خانه ی #هوشمند مارک #زاکربرگ بنیان گذار فیس بوک که از متدهای نوین هوش مصنوعی نظیر بازشناسی شئ، بازشناسی چهره، بازشناسی گفتار، پردازش زبان‌های طبیعی و ... بهره برده است.
زاکربرگ از انگیزه ی خود برای این کار و گام های انجام کارش می‌نویسد:


https://www.facebook.com/notes/mark-zuckerberg/building-jarvis/10154361492931634/

چالش شخصی من برای سال 2016 ساخت یک هوش مصنوعی ساده برای خانه ام بوده - مثل جارویس در فیلم مرد آهنین...

Building Jarvis:
- Getting Started: Connecting the Home
- #Natural_Language
- #Vision and #Face_Recognition
- Messenger Bot
- Voice and #Speech_Recognition
- Facebook Engineering Environment

—------
Vision and Face Recognition:
About one-third of the human #brain is dedicated to vision, and there are many important #AI problems related to understanding what is happening in images and videos. These problems include #tracking (eg is Max awake and moving around in her crib?), #object_recognition (eg is that Beast or a rug in that room?), and face recognition (eg who is at the door?).
Face recognition is a particularly difficult version of object recognition because most people look relatively similar compared to telling apart two random objects — for example, a sandwich and a house. But Facebook has gotten very good at face recognition for identifying when your friends are in your photos. That expertise is also useful when your friends are at your door and your AI needs to determine whether to let them in.
To do this, I installed a few cameras at my door that can capture images from all angles. AI systems today cannot identify people from the back of their heads, so having a few angles ensures we see the person's face. I built a simple server that continuously watches the cameras and runs a two step process: first, it runs face detection to see if any person has come into view, and second, if it finds a face, then it runs face recognition to identify who the person is. Once it identifies the person, it checks a list to confirm I'm expecting that person, and if I am then it will let them in and tell me they're here.
This type of visual AI system is useful for a number of things, including knowing when Max is awake so it can start playing music or a Mandarin lesson, or solving the context problem of knowing which room in the house we're in so the AI can correctly respond to context-free requests like "turn the lights on" without providing a location. Like most aspects of this AI, vision is most useful when it informs a broader model of the world, connected with other abilities like knowing who your friends are and how to open the door when they're here. The more context the system has, the smarter is gets overall.

#mark_zuckerberg #smart_home

#مقاله منتشر شده توسط گوگل

#MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications
(Submitted on 17 Apr 2017)

We present a class of efficient models called MobileNets for #mobile and embedded #vision applications. MobileNets are based on a streamlined architecture that uses depth-wise separable convolutions to build light weight deep neural networks. We introduce two simple global hyper-parameters that efficiently trade off between #latency and #accuracy. These hyper-parameters allow the model builder to choose the right sized model for their application based on the constraints of the problem. We present extensive experiments on resource and accuracy tradeoffs and show strong performance compared to other popular models on ImageNet classification. We then demonstrate the effectiveness of MobileNets across a wide range of applications and use cases including object detection, finegrain classification, face attributes and large scale geo-localization.

🔗 https://arxiv.org/pdf/1704.04861.pdf

#deep_learning #cnn #convolutional_neutral_network

#MobileNet: Pre_Trained Tensorflow Computer Vision Models.

Developers who want to start using these models should go to the Tensorflow Mobile page:
🔗 https://www.tensorflow.org/mobile/
More information about theTensorflow-Slim image classification library is available on Github:
🔗 https://github.com/tensorflow/models/blob/master/slim/README.md

#TensorFlow #pre_train #model #vision

#خبر

Google’s #TensorFlow object detection API will power your computer vision models
[Published June 17, 2017]
pic: http://bit.ly/2rInFXv

🔗 http://thetechnews.com/2017/06/17/googles-tensorflow-object-detection-api-will-power-your-computer-vision-models/

Code:
🔗 https://github.com/tensorflow/models/tree/master/object_detection

#object_detection #API #VISION

#خبر #آموزش
فیلمهای سمینار زمستانه‌ی شریف 2016 در کانال یوتیوب سمینار آپلود شده است.
فیلم ارائه دکتر علی اسلامی با موضوع
Beyond Supervised Deep Learning
شدیدا توصیه میشود.

Sharif Winter Seminar Series
🎥 https://www.youtube.com/channel/UC5-ct_yxHQJTYJP3TkeEDmQ
⏱ مورخ 8 و 9 دی 1395
مطالب مرتبط:
مباحث مرتبط به یادگیری ژرف در سمینار زمستانه شریف: https://t.me/cvision/44
صوت ضبط شده ی دکتر اسلامی در 8 دی 96 در سمینار زمستانی شریف: https://t.me/cvision/92
اسلایدهای ارائه دکتر اسلامی در 8 دی 95 در سمینار زمستانی شریف: https://t.me/cvision/78
اسلایدهای ارائه دکتر اسلامی در سمینار 11 دی 96 دانشگاه تهران: https://t.me/cvision/80
فیلم ارائه دکتر علی اسلامی در سمینار 11 دی 95 دانشگاه تهران: https://t.me/cvision/103

#deep_learning #Ali_Eslami #Vision #seminar
#سمینار

صحبت های اخیر خانم Fei-Fei Li ‏در TED با ترجمه فارسی.
دکتر Fei-Fei Li سرپرست آزمایشگاه های هوش مصنوعی و بینایی دانشگاه استنفورد است.

فهماندن تصاویر به رایانه ها

https://www.aparat.com/v/dBkqm

توئیت های جالب اخیر پروفسور Fei-Fei Li:
https://alisterta.github.io/2017-12-24/صحبت-های-خانم-Fei-Fei-Li-در-مورد-هوش-مصنوعی-در-سال-2017/

🙏Thanks to: @cyberbully_gng

#deep_learning #vision #fei_fei_li #ted

#آموزش #سورس_کد

Implementation of research papers on Deep Learning+ NLP+ CV in Python using #Keras, #Tensorflow and #Scikit_Learn.

http://deeplearn-ai.com


[1] Correlation Neural Networks. CV, transfer learning, representation learning.

[2] Reasoning With Neural Tensor Networks for Knowledge Base Completion. NLP, ML.

[3] Common Representation Learning Using Step-based Correlation Multi-Modal CNN. CV, transfer learning, representation learning.

[4] ABCNN: Attention-Based Convolutional Neural Network for Modeling Sentence Pairs. NLP, deep learning, sentence matching.

[5] Learning to Rank Short Text Pairs with Convolutional Deep Neural Networks. NLP, deep learning, CQA.

[6] Combining Neural, Statistical and External Features for Fake News Stance Identification. NLP, IR, deep learning.

[7] WIKIQA: A Challenge Dataset for Open-Domain Question Answering. NLP, deep learning, CQA.

[8] Siamese Recurrent Architectures for Learning Sentence Similarity. NLP, sentence similarity, deep learning.

[9] Convolutional Neural Tensor Network Architecture for Community Question Answering. NLP, deep learning, CQA.

[10] Map-Reduce for Machine Learning on Multicore. map-reduce, hadoop, ML.

[11] Teaching Machines to Read and Comprehend. NLP, deep learning.

[12] Improved Representation Learning for Question Answer Matching. NLP, deep learning, CQA.

[13] External features for community question answering. NLP, deep learning, CQA.

[14] Language Identification and Disambiguation in Indian Mixed-Script. NLP, IR, ML.

[15] Construction of a Semi-Automated model for FAQ Retrieval via Short Message Service. NLP, IR, ML.


🔗https://github.com/GauravBh1010tt/DeepLearn

#deep_learning #nlp #vision