#آموزش
Getting started with Deep Learning using Keras and TensorFlow in R
🔗 https://www.analyticsvidhya.com/blog/2017/06/getting-started-with-deep-learning-using-keras-in-r/
✔️Installation of Keras with tensorflow at the backend.
✔️Different types models that can be built in R using Keras
✔️Classifying MNIST handwritten digits using an MLP in R
✔️Comparing MNIST result with equivalent code in Python
#deep_learning #keras #TensorFlow #R
Getting started with Deep Learning using Keras and TensorFlow in R
🔗 https://www.analyticsvidhya.com/blog/2017/06/getting-started-with-deep-learning-using-keras-in-r/
✔️Installation of Keras with tensorflow at the backend.
✔️Different types models that can be built in R using Keras
✔️Classifying MNIST handwritten digits using an MLP in R
✔️Comparing MNIST result with equivalent code in Python
#deep_learning #keras #TensorFlow #R
Analytics Vidhya
Getting started with Deep Learning using Keras and TensorFlow in R
Introduction It has always been a debatable topic to choose between R and Python. The Machine Learning world has been divided over the preference of one language over the other. But with the explosion of Deep Learning, the balance shifted towards Python as…
#آموزش
How I Built a Reverse Image Search with Machine Learning and TensorFlow
[Published Jun 07, 2017]
🔗Part 1:
https://www.codementor.io/jimmfleming/how-i-built-a-reverse-image-search-with-machine-learning-and-tensorflow-part-1-8dje8gjm9
🔗Part 2:
https://www.codementor.io/jimmfleming/how-i-built-a-reverse-image-search-with-machine-learning-and-tensorflow-part-2-8lri6k4x5
#Image_Search #TensorFlow #machine_learning
How I Built a Reverse Image Search with Machine Learning and TensorFlow
[Published Jun 07, 2017]
🔗Part 1:
https://www.codementor.io/jimmfleming/how-i-built-a-reverse-image-search-with-machine-learning-and-tensorflow-part-1-8dje8gjm9
🔗Part 2:
https://www.codementor.io/jimmfleming/how-i-built-a-reverse-image-search-with-machine-learning-and-tensorflow-part-2-8lri6k4x5
#Image_Search #TensorFlow #machine_learning
www.codementor.io
How I Built a Reverse Image Search with Machine Learning and TensorFlow: Part 1 | Codementor
Read how I built a machine learning app, and how to make your own reverse image search. For this demo, the work is ⅓ data munging/setup, ⅓ model development and ⅓ app development.
#آموزش
Deep Learning With TensorFlow, Nvidia and Apache Mesos (DC/OS) (Part 1)
[Published Jun 06, 2017]
✏️Tutorial #1: Run a #TensorFlow #Docker image on your laptop and run a #Machine_Learning model with and without GPUs.
✏️Tutorial #2: Run a Tensoflow Docker image on a DC/OS #cluster with and without GPUs. See #GPU isolation and #Jupyter in action.
✏️Tutorial #3: Deploy a dynamic, #distributed TensorFlow on DC/OS from the Universe. See how TensorFlow on DC/OS dynamically consumes and releases resources on the cluster when done. Run multiple TensorFlows on the same cluster with different resource requirements.
🔗 https://dzone.com/articles/deep-learning-with-tensorflow-nvidia-and-apache-me
#deep_learning #TensorFlow #Nvidia #Apache_Mesos
Deep Learning With TensorFlow, Nvidia and Apache Mesos (DC/OS) (Part 1)
[Published Jun 06, 2017]
✏️Tutorial #1: Run a #TensorFlow #Docker image on your laptop and run a #Machine_Learning model with and without GPUs.
✏️Tutorial #2: Run a Tensoflow Docker image on a DC/OS #cluster with and without GPUs. See #GPU isolation and #Jupyter in action.
