Steps Toward Artificial Intelligence.pdf
5.8 MB
Discussions about several issues relevant to trial-and-error learning (Reinforcement Learning), including prediction,
expectation, and what this paper called the basic credit-assignment problem for complex reinforcement
learning systems: How do you distribute credit for success among the many
decisions that may have been involved in producing it? (Published 1961)
expectation, and what this paper called the basic credit-assignment problem for complex reinforcement
learning systems: How do you distribute credit for success among the many
decisions that may have been involved in producing it? (Published 1961)
StatisticalLearningTheory.pdf
359.2 KB
A Brief introduction to Statistical Learning theory
Computational Learning Theory versus Statistical Learning Theory
While both frameworks use similar mathematical analysis, the primary difference between CoLT and SLT are their objectives. CoLT focuses on studying “learnability,” or what functions/features are necessary to make a given task learnable for an algorithm. Whereas SLT is primarily focused on studying and improving the accuracy of existing training programs.
https://deepai.org/machine-learning-glossary-and-terms/computational-learning-theory
While both frameworks use similar mathematical analysis, the primary difference between CoLT and SLT are their objectives. CoLT focuses on studying “learnability,” or what functions/features are necessary to make a given task learnable for an algorithm. Whereas SLT is primarily focused on studying and improving the accuracy of existing training programs.
https://deepai.org/machine-learning-glossary-and-terms/computational-learning-theory
DeepAI
Computational Learning Theory
Computational Learning Theory (CoLT) is a field of AI research studying the design of machine learning algorithms to determine what sorts of problems are “learnable.”
sgd.pdf
3.9 MB
A brief introduction to stochastic approximation theory
Brains, Minds, and Machines summer course
https://www.youtube.com/watch?v=_svW8NV1A6k&list=PLUl4u3cNGP61RTZrT3MIAikp2G5EEvTjf&index=1
https://www.youtube.com/watch?v=_svW8NV1A6k&list=PLUl4u3cNGP61RTZrT3MIAikp2G5EEvTjf&index=1
YouTube
Lecture 0: Tomaso Poggio - Introduction to Brains, Minds, and Machines
MIT RES.9-003 Brains, Minds and Machines Summer Course, Summer 2015 View the complete course: https://ocw.mit.edu/RES-9-003SU15 Instructor: Tomaso Poggio Int...
How to write a book.pdf
548.2 KB
A 20 steps guide to write a book
Forwarded from Deleted Account
🔻پنجمین دورهمی مدرسه جهانی هوش مصنوعی در رشت
🔺School of AI
📘موضوع این جلسه: عصب شناسی محاسباتی و رابطه آن با هوش مصنوعی
🗓دوشنبه 11 شهریور 1398
🕐ساعت 16:30 الی 19:00
🏢 رشت، میدان انتظام (پل رازی)، پارک علم و فناوری گیلان
*جهت ثبت نام به لینک زیر مراجعه کنید:
▪️https://evnd.co/WtokW
برای کسب اطلاعات بیشتر درباره ی این مدرسه، به کانال زیر مراجعه کنید:
🏢 @schoolofairasht
✅ @Brainandcognition_GU
🔺School of AI
📘موضوع این جلسه: عصب شناسی محاسباتی و رابطه آن با هوش مصنوعی
🗓دوشنبه 11 شهریور 1398
🕐ساعت 16:30 الی 19:00
🏢 رشت، میدان انتظام (پل رازی)، پارک علم و فناوری گیلان
*جهت ثبت نام به لینک زیر مراجعه کنید:
▪️https://evnd.co/WtokW
برای کسب اطلاعات بیشتر درباره ی این مدرسه، به کانال زیر مراجعه کنید:
🏢 @schoolofairasht
✅ @Brainandcognition_GU
What is “ML Ops”? Best Practices for DevOps for ML
https://www.itsalways10.com/what-is-ml-ops-best-practices-for-devops-for-ml/
https://www.itsalways10.com/what-is-ml-ops-best-practices-for-devops-for-ml/
A Shared Vision for Machine Learning in Neuroscience
https://www.jneurosci.org/content/jneuro/38/7/1601.full.pdf
https://www.jneurosci.org/content/jneuro/38/7/1601.full.pdf
Deep Neural Networks in Computational Neuroscience
https://www.biorxiv.org/content/biorxiv/early/2017/05/04/133504.full.pdf
https://www.biorxiv.org/content/biorxiv/early/2017/05/04/133504.full.pdf
Forwarded from Tensorflow(@CVision) (Vahid Reza Khazaie)
Fast-Bert
This library will help you build and deploy BERT based models within minutes:
Fast-Bert is the deep learning library that allows developers and data scientists to train and deploy BERT and XLNet based models for natural language processing tasks beginning with Text Classification.
