Practical Text Classification With Python and Keras
https://realpython.com/python-keras-text-classification/
#keras #nlp
https://realpython.com/python-keras-text-classification/
#keras #nlp
Realpython
Practical Text Classification With Python and Keras – Real Python
Learn about Python text classification with Keras. Work your way from a bag-of-words model with logistic regression to more advanced methods leading to convolutional neural networks. See why word embeddings are useful and how you can use pretrained word embeddings.…
#آموزش #ویدیو
TensorFlow session recordings from O’Reilly AI Conference San Francisco 2018
https://medium.com/tensorflow/tensorflow-session-recordings-from-oreilly-ai-conference-san-francisco-2018-a8910277b449
#tensorflow
TensorFlow session recordings from O’Reilly AI Conference San Francisco 2018
https://medium.com/tensorflow/tensorflow-session-recordings-from-oreilly-ai-conference-san-francisco-2018-a8910277b449
#tensorflow
Medium
TensorFlow session recordings from O’Reilly AI Conference San Francisco 2018
By Marcus Chang, Developer Relations Program Manager
نقاشی کشیده شده با هوش مصنوعی، در یک حراجی 432 هزار دلار فروخته شد!
Portrait by AI program sells for $432,000
https://www.bbc.com/news/technology-45980863
#deep_learning #Gan
Portrait by AI program sells for $432,000
https://www.bbc.com/news/technology-45980863
#deep_learning #Gan
Bbc
Portrait by AI program sells for $432,000
The AI-generated portrait of a fictional Frenchman sold for 45 times its original estimate.
#خبر
Facebook is open sourcing QNNPACK, a high-performance kernel library that is optimized for mobile AI. The library speeds up many operations, such as depth-wise convolutions, that advanced neural network architectures use. QNNPACK has been integrated into Facebook apps and deployed to billions of devices. On benchmarks such as quantized MobileNetV2, QNNPACK outperforms SOTA implementations by approximately 2x on a variety of phones.
https://code.fb.com/ml-applications/qnnpack/
🙏Thanks to: @Alidiba
Facebook is open sourcing QNNPACK, a high-performance kernel library that is optimized for mobile AI. The library speeds up many operations, such as depth-wise convolutions, that advanced neural network architectures use. QNNPACK has been integrated into Facebook apps and deployed to billions of devices. On benchmarks such as quantized MobileNetV2, QNNPACK outperforms SOTA implementations by approximately 2x on a variety of phones.
https://code.fb.com/ml-applications/qnnpack/
🙏Thanks to: @Alidiba
Facebook Engineering
QNNPACK: Open source library for optimized mobile deep learning
Facebook open-sources QNNPACK, a high-performance kernel library optimized for mobile AI. QNNPACK speeds up many advanced neural network operations.
#آموزش
برای طبقه بندی باینری یه کلاسه چه مواردی را برای مثال منفی انتخاب کنیم؟
برای مثال برای گربه/غیر گربه چه تصاویری را در دیتاست غیر گربه بگذاریم بهتر است؟
One Class Classifying — What kind of data set I should have?
https://medium.com/@lankinen/one-class-classifying-what-kind-of-data-set-i-should-have-1486358e491b
برای طبقه بندی باینری یه کلاسه چه مواردی را برای مثال منفی انتخاب کنیم؟
برای مثال برای گربه/غیر گربه چه تصاویری را در دیتاست غیر گربه بگذاریم بهتر است؟
One Class Classifying — What kind of data set I should have?
https://medium.com/@lankinen/one-class-classifying-what-kind-of-data-set-i-should-have-1486358e491b
Medium
One Class Classifying — What kind of data set I should have?
For all these models I used exact same hyper parameters and only difference was data set I used to train.
state-of-the-art AutoAugment Modules for Image augmentation
#TensorFlowHub #GoogleAI #TFHub
https://tfhub.dev/s?keywords=image_augmentation
#TensorFlowHub #GoogleAI #TFHub
https://tfhub.dev/s?keywords=image_augmentation
Official BERT #TensorFlow code + pre-trained models released by Google AI Language
BERT is method of pre-training language representations, meaning that we train a general-purpose "language understanding" model on a large text corpus (like Wikipedia), and then use that model for downstream #NLP tasks that we care about (like question answering). #BERT outperforms previous methods because it is the first unsupervised, deeply #bidirectional system for pre-training NLP.
https://github.com/google-research/bert/blob/master/README.md
🙏Thanks to: @cyberbully_gng
BERT is method of pre-training language representations, meaning that we train a general-purpose "language understanding" model on a large text corpus (like Wikipedia), and then use that model for downstream #NLP tasks that we care about (like question answering). #BERT outperforms previous methods because it is the first unsupervised, deeply #bidirectional system for pre-training NLP.
https://github.com/google-research/bert/blob/master/README.md
🙏Thanks to: @cyberbully_gng
GitHub
bert/README.md at master · google-research/bert
TensorFlow code and pre-trained models for BERT. Contribute to google-research/bert development by creating an account on GitHub.
