🎥 Introduction to Deep Learning Recitation 11
👁 1 раз ⏳ 3021 сек.

🎥 Stanford CS224N: NLP with Deep Learning | Winter 2019 | Lecture 6 – Language Models and RNNs
👁 1 раз ⏳ 4105 сек.
Professor Christopher Manning & PhD Candidate Abigail See, Stanford University
http://onlinehub.stanford.edu/

Professor Christopher Manning
Thomas M. Siebel Professor in Machine Learning, Professor of Linguistics and of Computer Science
Director, Stanford Artificial Intelligence Laboratory (SAIL)

To follow along with the course schedule and syllabus, visit: http://web.stanford.edu/class/cs224n/index.html#schedule

To get the latest news on Stanford’s upcoming professional programs in Artificial Intelli

🎥 Stanford CS224N: NLP with Deep Learning | Winter 2019 | Lecture 20 – Future of NLP + Deep Learning
👁 1 раз ⏳ 4755 сек.
Professor Christopher Manning & Guest Speaker Kevin Clark, Stanford University
http://onlinehub.stanford.edu/

Professor Christopher Manning
Thomas M. Siebel Professor in Machine Learning, Professor of Linguistics and of Computer Science
Director, Stanford Artificial Intelligence Laboratory (SAIL)

To follow along with the course schedule and syllabus, visit: http://web.stanford.edu/class/cs224n/index.html#schedule

To get the latest news on Stanford’s upcoming professional programs in Artificial Intelli

​https://arxiv.org/pdf/1904.06778v1.pdf

🔗

​When Data is Scarce… Ways to Extract Valuable Insights

🔗 When Data is Scarce… Ways to Extract Valuable Insights
Descriptive statistics, Exploratory Data Analysis, and Natural Language Processing (NLP) techniques to understand your data.

🎥 Ian Goodfellow: Generative Adversarial Networks (GANs) | MIT Artificial Intelligence (AI) Podcast
👁 6 раз ⏳ 4117 сек.
Ian Goodfellow is an author of the popular textbook on deep learning (simply titled "Deep Learning"). He invented Generative Adversarial Networks (GANs) and with his 2014 paper is responsible for launching the incredible growth of research on GANs. He got his BS and MS at Stanford, his PhD at University of Montreal with Yoshua Bengio and Aaron Courville. He held several research positions including at OpenAI, Google Brain, and now at Apple as director of machine learning. This recording happened while Ian w

​Introduction to Tensorflow 2.0 | Tensorflow 2.0 Features and Changes | Edureka

🔗 Introduction to Tensorflow 2.0 | Tensorflow 2.0 Features and Changes | Edureka
***AI and Deep Learning with TensorFlow - https://www.edureka.co/ai-deep-learning-with-tensorflow *** This video will provide you with a short and summarized knowledge of tensorflow 2.0 alpha, what all changes have been made and how is it better from the previous version. 0:55 TensorFlow 2.0 1:50 Shortcomings/Problems 3:35 What Has Changed 10:30 Upgrade Your Code -------------------------------------------------- About the course: Edureka's Deep Learning in TensorFlow with Python Certification Training

🎥 Задание графов исполнения в распределенных системах
👁 1 раз ⏳ 2429 сек.
Существующие фреймворки распределенной обработки данных предоставляют пользователю возможность в различной степени влиять на построение плана исполнения. Ограничения могут возникать как из-за особенностей физической реализации распределенной системы, так и из-за принимаемой модели и вычислительной парадигмы.

На семинаре будут рассмотрены существующие подходы к заданию вычислений, начиная с MapReduce и заканчивая декларативными языками.

Докладчик: Вадим Фарутин.

Ссылка на слайды: https://research.jetbrain

​Advanced Machine Learning Day 3: Neural Architecture Search

🔗 Advanced Machine Learning Day 3: Neural Architecture Search
How do you search over architectures? View presentation slides and more at https://www.microsoft.com/en-us/research/video/advanced-machine-learning-day-3-neural-architecture-search/

​Democratising Machine learning with H2O

🔗 Democratising Machine learning with H2O
It is important to make AI accessible to everyone for the sake of social and economic stability.

​A Gentle Introduction to Channels First and Channels Last Image Formats for Deep Learning

🔗 A Gentle Introduction to Channels First and Channels Last Image Formats for Deep Learning
Color images have height, width, and color channel dimensions. When represented as three-dimensional arrays, the channel dimension for the image data is last by default, but may be moved to be the first dimension, often for performance-tuning reasons. The use of these two “channel ordering formats” and preparing data to meet a specific preferred channel …

​http://arxiv.org/abs/1904.08410

🔗 Neural Painters: A learned differentiable constraint for generating brushstroke paintings
We explore neural painters, a generative model for brushstrokes learned from a real non-differentiable and non-deterministic painting program. We show that when training an agent to "paint" images using brushstrokes, using a differentiable neural painter leads to much faster convergence. We propose a method for encouraging this agent to follow human-like strokes when reconstructing digits. We also explore the use of a neural painter as a differentiable image parameterization. By directly optimizing brushstrokes to activate neurons in a pre-trained convolutional network, we can directly visualize ImageNet categories and generate "ideal" paintings of each class. Finally, we present a new concept called intrinsic style transfer. By minimizing only the content loss from neural style transfer, we allow the artistic medium, in this case, brushstrokes, to naturally dictate the resulting style.

