🎥 A. David Redish: "Cross-species translation through computational analyses - implications for un..."
👁 1 раз ⏳ 3298 сек.
Deep Learning and Medical Applications 2020

"Cross-species translation through computational analyses - implications for understanding and modifying psychiatric treatments"
A. David Redish - University of Minnesota, Twin Cities

Abstract: In the new conceptual framework of computational psychiatry, psychiatric dysfunctions are seen as problems in information processing of environmental situations. This new framework has three implications which I will explore in this talk. (1) Diagnosis should align to inf

🎥 3. Class 3 : Building First Deep Neural Network #DeepLearning2020
👁 1 раз ⏳ 1770 сек.
Hello Everyone,
This is class 3 of Deeplearning 2020 - Free hands-on course on Deep Learning.

For questions/queries : please post in comments section

Reach the instructor at : instructor@manifoldailearning.in
Corporate Enquiries : support@manifoldailearning.in

Special Bootcamp on Data Science DeepLearning reach us : support@manifoldailearning.in

🎥 Machine Learning Interview Questions | Machine Learning Interview Preparation | Intellipaat
👁 1 раз ⏳ 2729 сек.
This machine learning interview questions and answers video is an exclusive machine learning interview preparation tutorial where you will learn everything about machine learning latest interview questions with detailed answer asked in top MNCs recently. If you are preparing for machine learning job then this is a must watch video for you. We have covered machine learning basic questions to advance questions so that this video caters to everyone at any stage of learning machine learning.
🔥🔥Intellipaat Mac

​ML Code Completeness Checklist

https://medium.com/paperswithcode/ml-code-completeness-checklist-e9127b168501

Tips for Publishing Research Code: https://github.com/paperswithcode/releasing-research-code

Facebook blog: https://ai.facebook.com/blog/new-code-completeness-checklist-and-reproducibility-updates/
Наш телеграм канал - tglink.me/ai_machinelearning_big_data

🔗 ML Code Completeness Checklist
Collated best practices from most popular ML research repositories — used for code submissions at NeurIPS 2020.

​New code completeness checklist and reproducibility updates

🔗 New code completeness checklist and reproducibility updates
Today, @paperswithcode introduced its new ML Code Completeness Checklist, building on the AI reproducibility work from FAIR Managing Director Joelle Pineau

​How Airbus Detects Anomalies in ISS Telemetry Data Using TFX

🔗 How Airbus Detects Anomalies in ISS Telemetry Data Using TFX

​Попытка использовать ИИ в детекторе лжи лишь усугубляет проблему распознавания обмана

🔗 Попытка использовать ИИ в детекторе лжи лишь усугубляет проблему распознавания обмана
Подробнейшее исследование попыток использования искусственного интеллекта в распознавании лжи До того, как полиграф вынес ему вердикт «виновен», Эммануэль Мерв...

​RLlib is an open-source library for reinforcement learning that offers both high scalability and a unified API for a variety of applications.
RLlib natively supports TensorFlow, TensorFlow Eager, and PyTorch, but most of its internals are framework agnostic.
The Ray Team : https://ray.readthedocs.io/en/latest/rllib.html

🔗 RLlib: Scalable Reinforcement Learning — Ray 0.9.0.dev0 documentation

​Как мы считаем людей с помощью компьютерного зрения

🔗 Как мы считаем людей с помощью компьютерного зрения
Фото из открытых источников Массовые скопления людей создают проблемы в самых разных областях (ритейл, госслужбы, банки, застройщики). Заказчикам необходимо об...

​uDepth: Real-time 3D Depth Sensing on the Pixel 4">
uDepth: Real-time 3D Depth Sensing on the Pixel 4

🔗 uDepth: Real-time 3D Depth Sensing on the Pixel 4
Posted by Michael Schoenberg, uDepth Software Lead and Adarsh Kowdle, uDepth Hardware/Systems Lead, Google Research The ability to deter...

