@Machine_learn
Tensorflow 2.0 release
Faster
TPU support
TensorFlow datasets
Change log: https://medium.com/tensorflow/tensorflow-2-0-is-now-available-57d706c2a9ab
#google #tensorflow #dl #tf
Tensorflow 2.0 release
Faster
TPU support
TensorFlow datasets
Change log: https://medium.com/tensorflow/tensorflow-2-0-is-now-available-57d706c2a9ab
#google #tensorflow #dl #tf
Medium
TensorFlow 2.0 is now available!
Earlier this year, we announced TensorFlow 2.0 in alpha at the TensorFlow Dev Summit. Today, we’re delighted to announce that the final…
📹Artificial caricature
Agents learn to draw simplified (artistic?) portraits via trial and error.
Project website: https://learning-to-paint.github.io
ArXiV: https://arxiv.org/abs/1910.01007
#GAN #CelebA #DL
Agents learn to draw simplified (artistic?) portraits via trial and error.
Project website: https://learning-to-paint.github.io
ArXiV: https://arxiv.org/abs/1910.01007
#GAN #CelebA #DL
arXiv.org
Unsupervised Doodling and Painting with Improved SPIRAL
We investigate using reinforcement learning agents as generative models of images (extending arXiv:1804.01118). A generative agent controls a simulated painting environment, and is trained with...
✅How to Develop a CycleGAN for Image-to-Image Translation with Keras
https://machinelearningmastery.com/cyclegan-tutorial-with-keras/
https://machinelearningmastery.com/cyclegan-tutorial-with-keras/
Title: Rebooting AI : building artificial intelligence we can trust / Gary Marcus and Ernest Davis.
#book #AI
@Machine_learn
#book #AI
@Machine_learn
4_5823350061324568102.pdf
16.6 MB
Title: Rebooting AI : building artificial intelligence we can trust / Gary Marcus and Ernest Davis.
#book #AI
@Machine_learn
#book #AI
@Machine_learn
Forwarded from Machinelearning
BigBiGAN representation learning models
https://arxiv.org/abs/1907.02544
TF Hub: https://tfhub.dev/s?publisher=deepmind&q=bigbigan
Try them out in a Colab at: https://colab.research.google.com/github/tensorflow/hub/blob/master/examples/colab/bigbigan_with_tf_hub.ipynb
https://arxiv.org/abs/1907.02544
TF Hub: https://tfhub.dev/s?publisher=deepmind&q=bigbigan
Try them out in a Colab at: https://colab.research.google.com/github/tensorflow/hub/blob/master/examples/colab/bigbigan_with_tf_hub.ipynb
arXiv.org
Large Scale Adversarial Representation Learning
Adversarially trained generative models (GANs) have recently achieved compelling image synthesis results. But despite early successes in using GANs for unsupervised representation learning, they...
📹Artificial caricature
Agents learn to draw simplified (artistic?) portraits via trial and error.
Project website: https://learning-to-paint.github.io
ArXiV: https://arxiv.org/abs/1910.01007
#GAN #CelebA #DL
Agents learn to draw simplified (artistic?) portraits via trial and error.
Project website: https://learning-to-paint.github.io
ArXiV: https://arxiv.org/abs/1910.01007
#GAN #CelebA #DL
arXiv.org
Unsupervised Doodling and Painting with Improved SPIRAL
We investigate using reinforcement learning agents as generative models of images (extending arXiv:1804.01118). A generative agent controls a simulated painting environment, and is trained with...
Forwarded from بینام
Deep Learning with Keras.pdf
20.1 MB
Forwarded from بینام
deep_learning_notes.pdf
19.1 MB
Notes by Tess Ferrandez on the Andrew Ng’s Deep learning specialization.
Very nice way to refresh you memory of DL concepts.
#DL #note
@Machine_learn
Credit: Tess Ferrandez, https://www.slideshare.net/TessFerrandez/notes-fr
Very nice way to refresh you memory of DL concepts.
#DL #note
@Machine_learn
Credit: Tess Ferrandez, https://www.slideshare.net/TessFerrandez/notes-fr
1: introduction two machine learning
2: Regression (linear and non-linear)
3: Tensorflow introduction
4: Tensorflow computaion graph
5: Tensorflow optimizer and loss function
6: Tensorflow linear and non linear regression
7: logistic regression
8: Tensorflow regression
_______
9: introduction to traditional machine learning
*10: knn and desicion tree
*11: desicion tree and Naive bayes
*12: desicion tree, knn, Naive bayes implementation
*13: k-means
*14: Guassion Mixture Model(GMM)
*15: implementation K-means and GMM
_____________
16: introduction to Artificial Neural Network
17: Multi-level Neural Network
18: Introduction to Convolution Neural Network
19: Tensorflow Multi-level Neural Network
20:Tensorflow CNN
21:CNN image clasaification
22: Cnn text clasaification
23: Recurrent Neural Network(RNN)
24: implementation RNN
25: RNN image Classification
26: RNN text Classification
__________
*optional class
در این دوره که در ۲۶ جلسه برگزار می شود بسیاری از سرفصل های یادگیری ماشین آموزش داده می شوند تمامی الگوریتم ها بغییر از * براساس کتابخانه Tensorflow اموزش داده می شوند. از پیش نیاز های این دوره آشنایی به زبان پایتون می باشد در صورت عدم آشنایی ۵ جلسه به موارد بالا اضافه می شود. این ۵ جلسه حدودا ۱۰ ساعت کد نویسی می باشد. جلسات به صورت آنلاین و از طریق اسکایپ برگزار می شوند. در انتهای جلسات جهت یادگیری بیشتر چند مساله واقعی به همراه کدنویسی و بدون هزینه برگزار می شود. کلاس ها به صورت کاملا خصوصی می باشند.