​ChengBinJin/MRI-to-CT-DCNN-TensorFlow

🔗 ChengBinJin/MRI-to-CT-DCNN-TensorFlow
This repository is the implementations of the paper "MR-based Synthetic CT Generation using Deep Convolutional Neural Network Method," Medical Physics 2017. - ChengBinJin/MRI-to-CT-DCNN-T...

​Пишу от команды CatBoost. Мы очень хотим сделать CatBoost лучшим градиентным бустингом в мире. Помогите нам, ответьте на вопросы в небольшом опросе по ссылке, чтобы мы лучше понимали, что важно для пользователей градиентного бустинга: https://forms.yandex.ru/surveys/10011699/?lang=en. Также ссылка на опрос есть у нас на сайте https://catboost.ai

🔗 Gradient Boosting Survey — Yandex.Forms

​https://arxiv.org/abs/1904.01326

🔗 HoloGAN: Unsupervised learning of 3D representations from natural images
We propose a novel generative adversarial network (GAN) for the task of unsupervised learning of 3D representations from natural images. Most generative models rely on 2D kernels to generate images and make few assumptions about the 3D world. These models therefore tend to create blurry images or artefacts in tasks that require a strong 3D understanding, such as novel-view synthesis. HoloGAN instead learns a 3D representation of the world, and to render this representation in a realistic manner. Unlike other GANs, HoloGAN provides explicit control over the pose of generated objects through rigid-body transformations of the learnt 3D features. Our experiments show that using explicit 3D features enables HoloGAN to disentangle 3D pose and identity, which is further decomposed into shape and appearance, while still being able to generate images with similar or higher visual quality than other generative models. HoloGAN can be trained end-to-end from unlabelled 2D images only. Particularly, we do not require pose labels, 3D shapes, or multiple views of the same objects. This shows that HoloGAN is the first generative model that learns 3D representations from natural images in an entirely unsupervised manner.

🎥 Stanford CS230: Deep Learning | Autumn 2018 | Lecture 1 - Class Introduction and Logistics
👁 1 раз ⏳ 4072 сек.
Andrew Ng, Adjunct Professor & Kian Katanforoosh, Lecturer - Stanford University
http://onlinehub.stanford.edu/

Andrew Ng
Adjunct Professor, Computer Science

Kian Katanforoosh
Lecturer, Computer Science

To follow along with the course schedule and syllabus, visit:
http://cs230.stanford.edu/

To get the latest news on Stanford’s upcoming professional programs in Artificial Intelligence, visit: http://learn.stanford.edu/AI.html

To view all online courses and programs offered by Stanford, visit: http:

🎥 Stanford CS230: Deep Learning | Autumn 2018 | Lecture 10
👁 1 раз ⏳ 3292 сек.
Andrew Ng, Adjunct Professor & Kian Katanforoosh, Lecturer - Stanford University
http://onlinehub.stanford.edu/

Andrew Ng
Adjunct Professor, Computer Science

Kian Katanforoosh
Lecturer, Computer Science

To follow along with the course schedule and syllabus, visit:
http://cs230.stanford.edu/

To get the latest news on Stanford’s upcoming professional programs in Artificial Intelligence, visit: http://learn.stanford.edu/AI.html

To view all online courses and programs offered by Stanford, visit: http:

🎥 Stanford CS230: Deep Learning | Autumn 2018 | Lecture 7
👁 1 раз ⏳ 5698 сек.
Andrew Ng, Adjunct Professor & Kian Katanforoosh, Lecturer - Stanford University
http://onlinehub.stanford.edu/

Andrew Ng
Adjunct Professor, Computer Science

Kian Katanforoosh
Lecturer, Computer Science

To follow along with the course schedule and syllabus, visit:
http://cs230.stanford.edu/

To get the latest news on Stanford’s upcoming professional programs in Artificial Intelligence, visit: http://learn.stanford.edu/AI.html

To view all online courses and programs offered by Stanford, visit: http:

🎥 Stanford CS230: Deep Learning | Autumn 2018 | Lecture 6
👁 1 раз ⏳ 3024 сек.
Andrew Ng, Adjunct Professor & Kian Katanforoosh, Lecturer - Stanford University
http://onlinehub.stanford.edu/

Andrew Ng
Adjunct Professor, Computer Science

Kian Katanforoosh
Lecturer, Computer Science

To follow along with the course schedule and syllabus, visit:
http://cs230.stanford.edu/

To get the latest news on Stanford’s upcoming professional programs in Artificial Intelligence, visit: http://learn.stanford.edu/AI.html

To view all online courses and programs offered by Stanford, visit: http:

🎥 Stanford CS230: Deep Learning | Autumn 2018 | Lecture 9
👁 1 раз ⏳ 4820 сек.
Andrew Ng, Adjunct Professor & Kian Katanforoosh, Lecturer - Stanford University
http://onlinehub.stanford.edu/

Andrew Ng
Adjunct Professor, Computer Science

Kian Katanforoosh
Lecturer, Computer Science

To follow along with the course schedule and syllabus, visit:
http://cs230.stanford.edu/

To get the latest news on Stanford’s upcoming professional programs in Artificial Intelligence, visit: http://learn.stanford.edu/AI.html

To view all online courses and programs offered by Stanford, visit: http: