Deep Learning and Hyper-Personalization
https://towardsdatascience.com/deep-learning-and-hyper-personalization-e0d2b1fcb86?source=collection_home---4------1---------------------

​Deep Learning: What is it good for? - Prof. Ankit Patel - Rice University

🔗 Deep Learning: What is it good for? - Prof. Ankit Patel - Rice University
"In this talk, we will introduce deep learning and review some of the key advances in the field focusing on current attempts at a theoretical understanding. ...

​Analyzing model improvements - Deep Learning in Halite AI competition p.7

🔗 Analyzing model improvements - Deep Learning in Halite AI competition p.7
Analyzing our neural network model's improvement from random, and then considering our next steps. Channel membership: https://www.youtube.com/channel/UCfzlC...

​A Bag of Tricks for Image Classification

1. Large batch size
2. Mini model-tweaks
3. Refined Training Methods
4. Transfer Learning
5. Fancy Data Augmentation
https://link.medium.com/fzJvIBfsJS

#CV #tipsandtricks

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🎥 Практическое занятие по профилю «Большие данные и машинное обучение» | Олег Ивченко
👁 1 раз ⏳ 6420 сек.

​[1812.06855] Bayesian Optimization in AlphaGo

🔗 [1812.06855] Bayesian Optimization in AlphaGo

​New interesting paper in Super Resolution area https://arxiv.org/abs/1812.04240

🔗 [1812.04240] Unsupervised Degradation Learning for Single Image Super-Resolution

​Коллекция Deep learning курсов от MIT

https://deeplearning.mit.edu/

🔗 MIT Deep Learning
Lectures on deep learning, deep reinforcement learning, artificial intelligence, and autonomous vehicles by Lex Fridman at MIT.

t-SNE: The Bits that No One Learns
https://towardsdatascience.com/t-sne-the-bits-that-no-one-learns-40add9e1978e?source=collection_home---4------2---------------------

https://habr.com/company/toshibarus/blog/433544/

Топ-5 сфер применения систем распознавания объектов

#machinelearning #neuralnets #deeplearning #машинноеобучение

Наш телеграмм канал - https://yangx.top/ai_machinelearning_big_data

​Lecture 7 - Reinforcement Learning | Deep Learning on Computational Accelerators

🔗 Lecture 7 - Reinforcement Learning | Deep Learning on Computational Accelerators
Given by Prof. Alex Bronstein

​Share Your AXA Research | Dr. E Ferrante: Deep Learning and Image Registration | AXA Research Fund

🔗 Share Your AXA Research | Dr. E Ferrante: Deep Learning and Image Registration | AXA Research Fund
What if, instead of explaining to a computer how to solve a problem, you let it learn by itself? Deep learning methods offer tremendous opportunities for mac...

​"New astonishing results in face generation by PGGAN author. All these people never existed. No cherry-picking was done"
🔎 arxiv.org/abs/1812.04948
👤 @j_links
📉 @loss_function_porn

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Automated machine learning: a different notion of deep
https://towardsdatascience.com/automated-machine-learning-a-different-notion-of-deep-e0f7e5c06fb2?source=collection_home---4------2---------------------

​afshinea/stanford-cs-229-machine-learning

🔗 afshinea/stanford-cs-229-machine-learning
VIP cheatsheets for Stanford's CS 229 Machine Learning - afshinea/stanford-cs-229-machine-learning

https://habr.com/company/tinkoff/blog/432056/

Верификация фотографий в ритейле с помощью Computer vision

#machinelearning #neuralnets #deeplearning #машинноеобучение

Наш телеграмм канал - https://yangx.top/ai_machinelearning_big_data

🎥 Практическое занятие по профилю «Большие данные и машинное обучение» | Татьяна Гайнцева
👁 1 раз ⏳ 4636 сек.
Часть 2

​Искусственный интеллект в управлении непрерывным производством - YouTube

🔗 Искусственный интеллект в управлении непрерывным производством - YouTube
Видеозаписи выступлений и дискуссий с совместной конференции Yandex Data Factory и «Газпром нефти» по применению искусственного интеллекта для задач непрерыв...

​Ensemble Methods for Deep Learning Neural Networks to Reduce Variance and Improve Performance

https://machinelearningmastery.com/ensemble-methods-for-deep-learning-neural-networks/

🔗 Ensemble Methods for Deep Learning Neural Networks to Reduce Variance and Improve Performance
Deep learning neural networks are nonlinear methods. They offer increased flexibility and can scale in proportion to the amount of training data available. A downside of this flexibility is that they learn via a stochastic training algorithm which means that they are sensitive to the specifics of the training data and may find a different …

Understanding Support Vector Machine: Part 2: Kernel Trick; Mercer’s Theorem
https://towardsdatascience.com/understanding-support-vector-machine-part-2-kernel-trick-mercers-theorem-e1e6848c6c4d?source=collection_home---4------3---------------------