جهت رزرو وقت کلاس با ایدی تلگرام زیر در ارتباط باشین.
📱 Teleg: @Raminmousa
2: Regression (linear and non-linear)
3: Tensorflow introduction
4: Tensorflow computaion graph
5: Tensorflow optimizer and loss function
6: Tensorflow linear and non linear regression
7: logistic regression
8: Tensorflow regression
_______
9: introduction to traditional machine learning
*10: knn and desicion tree
*11: desicion tree and Naive bayes
*12: desicion tree, knn, Naive bayes implementation
*13: k-means
*14: Guassion Mixture Model(GMM)
*15: implementation K-means and GMM
_____________
16: introduction to Artificial Neural Network
17: Multi-level Neural Network
18: Introduction to Convolution Neural Network
19: Tensorflow Multi-level Neural Network
20:Tensorflow CNN
21:CNN image clasaification
22: Cnn text clasaification
23: Recurrent Neural Network(RNN)
24: implementation RNN
25: RNN image Classification
26: RNN text Classification
__________
*optional class
در این دوره که در ۲۶ جلسه برگزار می شود بسیاری از سرفصل های یادگیری ماشین آموزش داده می شوند تمامی الگوریتم ها بغییر از * براساس کتابخانه Tensorflow اموزش داده می شوند. از پیش نیاز های این دوره آشنایی به زبان پایتون می باشد در صورت عدم آشنایی ۵ جلسه به موارد بالا اضافه می شود. این ۵ جلسه حدودا ۱۰ ساعت کد نویسی می باشد. جلسات به صورت آنلاین و از طریق اسکایپ برگزار می شوند. در انتهای جلسات جهت یادگیری بیشتر چند مساله واقعی به همراه کدنویسی و بدون هزینه برگزار می شود. کلاس ها به صورت کاملا خصوصی می باشند.
جهت رزرو وقت کلاس با ایدی تلگرام زیر در ارتباط باشین.
📱 Teleg: @Raminmousa
Unfolding the Structure of a Document using Deep Learning.
#DL #paper
@Machine_learn
https://arxiv.org/abs/1910.03678
#DL #paper
@Machine_learn
https://arxiv.org/abs/1910.03678
Exploring Massively Multilingual, Massive Neural Machine Translation
http://ai.googleblog.com/2019/10/exploring-massively-multilingual.html
#DL #paper
@Machine_learn
article: https://arxiv.org/pdf/1907.05019.pdf
http://ai.googleblog.com/2019/10/exploring-massively-multilingual.html
#DL #paper
@Machine_learn
article: https://arxiv.org/pdf/1907.05019.pdf
research.google
Exploring Massively Multilingual, Massive Neural Machine Translation
Posted by Ankur Bapna, Software Engineer and Orhan Firat, Research Scientist, Google Research “... perhaps the way [of translation] is to descend...
Forwarded from بینام
Mastering OpenCV 3 Second Edition.pdf
17.7 MB
Forwarded from بینام
OpenCV By Example.pdf
14.6 MB
Machine learning books and papers pinned «1: introduction two machine learning 2: Regression (linear and non-linear) 3: Tensorflow introduction 4: Tensorflow computaion graph 5: Tensorflow optimizer and loss function 6: Tensorflow linear and non linear regression 7: logistic regression 8: Tensorflow…»
Open-Source Library for Real-Time Metric-Semantic Localization and Mapping
video: https://www.youtube.com/watch?v=-5XxXRABXJs&feature=youtu.be
code: https://github.com/MIT-SPARK/Kimera
article: https://arxiv.org/abs/1910.02490
video: https://www.youtube.com/watch?v=-5XxXRABXJs&feature=youtu.be
code: https://github.com/MIT-SPARK/Kimera
article: https://arxiv.org/abs/1910.02490
YouTube
Kimera: an Open-Source Library for Real-Time Metric-Semantic Localization and Mapping
Code available: https://github.com/MIT-SPARK/Kimera
Paper: https://arxiv.org/abs/1910.02490
Kimera has also been used in:
- 3D Dynamic Scene Graphs:
Video: https://www.youtube.com/watch?v=SWbofjhyPzI&feature=youtu.be
Paper: https://arxiv.org/abs/2002.06289…
Paper: https://arxiv.org/abs/1910.02490
Kimera has also been used in:
- 3D Dynamic Scene Graphs:
Video: https://www.youtube.com/watch?v=SWbofjhyPzI&feature=youtu.be
Paper: https://arxiv.org/abs/2002.06289…
CrypTen: A new research tool for secure machine learning with PyTorch
https://ai.facebook.com/blog/crypten-a-new-research-tool-for-secure-machine-learning-with-pytorch
code: https://github.com/facebookresearch/CrypTen
https://ai.facebook.com/blog/crypten-a-new-research-tool-for-secure-machine-learning-with-pytorch
code: https://github.com/facebookresearch/CrypTen
Meta
CrypTen: A new research tool for secure machine learning with PyTorch
Facebook AI is open-sourcing CrypTen, a research-focused framework to explore encrypted machine learning techniques in the PyTorch environment.
ICCV 2019 papers open access
http://openaccess.thecvf.com/ICCV2019.py
Workshops:
http://openaccess.thecvf.com/ICCV2019_workshops/menu.py
http://openaccess.thecvf.com/ICCV2019.py
Workshops:
http://openaccess.thecvf.com/ICCV2019_workshops/menu.py