✏️Tutorial #3: Deploy a dynamic, #distributed TensorFlow on DC/OS from the Universe. See how TensorFlow on DC/OS dynamically consumes and releases resources on the cluster when done. Run multiple TensorFlows on the same cluster with different resource requirements.
🔗 https://dzone.com/articles/deep-learning-with-tensorflow-nvidia-and-apache-me
#deep_learning #TensorFlow #Nvidia #Apache_Mesos
dzone.com
Deep Learning With TensorFlow, Nvidia and Apache Mesos (DC/OS) (Part 1) - DZone Big Data
This is the first in a multipart tutorial to demonstrate the value of GPUs and how running them on DC/OS makes your apps efficient, reliable, and scalable.
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استفاده از پهباد برای رساندن توپ به داور در فینال جام حذفی پرتغال!
#مقاله 2013
Network In Network
https://arxiv.org/pdf/1312.4400.pdf
#Mlpconv_layer #deep_learning #CNN #convolutional_neutral_network
Network In Network
https://arxiv.org/pdf/1312.4400.pdf
#Mlpconv_layer #deep_learning #CNN #convolutional_neutral_network
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Deep Learning: Transfer Learning in 10 lines of MATLAB Code
https://blogs.mathworks.com/pick/2017/02/24/deep-learning-transfer-learning-in-10-lines-of-matlab-code/
#deep_learning #matlab
https://blogs.mathworks.com/pick/2017/02/24/deep-learning-transfer-learning-in-10-lines-of-matlab-code/
#deep_learning #matlab
Forwarded from کارگروه کلانداده - دانشگاه صنعتی شریف
📝 مروری بر مسابقه بزرگ هوش مصنوعی پیشبینی انتخابات ریاست جمهوری
⏱چهارشنبه 24 خردادماه (ساعت 16-18)
👤مهندس مجید زرینکلاه
🏛کلاس101دانشکده کامپیوتر
ثبتنام: goo.gl/qJdvns
@BigDataWorkGroup
⏱چهارشنبه 24 خردادماه (ساعت 16-18)
👤مهندس مجید زرینکلاه
🏛کلاس101دانشکده کامپیوتر
ثبتنام: goo.gl/qJdvns
@BigDataWorkGroup
#آموزش
Short history of the #Inception deep learning architecture
🔗 http://nicolovaligi.com/history-inception-deep-learning-architecture.html
Inception in #TensorFlow
🔗 https://github.com/tensorflow/models/tree/master/inception
#inception #cnn #convolutional_neutral_network #deep_learning
Short history of the #Inception deep learning architecture
🔗 http://nicolovaligi.com/history-inception-deep-learning-architecture.html
Inception in #TensorFlow
🔗 https://github.com/tensorflow/models/tree/master/inception
#inception #cnn #convolutional_neutral_network #deep_learning
Nicolovaligi
Short history of the Inception deep learning architecture -
Nicolò Valigi
Nicolò Valigi
Posts and writings by Nicolò Valigi
Tensorflow(@CVision)
#آموزش Short history of the #Inception deep learning architecture 🔗 http://nicolovaligi.com/history-inception-deep-learning-architecture.html Inception in #TensorFlow 🔗 https://github.com/tensorflow/models/tree/master/inception #inception #cnn #convol…
Network In Network, Lin et al. (2014)
https://arxiv.org/pdf/1312.4400v3.pdf
Going deeper with convolutions, Szegedy et al. (2014)
https://arxiv.org/pdf/1409.4842v1.pdf
Rethinking the inception architecture for computer vision, Szegedy et al. (2015)
https://arxiv.org/pdf/1512.00567v3.pdf
v4: Inception-v4, Inception-ResNet and the Impact of Residual Connections on Learning, Szegedy et al. (2016)
https://arxiv.org/pdf/1602.07261v2.pdf
