficiency is multitask learning with shared neural network parameters, where efficiency may be improved through transfer across related tasks. In practice, however, this is not usually observed, because gradients from different tasks can interfere negatively, making learning unstable and sometimes even less data efficient. Another issue is the different reward schemes between tasks, which can easily lead to one task dominating the learning of a shared model. We propose a new approach for joint training of multiple tasks, which we refer to as Distral (DIStill & TRAnsfer Learning). Instead of sharing parameters between the different workers, we propose to share a distilled policy that captures common behavior across tasks. Each worker is trained to solve its own task while constrained to stay close to the shared policy, while the shared policy is trained by distillation to be the centroid of all task policies. Both aspects of the learning process are derived by optimizing a joint objective function. We show that our approach supports efficient transfer on complex 3D environments, outperforming several related methods. Moreover, the proposed learning process is more robust and more stable---attributes that are critical in deep reinforcement learning."
https://www.youtube.com/watch?v=scf7Przmh7c
#reinforcement_learning #multi_task_learning #transfer_learning
YouTube
Distill and transfer learning for robust multitask RL
Most deep reinforcement learning algorithms are data inefficient in complex and rich environments, limiting their applicability to many scenarios. One direction for improving data efficiency is multitask learning with shared neural network parameters, where…