​Private AI — Federated Learning with PySyft and PyTorch

🔗 Private AI — Federated Learning with PySyft and PyTorch
An application to SMS spam detection with a GRU model

🎥 4 Ways to Use Machine Learning for Mobile
👁 1 раз ⏳ 3020 сек.
Learn more about AWS Startups at – https://amzn.to/2WG04um 
Machine Learning at the edge is about making your apps smarter with real-time object and face recognition, offline predictions, and protecting user privacy. In this session, we'll explore the four ways to handle prediction or inference and decision making in modern apps. Demystify deep learning and easily call managed ML services, build, train, and/or deploy ML models to mobile and IoT devices.

🎥 An Introduction to Deep Learning
👁 1 раз ⏳ 2627 сек.
Learn more about AWS Startups at – https://amzn.to/2WhgEwo 
Getting started with deep learning can feel really intimidating. In this session we'll dive right in to explaining the basic concepts of deep learning with barely any jargon and a small amount of arithmetic. We'll see how easy it is to take an existing pre-trained general-purpose image classification model from the cloud and re-train it to identify objects that we want the computer to recognize. We'll show how to do all of this with python, using a

🎥 My Experience With Deep Learning and TensorFlow | Nathan Hadjiyski | TEDxYouth@AnnArbor
👁 1 раз ⏳ 674 сек.
Nathan Hadjiyski is a 10th grader at Pioneer High School. He has been interested in science from young age and his passion for it continues to grow. He is trying to benefit society with his cancer research, and he hopes it could ultimately help patients get a more accurate diagnosis and as a result more adequate treatment. Nathan Hadjiyski’s presentation is about his experience with Deep Learning and Tensor Flow applied to kidney cancer diagnosis. Nathan Hadjiyski is a 10th grader at Pioneer High School. He

🎥 Обзор ICLR 2019
👁 1 раз ⏳ 3171 сек.
Девятого мая закончилась очередная International Conference on Learning Representations (ICLR). Мы сделаем обзор публикаций с ICLR, которые нам показались наиболее интересными.

Докладчик: Рауф Курбанов.

Ссылка на слайды: https://research.jetbrains.org/files/material/5cf7a348264f3.pdf

Автор поделился крутым опытом по разработке ботов для Слака на Питоне http://bit.ly/2WeNb67

​AI Replaces Human Appraisers stardate 2019.420

🔗 AI Replaces Human Appraisers stardate 2019.420
All the data that matters:

​ARTificial: на заре искусственного интеллекта

Наш телеграм канал - tglink.me/ai_machinelearning_big_data

Как думаете, может ли искусственный интеллект творить? Или же он просто бездушная машина, способная лишь копировать?

За последние 10 лет произошел колоссальный прорыв в развитии искусственного интеллекта. Созданный человеком алгоритм прошел путь от простого распознавания образов до побед в самых разнообразных играх. Однако одна из самых эмоциональных и экспрессивных сфер деятельности человека – искусство – ему все еще неподвластна. Или нет? Это мы и предложили решить гостям закрытой выставки, которая расположилась в Музее русского импрессионизма на один день 29 мая.

🔗 ARTificial: на заре искусственного интеллекта
Как думаете, может ли искусственный интеллект творить? Или же он просто бездушная машина, способная лишь копировать? За последние 10 лет произошел колоссальный...

​Depth Estimation on Camera Images using DenseNets

🔗 Depth Estimation on Camera Images using DenseNets
Doing cool things with data!

​New interesting paper to read, on face generation(faster then GANs)
https://arxiv.org/abs/1906.00446

🔗 Generating Diverse High-Fidelity Images with VQ-VAE-2
We explore the use of Vector Quantized Variational AutoEncoder (VQ-VAE) models for large scale image generation. To this end, we scale and enhance the autoregressive priors used in VQ-VAE to generate synthetic samples of much higher coherence and fidelity than possible before. We use simple feed-forward encoder and decoder networks, making our model an attractive candidate for applications where the encoding and/or decoding speed is critical. Additionally, VQ-VAE requires sampling an autoregressive model only in the compressed latent space, which is an order of magnitude faster than sampling in the pixel space, especially for large images. We demonstrate that a multi-scale hierarchical organization of VQ-VAE, augmented with powerful priors over the latent codes, is able to generate samples with quality that rivals that of state of the art Generative Adversarial Networks on multifaceted datasets such as ImageNet, while not suffering from GAN's known shortcomings such as mode collapse and lack of diversity.

​https://youtu.be/pgaEE27nsQw

Кто нибудь знает название пироги, либо аналоги в свободном доступе ?

🔗 Flexible Muscle-Based Locomotion for Bipedal Creatures
We present a control method for simulated bipeds, in which natural gaits are discovered through optimization. No motion capture or key frame animation was used in any of the results. For more information, see http://goatstream.com/research/papers/SA2013

Unsupervised Object Segmentation by Redrawing
https://arxiv.org/abs/1905.13539

🎥 ИИ и машинное обучение Рустам Гафаров Тренды в машинном обучении
👁 1 раз ⏳ 4970 сек.

​Alexa, Alex, or Al?

🔗 Alexa, Alex, or Al?
3 Suggestions to Fight Gender Biases in AI Assistants

🎥 How to Structure Testing of Deep Learning Systems
👁 1 раз ⏳ 412 сек.
Sergey Karayev (https://twitter.com/sergeykarayev) shares a framework for thinking about the different modules of a deep learning production system, and the types of tests they require.

Recorded during the Spring 2019 Full Stack Deep Learning Bootcamp (https://fullstackdeeplearning.com/march2019).

https://www.youtube.com/watch?v=tDvqb4Q5NhI
как

🎥 Practice of Machine Learning - Google AI Impact Challenge Accelerator
👁 3 раз ⏳ 914 сек.
Peter Norvig, Google AI Director of Research at Google, discusses how machine learning fits into changing the world.

The Google AI Impact Challenge Accelerator brings together organizations from around the world to submit their ideas on how to use AI for social good.

Watch the whole playlist → https://goo.gle/2QUFHnU

Subscribe to Launchpad to learn all about startups → https://goo.gle/GDevLaunchpad

🎥 Types of Machine Learning 2
👁 1 раз ⏳ 409 сек.
This lecture gives an overview of the main categories of machine learning, including supervised, un-supervised, and semi-supervised techniques, depending on the availability of expert labels. We also discuss the different methods to handle discrete versus continuous labels.

Book website: http://databookuw.com/
Steve Brunton's website: eigensteve.com

🎥 Machine Learning Goals
👁 2 раз ⏳ 465 сек.
This lecture discusses the high-level goals of machine learning, and what we want out of our models. Goals include speed and accuracy, along with interpretability, generalizability, explainability, certifiability.

Book website: http://databookuw.com/
Steve Brunton's website: eigensteve.com

​Algorithms for better decision making

🔗 Algorithms for better decision making
Algorithms are complex mathematical equations for computers that has little practical use for people, or are they? In reality, an…

​Deep Learning Gallery - a curated list of awesome deep learning projects

🔗 Deep Learning Gallery - a curated list of awesome deep learning projects
Showcase of the best deep learning algorithms and deep learning applications.