Conclusion and pretrained models
https://arxiv.org/pdf/1312.4400v3.pdf
Going deeper with convolutions, Szegedy et al. (2014)
https://arxiv.org/pdf/1409.4842v1.pdf
Rethinking the inception architecture for computer vision, Szegedy et al. (2015)
https://arxiv.org/pdf/1512.00567v3.pdf
v4: Inception-v4, Inception-ResNet and the Impact of Residual Connections on Learning, Szegedy et al. (2016)
https://arxiv.org/pdf/1602.07261v2.pdf
Conclusion and pretrained models
#مقاله #سورس #کد
IMDB-WIKI – 500k+ face images with age and gender labels
pic: http://bit.ly/2rZNDJx
دیتاست سن و جنسیت افراد؛ جمع آوری شده از صفحات ویکی پدیا
+
مقاله و مدل در Caffe , و وزنهای آموزش دیده شده شبکه
https://data.vision.ee.ethz.ch/cvl/rrothe/imdb-wiki/
#Gender_prediction #age #Deep_Learning #dataset #model
#CNN #convolutional_neutral_network
IMDB-WIKI – 500k+ face images with age and gender labels
pic: http://bit.ly/2rZNDJx
دیتاست سن و جنسیت افراد؛ جمع آوری شده از صفحات ویکی پدیا
+
مقاله و مدل در Caffe , و وزنهای آموزش دیده شده شبکه
https://data.vision.ee.ethz.ch/cvl/rrothe/imdb-wiki/
#Gender_prediction #age #Deep_Learning #dataset #model
#CNN #convolutional_neutral_network
#مقاله #سورس_کد
Joint Face Detection and Alignment using Multi-task Cascaded Convolutional Networks
pic: http://bit.ly/2srFGOS
(Submitted on 11 Apr 2016)
#MTCNN
در الگوریتمهای قدیمی تشخیص چهره, موقعیت سر, حالات چهره، نور و ... تاثیر گذار بود و الگوریتم های موجود را با مشکل مواجه میکرد.
الگوریتمهایی نظیر Viola and Jones - Haar-cascade که در opencv پیاده سازی شده برای چهر های رو به رو و نزدیک رو به رو خوب کار میکند اما برای چهره هادر موقعیت ای دیگر کارایی ندارد!
تیم تحقیقاتی ماکروسافت با ارائه مقاله ای در سال 2014 با عنوان
Joint Cascade Face Detection and Alignment
با جنگل تصادفی این مشکل را تا حد زیادی برطرف کرد و سرعت هم تا حدود 30 میلی ثانیه برای تصاویر VGA بهبود داد، اما انتخاب ویژگی های دستی پرفورمنس را محدود کرده بود.
اما در این مقاله با کمک یادگیری عمیق دقت تا حد خوبی افزایش یافته و همانن مقاله ماکروسافت علاوه بر تشخیص کار face alignment هم انجام میشود.
در این روش 3 شبکه عصبی کانولوشنالی پشت سر هم و به صورت آبشاری قرار گرفته است
شبکه اول: بسیار سبک برای پیدا کردن تمامی نواحی مشکوک به وجود چهره.
شبکه دوم : این شبکه از از خروجی شبکه اول ورودی میگیرد؛ نواحی اشتباه پیدا شده را حذف میکند.
شبکه سوم: کاری شبیه یه شبکه دوم بر روی نواحی باقی مانده, همچنین پیدا کردن landmark های چهره نظیر چشم ها و بینی و alignment نقاط.
🕐Runtime efficiency:
It takes 16fps on a 2.60GHz CPU and 99fps on GPU (Nvidia Titan Black)
🔗abstract:
https://arxiv.org/abs/1604.02878
🔗Paper:
https://arxiv.org/pdf/1604.02878
🔗Project Page:
https://kpzhang93.github.io/MTCNN_face_detection_alignment/
🔗codes:
⚙️#Matlab / #Caffe implementation :
http://bit.ly/2rvkr9Q
⚙️#Python / #Tensorflow implementation :
http://bit.ly/2t1fB6e
#face #face_detection #face_alignment #MTCNN
#deep_learning #cnn #convolutional_neutral_network
Joint Face Detection and Alignment using Multi-task Cascaded Convolutional Networks
pic: http://bit.ly/2srFGOS
(Submitted on 11 Apr 2016)
#MTCNN
در الگوریتمهای قدیمی تشخیص چهره, موقعیت سر, حالات چهره، نور و ... تاثیر گذار بود و الگوریتم های موجود را با مشکل مواجه میکرد.