🎥 Knowledge - Lecture 1 - CS50's Introduction to Artificial Intelligence with Python
👁 1 раз ⏳ 6464 сек.
00:00:00 - Introduction
00:00:15 - Knowledge
00:04:52 - Propositional Logic
00:21:47 - Inference
00:40:06 - Knowledge Engineering
01:04:33 - Inference Rules
01:30:31 - Resolution
01:38:25 - First-Order Logic

🎥 Neural Networks - Lecture 5 - CS50's Introduction to Artificial Intelligence with Python
👁 1 раз ⏳ 6080 сек.
00:00:00 - Introduction
00:00:15 - Neural Networks
00:05:41 - Activation Functions
00:07:47 - Neural Network Structure
00:16:02 - Gradient Descent
00:30:00 - Multilayer Neural Networks
00:32:58 - Backpropagation
00:36:27 - Overfitting
00:38:52 - TensorFlow
00:53:01 - Computer Vision
00:58:09 - Image Convolution
01:08:18 - Convolutional Neural Networks
01:27:03 - Recurrent Neural Networks

​How we launched a data product in 60 days with AWS

🔗 How we launched a data product in 60 days with AWS
Team of two planned and shipped a beta for 200 users in less than 2 months without quitting their full-time jobs.

​Calculate The Economic Impact Of Your Click-Through Prediction

🔗 Calculate The Economic Impact Of Your Click-Through Prediction
Beyond Accuracy, AUC or Lift: a model for measuring the Economic Impact

🎥 Applying Graph Machine Learning to Enterprise Business Problems
👁 1 раз ⏳ 2005 сек.
This event is part of a series of talk organized by Machine Learning Milan and was recorder during the following event: https://www.meetup.com/it-IT/Machine-Learning-Milan/events/269775350/

Fujitsu’s research labs have worked on graph-based Machine Learning from both a theoretical and an applications perspective. In this talk, we’ll describe Fujitsu’s Deep Tensor technology for graph ML and highlight our experiences with applying graph-ML to business problems.

Jeff Fischer and Wing Au, Fujitsu
Jeff is an

🎥 Типовые задачи линейной алгебры. Консультация для студентов 1 курса. Лунина М.А.
👁 1 раз ⏳ 8306 сек.
00:00:41 Ранг матрицы. Системы линейных уравнений
00:16:13 Линейные пространства
00:36:12 Линейные отображения
01:06:19 Структура линейных преобразований
01:18:20 Евклидовы пространства
01:37:04 Линейные преобразования в евклидовых пространствах
01:50:52 Квадратичные функции в линейных пространствах
02:03:20 Квадратичные функции в евклидовых пространствах

​"Автомобиль на карантине". Что делать, если машина долго простояла во дворе?

🔗 "Автомобиль на карантине". Что делать, если машина долго простояла во дворе?
Добрый день! Продолжаем обсуждать актуальные темы и делиться друг с другом своими мнениями и лайфхаками. Подписывайтесь на канал и друзей зовите 😉 На фоне всеобщей самоизоляции мне в голову пришёл вопрос- человек без движения чахнет, а что же будет с нашими ласточками, без дела простаивающими во дворе? Конечно, это бесспорно отражается на здоровье железного коня. Причём машина за время простоя

​Forecasting Wars: Classical Forecasting Methods vs Machine Learning

🔗 Forecasting Wars: Classical Forecasting Methods vs Machine Learning
Who will win?

​Mind hacks based on machine learning

🔗 Mind hacks based on machine learning
Where AI research overlaps with psychology, you can learn interesting mind hacks and productivity tricks.

​Paper:
https://drive.google.com/file/d/17ki_YAL1k5CaHHP3pIBFWvw-ztF4CCPP/view

Github:
https://github.com/vt-vl-lab/3d-photo-inpainting

Project:
https://shihmengli.github.io/3D-Photo-Inpainting/

Notebook:
https://colab.research.google.com/drive/1706ToQrkIZshRSJSHvZ1RuCiM__YX3Bz

🔗 06222.pdf

🎥 A.I. learns to play | Neural Network + Genetic Algorithm
👁 6 раз ⏳ 845 сек.
#neuroevolution #geneticalgorithm
This is how I created an AI that learns to play the game and beats it!

Watch the full evolution and relax -
https://youtu.be/o4ioK7vwmYU

For more videos please subscribe -
http://bit.ly/normalizedNERD

Support me if you can ❤️
https://www.paypal.com/paypalme2/suji04
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The coding train's playlist on genetic algorithm
https://www.youtube.com/playlist?list=PLRqwX-V7Uu6bJM3VgzjNV5YxVxUwzALHV

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https://www.facebook.c