#کنفرانس
فیلم های کنفرانس هوش مصنوعی H2O لندن - برگزار شده در 29 و 30 اکتبر (در حال به روز رسانی)
https://bit.ly/2yOUpor
فیلم های کنفرانس هوش مصنوعی H2O نیویورک - برگزار شده در 7 ژوئن
https://bit.ly/2qplJ8n
فیلم های کنفرانس هوش مصنوعی H2O لندن - برگزار شده در 29 و 30 اکتبر (در حال به روز رسانی)
https://bit.ly/2yOUpor
فیلم های کنفرانس هوش مصنوعی H2O نیویورک - برگزار شده در 7 ژوئن
https://bit.ly/2qplJ8n
YouTube
Keynote - SriSatish Ambati - H2O AI World London 2018
This video was recorded in London on October 30th, 2018. SriSatish Ambati is the CEO and Co-Founder of H2O.ai – the maker behind H2O, the leading open source...
#آموزش
Serving ML Quickly with TensorFlow Serving and Docker
https://medium.com/tensorflow/serving-ml-quickly-with-tensorflow-serving-and-docker-7df7094aa008?linkId=59087318
#tensorflow #docker #serving
Serving ML Quickly with TensorFlow Serving and Docker
https://medium.com/tensorflow/serving-ml-quickly-with-tensorflow-serving-and-docker-7df7094aa008?linkId=59087318
#tensorflow #docker #serving
Medium
Serving ML Quickly with TensorFlow Serving and Docker
Posted by Gautam Vasudevan, Technical Program Manager, and Abhijit Karmarkar, Software Engineer, Google Brain team
یک نوت نسبتا کامل از 5 تا کورس deep learning specialization اندرو انگ:
https://github.com/mbadry1/DeepLearning.ai-Summary
https://github.com/mbadry1/DeepLearning.ai-Summary
GitHub
GitHub - mbadry1/DeepLearning.ai-Summary: This repository contains my personal notes and summaries on DeepLearning.ai specialization…
This repository contains my personal notes and summaries on DeepLearning.ai specialization courses. I've enjoyed every little bit of the course hope you enjoy my notes too. - mbadry1/DeepLe...
نکاتی درباره GANs،cGANs و PatchGAN
https://gist.github.com/brannondorsey/fb075aac4d5423a75f57fbf7ccc12124
🙏Thanks to: @cyberbully_gng
#GAN
https://gist.github.com/brannondorsey/fb075aac4d5423a75f57fbf7ccc12124
🙏Thanks to: @cyberbully_gng
#GAN
Gist
Notes on the Pix2Pix (pixel-level image-to-image translation) Arxiv paper
Notes on the Pix2Pix (pixel-level image-to-image translation) Arxiv paper - pix2pix_paper_notes.md
#منبع
book : Deep Learning with JavaScript
by François Chollet , Shanqing Cai, Stanley Bileschi, Eric D. Nielsen https://livebook.manning.com/#!/book/deep-learning-with-javanoscript/welcome/v-1/0
book : Deep Learning with JavaScript
by François Chollet , Shanqing Cai, Stanley Bileschi, Eric D. Nielsen https://livebook.manning.com/#!/book/deep-learning-with-javanoscript/welcome/v-1/0
Tensorflow(@CVision)
#منبع book : Deep Learning with JavaScript by François Chollet , Shanqing Cai, Stanley Bileschi, Eric D. Nielsen https://livebook.manning.com/#!/book/deep-learning-with-javanoscript/welcome/v-1/0
brief contents
Part 1: Motivation and Basic Concepts
1 Deep Learning and JavaScript
Part 2: A Gentle Introduction to TensorFlow.js
2 Getting Started: Simple Linear Regression in TensorFlow.js
3 Adding Nonlinearity: Beyond Weighted Sums
4 Recognizing Images and Sounds Using Convolutional Neural Networks