الگوریتمهایی نظیر Viola and Jones - Haar-cascade که در opencv پیاده سازی شده برای چهر های رو به رو و نزدیک رو به رو خوب کار میکند اما برای چهره هادر موقعیت ای دیگر کارایی ندارد!
تیم تحقیقاتی ماکروسافت با ارائه مقاله ای در سال 2014 با عنوان
Joint Cascade Face Detection and Alignment
با جنگل تصادفی این مشکل را تا حد زیادی برطرف کرد و سرعت هم تا حدود 30 میلی ثانیه برای تصاویر VGA بهبود داد، اما انتخاب ویژگی های دستی پرفورمنس را محدود کرده بود.
اما در این مقاله با کمک یادگیری عمیق دقت تا حد خوبی افزایش یافته و همانن مقاله ماکروسافت علاوه بر تشخیص کار face alignment هم انجام میشود.
در این روش 3 شبکه عصبی کانولوشنالی پشت سر هم و به صورت آبشاری قرار گرفته است
شبکه اول: بسیار سبک برای پیدا کردن تمامی نواحی مشکوک به وجود چهره.
شبکه دوم : این شبکه از از خروجی شبکه اول ورودی میگیرد؛ نواحی اشتباه پیدا شده را حذف میکند.
شبکه سوم: کاری شبیه یه شبکه دوم بر روی نواحی باقی مانده, همچنین پیدا کردن landmark های چهره نظیر چشم ها و بینی و alignment نقاط.
🕐Runtime efficiency:
It takes 16fps on a 2.60GHz CPU and 99fps on GPU (Nvidia Titan Black)
🔗abstract:
https://arxiv.org/abs/1604.02878
🔗Paper:
https://arxiv.org/pdf/1604.02878
🔗Project Page:
https://kpzhang93.github.io/MTCNN_face_detection_alignment/
🔗codes:
⚙️#Matlab / #Caffe implementation :
http://bit.ly/2rvkr9Q
⚙️#Python / #Tensorflow implementation :
http://bit.ly/2t1fB6e
#face #face_detection #face_alignment #MTCNN
#deep_learning #cnn #convolutional_neutral_network
#خبر
For the first time ever, #NumPy—a core project for the #Python scientific computing stack—has received #grant funding. The proposal, “Improving NumPy for Better Data Science” will receive $645,020 from the Moore Foundation over 2 years, with the funding going to UC Berkeley Institute for Data Science.
https://www.numfocus.org/blog/numpy-receives-first-ever-funding-thanks-to-moore-foundation/
🙏Thanks to: @cyberbully_gng
For the first time ever, #NumPy—a core project for the #Python scientific computing stack—has received #grant funding. The proposal, “Improving NumPy for Better Data Science” will receive $645,020 from the Moore Foundation over 2 years, with the funding going to UC Berkeley Institute for Data Science.
https://www.numfocus.org/blog/numpy-receives-first-ever-funding-thanks-to-moore-foundation/
🙏Thanks to: @cyberbully_gng
NumFOCUS
NumPy receives first ever funding, thanks to Moore Foundation - NumFOCUS
For the first time ever, NumPy—a core project for the Python scientific computing stack—has received grant funding. The proposal, “Improving NumPy for Better Data Science” will receive $645,020 from the Moore Foundation over 2 years, with the funding going…
Deep Learning without Backpropagation
[Tutorial: DeepMind's Synthetic Gradients]
https://iamtrask.github.io/2017/03/21/synthetic-gradients/?utm_content=buffer6d5d4&utm_medium=social&utm_source=twitter.com&utm_campaign=buffer
✔️Part 1: #Synthetic Gradients Overview
✔️Part 2: Using Synthetic Gradients
✔️Part 3: Generating Synthetic Gradients
✔️Part 4: A Baseline Neural Network
✔️?
