​SNA Hackathon 2019: усложняем архитектуру — упрощаем признаки

https://habr.com/ru/company/mailru/blog/445348/

🔗 SNA Hackathon 2019: усложняем архитектуру — упрощаем признаки
В этой статье я расскажу про свое решение текстовой части задачи SNA Hackathon 2019. Какие-то из предложенных идей будут полезны участникам очной части хакатон...

​snakers4/gpu-box-setup

🔗 snakers4/gpu-box-setup
Contribute to snakers4/gpu-box-setup development by creating an account on GitHub.

​Quantum Machine Learning - Prof. Lilienfeld

🔗 Quantum Machine Learning - Prof. Lilienfeld
Prof. O. Anatole von Lilienfeld of the University of Bassel presented his labs work on Quantum Machine Learning at the 2017 Conference on Neural Information ...

​https://arxiv.org/abs/1903.08114

🔗 Exact Gaussian Processes on a Million Data Points
Gaussian processes (GPs) are flexible models with state-of-the-art performance on many impactful applications. However, computational constraints with standard inference procedures have limited exact GPs to problems with fewer than about ten thousand training points, necessitating approximations for larger datasets. In this paper, we develop a scalable approach for exact GPs that leverages multi-GPU parallelization and methods like linear conjugate gradients, accessing the kernel matrix only through matrix multiplication. By partitioning and distributing kernel matrix multiplies, we demonstrate that an exact GP can be trained on over a million points in 3 days using 8 GPUs and can compute predictive means and variances in under a second using 1 GPU at test time. Moreover, we perform the first-ever comparison of exact GPs against state-of-the-art scalable approximations on large-scale regression datasets with $10^4-10^6$ data points, showing dramatic performance improvements.

​A quantum version of the building block behind neural networks could be exponentially more powerful. By Emerging Technology from the arXiv: https://www.technologyreview.com/…/machine-learning-meet-q…/

An Artificial Neuron Implemented on an Actual Quantum Processor, https://arxiv.org/abs/1811.02266

#artificialinteligence #quantumcomputing #neuralnetworks #machinelearning #processors

🔗 An Artificial Neuron Implemented on an Actual Quantum Processor
Artificial neural networks are the heart of machine learning algorithms and artificial intelligence protocols. Historically, the simplest implementation of an artificial neuron traces back to the classical Rosenblatt's `perceptron', but its long term practical applications may be hindered by the fast scaling up of computational complexity, especially relevant for the training of multilayered perceptron networks. Here we introduce a quantum information-based algorithm implementing the quantum computer version of a perceptron, which shows exponential advantage in encoding resources over alternative realizations. We experimentally test a few qubits version of this model on an actual small-scale quantum processor, which gives remarkably good answers against the expected results. We show that this quantum model of a perceptron can be used as an elementary nonlinear classifier of simple patterns, as a first step towards practical training of artificial quantum neural networks to be efficiently implemented o

​Illustrated: Efficient Neural Architecture Search

🔗 Illustrated: Efficient Neural Architecture Search
Macro and micro search strategies in ENAS

🎥 Really Quick Questions - Nvidia Research
👁 21 раз ⏳ 490 сек.
Nvidia is the inventor of the GPU! This tech company based in Silicon Valley has played a huge role in the deep learning revolution (which has relied primarily on GPUs for computing), and its transformed many industries. In this video, I interview Bryan Catanzaro, the Vice President of Applied Deep Learning Research at NVIDIA. Bryan got his PhD in AI from Berkeley, invented a language called Copperhead, and is an expert in parallel programming theory. Nvidia invited me to their annual conference, so I took

​Making Art With Your Webcam

🔗 Making Art With Your Webcam
An implementation and explanation of Fast Style Transfer with a precursor in Basic Style Transfer

​Pruned Cross Validation for hyperparameter optimization

🔗 Pruned Cross Validation for hyperparameter optimization
The technique’s motivation, design, and implementation

Курс «NLP with Deep Learning»

Наш телеграм канал - tglink.me/ai_machinelearning_big_data
1. Natural Language Processing with Deep Learning
2. Word Vector Representations: word2vec
3. GloVe: Global Vectors for Word Representation
4. Word Window Classification and Neural Networks
5. Backpropagation and Project Advice
6. Dependency Parsing
7. Introduction to TensorFlow
8. Recurrent Neural Networks and Language Models
9. Machine Translation and Advanced Recurrent LSTMs and GRUs

🎥 Lecture 1 | Natural Language Processing with Deep Learning
👁 877 раз ⏳ 4301 сек.
Lecture 1 introduces the concept of Natural Language Processing (NLP) and the problems NLP faces today. The concept of representing words as numeri...

🎥 Lecture 2 | Word Vector Representations: word2vec
👁 115 раз ⏳ 4697 сек.
Lecture 2 continues the discussion on the concept of representing words as numeric vectors and popular approaches to designing word vectors.

Key ...

🎥 Lecture 3 | GloVe: Global Vectors for Word Representation
👁 132 раз ⏳ 4720 сек.
Lecture 3 introduces the GloVe model for training word vectors. Then it extends our discussion of word vectors (interchangeably called word embeddi...

🎥 Lecture 4: Word Window Classification and Neural Networks
👁 72 раз ⏳ 4603 сек.
Lecture 4 introduces single and multilayer neural networks, and how they can be used for classification purposes.

Key phrases: Neural networks. Fo...

🎥 Lecture 5: Backpropagation and Project Advice
👁 31 раз ⏳ 4700 сек.
Lecture 5 discusses how neural networks can be trained using a distributed gradient descent technique known as back propagation.

Key phrases: Neur...