5 Transfer learning: Using pre-trained models for custom tasks
Part 3: Advanced Deep Learning with TensorFlow.js
6 Preparing data for TensorFlow.js
7 Model visualization and tuning
8 Deep learning for text and sequences
9 Generative deep learning in the browser
10 Reinforcement learning in the browser
Part 4: Summary and Closing Words
11 Quick review
12 Final Words
https://news.1rj.ru/str/cvision/750
Part 1: Motivation and Basic Concepts
1 Deep Learning and JavaScript
Part 2: A Gentle Introduction to TensorFlow.js
2 Getting Started: Simple Linear Regression in TensorFlow.js
3 Adding Nonlinearity: Beyond Weighted Sums
4 Recognizing Images and Sounds Using Convolutional Neural Networks
5 Transfer learning: Using pre-trained models for custom tasks
Part 3: Advanced Deep Learning with TensorFlow.js
6 Preparing data for TensorFlow.js
7 Model visualization and tuning
8 Deep learning for text and sequences
9 Generative deep learning in the browser
10 Reinforcement learning in the browser
Part 4: Summary and Closing Words
11 Quick review
12 Final Words
https://news.1rj.ru/str/cvision/750
Telegram
Tensorflow
#منبع
book : Deep Learning with JavaScript
by François Chollet , Shanqing Cai, Stanley Bileschi, Eric D. Nielsen https://livebook.manning.com/#!/book/deep-learning-with-javanoscript/welcome/v-1/0
book : Deep Learning with JavaScript
by François Chollet , Shanqing Cai, Stanley Bileschi, Eric D. Nielsen https://livebook.manning.com/#!/book/deep-learning-with-javanoscript/welcome/v-1/0
Tensorflow(@CVision)
دوره مقدماتی یادگیری ژرف http://plan.azad.ac.ir/fa/page/9755 سرفصل دوره مقدماتی و پیشرفته: https://news.1rj.ru/str/cvision/737
با سلام
گارگاه "اصول شبکه های عصبی عمیق در بینایی ماشین مقدماتی" در تاریخ های زیر برگزار می گردد. علاقه مندان می توانند از طریق لینک زیر ثبت نام کنند.
جلسه اول: چهارشنبه مورخ 23 آبان ماه ساعت 1 الی 5
جلسه دوم: چهارشنبه مورخ 30 آبان ماه ساعت 1 الی 5
مکان برگزاری، دانشکده فنی و مهندسی تهران جنوب واقع در بلوار اهنگ
پوستر و اطلاعات بیشتر:
https://news.1rj.ru/str/cvision/736
لینک ثبت نام:
http://plan.azad.ac.ir/fa/page/9755
گارگاه "اصول شبکه های عصبی عمیق در بینایی ماشین مقدماتی" در تاریخ های زیر برگزار می گردد. علاقه مندان می توانند از طریق لینک زیر ثبت نام کنند.
جلسه اول: چهارشنبه مورخ 23 آبان ماه ساعت 1 الی 5
جلسه دوم: چهارشنبه مورخ 30 آبان ماه ساعت 1 الی 5
مکان برگزاری، دانشکده فنی و مهندسی تهران جنوب واقع در بلوار اهنگ
پوستر و اطلاعات بیشتر:
https://news.1rj.ru/str/cvision/736
لینک ثبت نام:
http://plan.azad.ac.ir/fa/page/9755
Telegram
Tensorflow
دوره مقدماتی یادگیری ژرف
http://plan.azad.ac.ir/fa/page/9755
سرفصل دوره مقدماتی و پیشرفته:
https://news.1rj.ru/str/cvision/737
http://plan.azad.ac.ir/fa/page/9755
سرفصل دوره مقدماتی و پیشرفته:
https://news.1rj.ru/str/cvision/737
Forwarded from رویدادهای ملی و بین المللی
کانال اطلاع رسانی رویدادهای ملی و بین المللی:
- اخبار و استخدامی ها
- همایش ها و سمینار ها
- کنفراس ها و ژورنال ها
- کارگاه ها و مسابقات
#Convent
https://telegram.me/convent
@convent
- اخبار و استخدامی ها
- همایش ها و سمینار ها
- کنفراس ها و ژورنال ها
- کارگاه ها و مسابقات
#Convent
https://telegram.me/convent
@convent