The work on FastBert is built on solid foundations provided by the excellent Hugging Face BERT PyTorch library and is inspired by fast.ai and strives to make the cutting edge deep learning technologies accessible for the vast community of machine learning practitioners.
With FastBert, you will be able to:
Train (more precisely fine-tune) BERT, RoBERTa and XLNet text classification models on your custom dataset.
Tune model hyper-parameters such as epochs, learning rate, batch size, optimiser schedule and more.
Save and deploy trained model for inference (including on AWS Sagemaker).
Fast-Bert will support both multi-class and multi-label text classification for the following and in due course, it will support other NLU tasks such as Named Entity Recognition, Question Answering and Custom Corpus fine-tuning.
Blog post: https://medium.com/huggingface/introducing-fastbert-a-simple-deep-learning-library-for-bert-models-89ff763ad384
Code: https://github.com/kaushaltrivedi/fast-bert
#language_model #BERT
This library will help you build and deploy BERT based models within minutes:
Fast-Bert is the deep learning library that allows developers and data scientists to train and deploy BERT and XLNet based models for natural language processing tasks beginning with Text Classification.
The work on FastBert is built on solid foundations provided by the excellent Hugging Face BERT PyTorch library and is inspired by fast.ai and strives to make the cutting edge deep learning technologies accessible for the vast community of machine learning practitioners.
With FastBert, you will be able to:
Train (more precisely fine-tune) BERT, RoBERTa and XLNet text classification models on your custom dataset.
Tune model hyper-parameters such as epochs, learning rate, batch size, optimiser schedule and more.
Save and deploy trained model for inference (including on AWS Sagemaker).
Fast-Bert will support both multi-class and multi-label text classification for the following and in due course, it will support other NLU tasks such as Named Entity Recognition, Question Answering and Custom Corpus fine-tuning.
Blog post: https://medium.com/huggingface/introducing-fastbert-a-simple-deep-learning-library-for-bert-models-89ff763ad384
Code: https://github.com/kaushaltrivedi/fast-bert
#language_model #BERT
Medium
Introducing FastBert — A simple Deep Learning library for BERT Models
A simple to use Deep Learning library to build and deploy BERT models
Neuroscience and Reinforcement Learning
#neuroscience #reinforcement_learning
https://www.princeton.edu/~yael/ICMLTutorial.pdf
#neuroscience #reinforcement_learning
https://www.princeton.edu/~yael/ICMLTutorial.pdf
Deep Learning and Computational Neuroscience
#neuroscience #reinforcement_learning
https://link.springer.com/article/10.1007/s12021-018-9360-6
#neuroscience #reinforcement_learning
https://link.springer.com/article/10.1007/s12021-018-9360-6
Neuroinformatics
Deep Learning and Computational Neuroscience
Neuroinformatics -
The Roles of Supervised Machine Learning in Systems Neuroscience
Over the last several years, the use of machine learning (ML) in neuroscience has been rapidly increasing. Here, we review ML’s contributions, both realized and potential, across several areas of systems neuroscience. We describe four primary roles of ML within neuroscience: 1) creating solutions to engineering problems, 2) identifying predictive variables, 3) setting benchmarks for simple models of the brain, and 4) serving itself as a model for the brain. The breadth and ease of its applicability suggests that machine learning should be in the toolbox of most systems neuroscientists.
https://arxiv.org/ftp/arxiv/papers/1805/1805.08239.pdf
#neuroscience #machine_learning
Over the last several years, the use of machine learning (ML) in neuroscience has been rapidly increasing. Here, we review ML’s contributions, both realized and potential, across several areas of systems neuroscience. We describe four primary roles of ML within neuroscience: 1) creating solutions to engineering problems, 2) identifying predictive variables, 3) setting benchmarks for simple models of the brain, and 4) serving itself as a model for the brain. The breadth and ease of its applicability suggests that machine learning should be in the toolbox of most systems neuroscientists.
https://arxiv.org/ftp/arxiv/papers/1805/1805.08239.pdf
#neuroscience #machine_learning