🎥 Lecture 6: Dependency Parsing
👁 50 раз ⏳ 4987 сек.
Lecture 6 covers dependency parsing which is the task of analyzing the syntactic dependency structure of a given input sentence S. The output of a ...

🎥 Lecture 7: Introduction to TensorFlow
👁 72 раз ⏳ 4354 сек.
Lecture 7 covers Tensorflow. TensorFlow is an open source software library for numerical computation using data flow graphs. It was originally deve...

🎥 Lecture 8: Recurrent Neural Networks and Language Models
👁 36 раз ⏳ 4683 сек.
Lecture 8 covers traditional language models, RNNs, and RNN language models. Also reviewed are important training problems and tricks, RNNs for oth...

​В коре вашего мозга 17 млрд компьютеров

В мозг поступает информация из внешнего мира, его нейроны получают данные на входе, производят обработку и выдают некий результат. Это может быть мысль (хочу карри на ужин), действие (сделать карри), изменение настроения (ура, карри!). Что бы ни получилось на выходе, это «что-то» является преобразованием данных со входа (меню) в результат на выходе («куриный дхансак, пожалуйста»). И если представлять мозг как преобразователь с входом в выходом, то неизбежна аналогия с компьютером.

Для одних это просто полезный риторический приём, для других — серьёзная идея. Но мозг — это не компьютер. Компьютером является каждый нейрон. В коре головного мозга 17 миллиардов компьютеров.
https://habr.com/ru/post/445420/

🔗 В коре вашего мозга 17 млрд компьютеров
Нейросеть нейросетей Изображение brentsview под лицензией CC BY-NC 2.0 В мозг поступает информация из внешнего мира, его нейроны получают данные на входе, про...

​Tuning a Multi-Task Fate Grand Order Trained Pytorch Network

🔗 Tuning a Multi-Task Fate Grand Order Trained Pytorch Network
building and tuning pytorch based multi-task networks

Watch Me Build an AI Startup
https://www.youtube.com/watch?v=NzmoPqte4V4

🎥 Watch Me Build an AI Startup
👁 6 раз ⏳ 2374 сек.
I'm going to build a medical imaging classification app called SmartMedScan! The potential customers for this app are medical professionals that need to scale and improve the accuracy of their diagnoses using AI. From ideation, to logo design, to integrating features like payments and AI into a single app, I'll show you my 10 step process. I hope that by seeing my thought process and getting familiar with the sequence of steps I'll demonstrate,, you too will be as inspired as I am to use this technology to

​Python Tutorial: A Name Lookup Table for Fuzzy Name Data Sets

🔗 Python Tutorial: A Name Lookup Table for Fuzzy Name Data Sets
Increase accuracy in person name matching by using name component combinations

🎥 This AI is The “Photoshop” of Human Faces
👁 1 раз ⏳ 192 сек.
❤️ Pick up cool perks on our Patreon page: https://www.patreon.com/TwoMinutePapers

📝 The paper "SC-FEGAN: Face Editing Generative Adversarial Network with User's Sketch and Color" is available here:
https://arxiv.org/abs/1902.06838
https://github.com/JoYoungjoo/SC-FEGAN

🙏 We would like to thank our generous Patreon supporters who make Two Minute Papers possible:
313V, Alex Haro, Andrew Melnychuk, Angelos Evripiotis, Anthony Vdovitchenko, Brian Gilman, Christian Ahlin, Christoph Jadanowski, Claudio Fernand

​Geo Experiments, The Perfect Complement to A/B Testing

🔗 Geo Experiments, The Perfect Complement to A/B Testing
What Is It and How Will It Help You In Marketing?

🎥 Getting Started with TensorFlow and Deep Learning | SciPy 2018 Tutorial | Josh Gordon
👁 1 раз ⏳ 9679 сек.
A friendly introduction to Deep Learning, taught at the beginner level. We’ll work through introductory exercises across several domains - including computer vision, natural language processing, and structured data classification. We’ll introduce TensorFlow - the world’s most popular open source machine learning library - preview the latest APIs (including Eager Execution), discuss best practices, and point you to recommended educational resources you can use to learn more.

Tutorial instructions may be fou

​Другой Github 2: машинное обучение, датасеты и Jupyter Notebooks
Несмотря на то, что в интернете существует множество источников свободного программного обеспечения для машинного обучения, Github остается важным центром обмена информацией для всех типов инструментов с открытым исходным кодом, используемых в сообществе специалистов по машинному обучению и анализу данных.

В этой подборке собраны репозитории по машинному обучению, датасетам и Jupyter Notebooks, ранжированные по количеству звезд. В предыдущей части мы рассказывали о популярных репозиториях для изучения работ по визуализации данных и глубокому обучению.
https://habr.com/ru/company/mailru/blog/445530/

🔗 Другой Github 2: машинное обучение, датасеты и Jupyter Notebooks
Несмотря на то, что в интернете существует множество источников свободного программного обеспечения для машинного обучения, Github остается важным центром обме...

​Neural Quantum States — представление волновой функции нейронной сетью

В этой статье мы рассмотрим необычное применение нейронных сетей в целом и ограниченных машин Больцмана в частности для решения двух сложных задач квантовой механики — поиска энергии основного состояния и аппроксимации волновой функции системы многих тел.
https://habr.com/ru/company/raiffeisenbank/blog/445516/

🔗 Neural Quantum States — представление волновой функции нейронной сетью
В этой статье мы рассмотрим необычное применение нейронных сетей в целом и ограниченных машин Больцмана в частности для решения двух сложных задач квантовой меха...

​Unifying Physics and Deep Learning with TossingBot

🔗 Unifying Physics and Deep Learning with TossingBot
Posted by Andy Zeng, Student Researcher, Robotics at Google Though considerable progress has been made in enabling robots to grasp objec...