✔️Part 6: Synthetic Gradients Based on Layer Output
[Tutorial: DeepMind's Synthetic Gradients]
https://iamtrask.github.io/2017/03/21/synthetic-gradients/?utm_content=buffer6d5d4&utm_medium=social&utm_source=twitter.com&utm_campaign=buffer
✔️Part 1: #Synthetic Gradients Overview
✔️Part 2: Using Synthetic Gradients
✔️Part 3: Generating Synthetic Gradients
✔️Part 4: A Baseline Neural Network
✔️?
✔️Part 6: Synthetic Gradients Based on Layer Output
iamtrask.github.io
Deep Learning without Backpropagation - i am trask
A machine learning craftsmanship blog.
#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
#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
#خبر
#Google open-sources mobile-first computer vision models for TensorFlow
pic: http://bit.ly/2sbzqYu
[Published June 14, 2017]
گوگل mobileNet منتشر شده در مقاله دو ماه پیش را به صورت متن باز برای #تنسورفلو منتشر کرد.
شبکه های از پیش آموزش داده شده مناسب برای بازشناسی اشیاء در موبایل.
🔗 https://research.googleblog.com/2017/06/mobilenets-open-source-models-for.html
🔗 https://venturebeat.com/2017/06/14/google-open-sources-mobile-first-computer-vision-models-for-tensorflow/
✒️مرتبط با:
✔️تنسورفلو برای اندروید:
https://t.me/cvision/208
✔️مقاله mobileNet
https://t.me/cvision/254
#TensorFlow #TensorFlow #TensorFlow_Lite #convolutional_neutral_network
#pre_train #computer_vision
#Google open-sources mobile-first computer vision models for TensorFlow
pic: http://bit.ly/2sbzqYu
[Published June 14, 2017]
گوگل mobileNet منتشر شده در مقاله دو ماه پیش را به صورت متن باز برای #تنسورفلو منتشر کرد.
شبکه های از پیش آموزش داده شده مناسب برای بازشناسی اشیاء در موبایل.
🔗 https://research.googleblog.com/2017/06/mobilenets-open-source-models-for.html
🔗 https://venturebeat.com/2017/06/14/google-open-sources-mobile-first-computer-vision-models-for-tensorflow/
✒️مرتبط با:
✔️تنسورفلو برای اندروید:
https://t.me/cvision/208
✔️مقاله mobileNet
https://t.me/cvision/254
#TensorFlow #TensorFlow #TensorFlow_Lite #convolutional_neutral_network
#pre_train #computer_vision
#آموزش در سایت ماکروسافت
Learning Image to Image Translation with CycleGANs
[Published June 12, 2017]
https://www.microsoft.com/reallifecode/2017/06/12/learning-image-image-translation-cyclegans/
مرتبط با https://t.me/cvision/214
#CycleGAN #GAN #Generative #CNN #Convolutional #deep_learning #adversarial #Generative_Models #Generative
Learning Image to Image Translation with CycleGANs
[Published June 12, 2017]
https://www.microsoft.com/reallifecode/2017/06/12/learning-image-image-translation-cyclegans/
مرتبط با https://t.me/cvision/214
#CycleGAN #GAN #Generative #CNN #Convolutional #deep_learning #adversarial #Generative_Models #Generative
Real Life Code
Learning Image to Image Translation with CycleGANs - Real Life Code
Microsoft has partnered with Getty Images to explore how Neural Nets could be used to transform the stock photo industry.
Tensorflow(@CVision)
#MobileNet Accuracy https://t.me/cvision/255
#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
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
GitHub
tensorflow/models
models - Models built with TensorFlow