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…
Machine Learning for Combinatorial Optimization: a Methodological Tour d’Horizon
"This paper surveys the recent attempts, both from the machine learning and operations research communities, at leveraging machine learning to solve combinatorial optimization problems. Given the hard nature of these problems, state-of-the-art methodologies involve algorithmic decisions that either require too much computing time or are not mathematically well defined. Thus, machine learning looks like a promising candidate to effectively deal with those decisions. We advocate for pushing further the integration of machine learning and combinatorial optimization and detail methodology to do so. A main point of the paper is seeing generic optimization problems as data points and inquiring what is the relevant distribution of problems to use for learning on a given task."
https://arxiv.org/pdf/1811.06128.pdf
"This paper surveys the recent attempts, both from the machine learning and operations research communities, at leveraging machine learning to solve combinatorial optimization problems. Given the hard nature of these problems, state-of-the-art methodologies involve algorithmic decisions that either require too much computing time or are not mathematically well defined. Thus, machine learning looks like a promising candidate to effectively deal with those decisions. We advocate for pushing further the integration of machine learning and combinatorial optimization and detail methodology to do so. A main point of the paper is seeing generic optimization problems as data points and inquiring what is the relevant distribution of problems to use for learning on a given task."
https://arxiv.org/pdf/1811.06128.pdf
How Relevant is the Turing Test in the Age of Sophisbots?
Popular culture has contemplated societies of thinking machines for generations, envisioning futures from utopian to dystopian. These futures are, arguably, here now-we find ourselves at the doorstep of technology that can at least simulate the appearance of thinking, acting, and feeling. The real question is: now what?
https://arxiv.org/pdf/1909.00056.pdf
#machine_learning #technology #ethics
Popular culture has contemplated societies of thinking machines for generations, envisioning futures from utopian to dystopian. These futures are, arguably, here now-we find ourselves at the doorstep of technology that can at least simulate the appearance of thinking, acting, and feeling. The real question is: now what?
https://arxiv.org/pdf/1909.00056.pdf
#machine_learning #technology #ethics
Noam Chomsky: Language, Cognition, and Deep Learning | Artificial Intelligence
Noam Chomsky is one of the greatest minds of our time and is one of the most cited scholars in history. He is a linguist, philosopher, cognitive scientist, historian, social critic, and political activist. He has spent over 60 years at MIT and recently also joined the University of Arizona. This conversation is part of the Artificial Intelligence podcast.
https://www.youtube.com/watch?v=cMscNuSUy0I
#natural_language_processing #deep_learning
Noam Chomsky is one of the greatest minds of our time and is one of the most cited scholars in history. He is a linguist, philosopher, cognitive scientist, historian, social critic, and political activist. He has spent over 60 years at MIT and recently also joined the University of Arizona. This conversation is part of the Artificial Intelligence podcast.
https://www.youtube.com/watch?v=cMscNuSUy0I
#natural_language_processing #deep_learning
YouTube
Noam Chomsky: Language, Cognition, and Deep Learning | Lex Fridman Podcast #53
Quantum Computer Programming
A practical and applied introduction to quantum computer programming, using IBM's free cloud-based quantum machines and Qiskit.
https://youtu.be/aPCZcv-5qfA
#quantum_programming
A practical and applied introduction to quantum computer programming, using IBM's free cloud-based quantum machines and Qiskit.
https://youtu.be/aPCZcv-5qfA
#quantum_programming
YouTube
Quantum Computer Programming w/ Qiskit
A practical and applied introduction to quantum computer programming, using IBM's free cloud-based quantum machines and Qiskit.
Part 2: https://www.youtube.com/watch?v=lB_5pC1MkGg
Text-based tutorials and sample code: https://pythonprogramming.net/quantum…
Part 2: https://www.youtube.com/watch?v=lB_5pC1MkGg
Text-based tutorials and sample code: https://pythonprogramming.net/quantum…
Programming a quantum computer with Cirq (QuantumCasts)
Want to learn how to program a quantum computer using Cirq? In this episode of QuantumCasts, Dave Bacon (Twitter: @dabacon) teaches you what a quantum program looks like via a simple “hello qubit” program. You’ll also learn about some of the exciting challenges facing quantum programmers today, such as whether Noisy Intermediate-Scale Quantum (NISQ) processors have the ability to solve important practical problems. We’ll also delve a little into how the open source Python framework Cirq was designed to help answer that question.
https://www.youtube.com/watch?v=16ZfkPRVf2w
#quantum_programming
Want to learn how to program a quantum computer using Cirq? In this episode of QuantumCasts, Dave Bacon (Twitter: @dabacon) teaches you what a quantum program looks like via a simple “hello qubit” program. You’ll also learn about some of the exciting challenges facing quantum programmers today, such as whether Noisy Intermediate-Scale Quantum (NISQ) processors have the ability to solve important practical problems. We’ll also delve a little into how the open source Python framework Cirq was designed to help answer that question.
https://www.youtube.com/watch?v=16ZfkPRVf2w
#quantum_programming
YouTube
Programming a quantum computer with Cirq (QuantumCasts)
Want to learn how to program a quantum computer using Cirq? In this episode of QuantumCasts, Dave Bacon (Twitter: @dabacon) teaches you what a quantum program looks like via a simple “hello qubit” program. You’ll also learn about some of the exciting challenges…
Adaptive_Computation_and_Machine.pdf
3.4 MB
Foundations Of Machine Learning
✅ A must read book for machine learning researchers
It mainly discusses the mathematical background of machine learning algorithms.
✅ A must read book for machine learning researchers
It mainly discusses the mathematical background of machine learning algorithms.