🎥 Deep Learning Interview Questions and Answers | AI & Deep Learning Interview Questions | Edureka
👁 25 раз ⏳ 2442 сек.
*** AI and Deep-Learning with TensorFlow - https://www.edureka.co/ai-deep-learning-with-tensorflow ***
This video covers most of the hottest deep learning interview questions and answers. It also provides you with an understanding process of Deep Learning and the various aspects of it.

#edureka #DeepLearningInterviewQuestions #TensorFlowInterviewQuestions #DeepLearning #TensorFlow
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*** Machine Learning Podcast - https://castbox.fm/channel/id1832236 ***
In

​Deep learning models reveal internal structure and diverse computations in the retina under natural scenes

🔗 Deep learning models reveal internal structure and diverse computations in the retina under natural scenes
The normal function of the retina is to convey information about natural visual images. It is this visual environment that has driven evolution, and that is clinically relevant. Yet nearly all of our understanding of the neural computations, biological function, and circuit mechanisms of the retina comes in the context of artificially structured stimuli such as flashing spots, moving bars and white noise. It is fundamentally unclear how these artificial stimuli are related to circuit processes engaged under natural stimuli. A key barrier is the lack of methods for analyzing retinal responses to natural images. We addressed both these issues by applying convolutional neural network models (CNNs) to capture retinal responses to natural scenes. We find that CNN models predict natural scene responses with high accuracy, achieving performance close to the fundamental limits of predictability set by intrinsic cellular variability. Furthermore, individual internal units of the model are highly correlated with actual retinal interneuron responses that were recorded separately and never presented to the model during training. Finally, we find that models fit only to natural scenes, but not white noise, reproduce a range of phenomena previously described using distinct artificial stimuli, including frequency doubling, latency encoding, motion anticipation, fast contrast adaptation, synchronized responses to motion reversal and object motion sensitivity. Further examination of the model revealed extremely rapid context dependence of retinal feature sensitivity under natural scenes using an analysis not feasible from direct examination of retinal responses. Overall, these results show that nonlinear retinal processes engaged by artificial stimuli are also engaged in and relevant to natural visual processing, and that CNN models form a powerful and unifying tool to study how sensory circuitry produces computations in a natural context.

​План ИИ-трансформации: как управлять компанией в эпоху ИИ?

🔗 План ИИ-трансформации: как управлять компанией в эпоху ИИ?
Делимся с вами ещё одним полезным переводом статьи. Также всех, у кого есть желание за 3 месяца освоить Best Practice по внедрению в проекты современных аналитич...

​Microsoft Introduction to AI — Part 3

🔗 Microsoft Introduction to AI — Part 3
Computer Vision

​How Do Neural Networks Memorize Text?

🔗 How Do Neural Networks Memorize Text?
📝 The paper "Visualizing memorization in RNNs" is available here: https://distill.pub/2019/memorization-in-rnns/ ❤️ Pick up cool perks on our Patreon page: h...

​How I used Python to analyze Game of Thrones

🔗 How I used Python to analyze Game of Thrones
I wanted to learn Python. When I had to do a bunch of boring stuff for work… I got my chance! I will now show you, using Game of Thrones!

🎥 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

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​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