#AI is changing how we do #science. Get a glimpse

#ML #DataScience #BigData
#fintech #Insurtech
http://bit.ly/2uqdtp9

Stanford's Convolutional #NeuralNetworks for #Visual Recognition (CS231n): Course Projects Spring 2017 http://buff.ly/2tNXvIB

A 2017 Guide to Semantic Segmentation with Deep Learning
#AI #MachineLearning #DeepLearning #ML #DL #tech
http://blog.qure.ai/notes/semantic-segmentation-deep-learning-review

"Adversarial Representation Learning for Domain Adaptation": Wasserstein DANN...? https://arxiv.org/abs/1707.01217

Variance Regularizing Adversarial Learning" by MILA: meta-adversarial training for bi-modal distribution match https://arxiv.org/abs/1707.00309

Skeleton-aided Articulated Motion Generation: based on GAN rather than LSTM https://arxiv.org/abs/1707.01058

Discriminative Localization in CNNs for Weakly-Supervised Segmentation of Pulmonary Nodules": CNN+GAP+various scale https://arxiv.org/abs/1707.01086

Improving Content-Invariance in Gated Autoencoders for 2D and 3D Object Rotation": learning relations b/w instances https://arxiv.org/abs/1707.01357

Teacher-Student Curriculum Learning" by OpenAI, https://arxiv.org/abs/1707.00183

Advances in Deep Neural Networks," at ACM Turing 50 Celebration https://www.youtube.com/watch?v=mFYM9j8bGtg&feature=youtu.be

Deep Learning: A Practitioner's Approach here http://zpy.io/0463bcb8 #deeplearning

Exploring the structure of a real-time, arbitrary neural artistic stylization network. https://arxiv.org/abs/1705.06830

New work showing a principled way to learn geometry and semantics from a single deep learning model https://arxiv.org/pdf/1705.07115.pdf

GeneGAN: "Learning Object Transfiguration & Attribute Subspace from Unpaired Data": https://arxiv.org/abs/1705.04932v1 Code: https://github.com/Prinsphield/GeneGAN

Interpreting the black box: Interesting paper + code quantifies and examines interpretability of CNN architectures. http://netdissect.csail.mit.edu/

‍ (http://axnegar.fahares.com/axnegar/gulAyqskiOYd6X/5041842.jpg)


🌒جدیدترین کتاب دیپ لرنینگ منتشر شده در تاریخ July 5, 2017 در سایت امازون

🔵Deep Learning with Keras: Introduction to Deep Learning with Keras

This book will introduce you to various deep learning models in Keras, and you will see how different neural networks can be used in real-world examples as well as in various scientific fields. You will explore various Keras algorithms like the simplest linear regression or more complex deep convolutional network. You will get to know what is the difference between supervised and unsupervised deep learning and you will be able to implement various algorithms in Keras by yourself as you follow step-by-step guide in this book.

You will explore various applications of deep learning models such as speech recognition systems, natural language processing, and video game development. A whole new world will open in front of you since, by the time you reach the final page of this book, you will be a Keras expert and ready for your deep-learning projects.


http://buff.ly/2tQkj9P


#BigData #DeepLearning #MachinLearning #DataScience #AI #Books

Deep Reinforcement Learning For Social Dilemmas. #BigData #DeepLearning #MachineLearning #DataScience #AI
http://buff.ly/2utwFTd

Text Clustering : Get quick insights from Unstructured Data #abdsc http://buff.ly/2tQrRt0

"CNN features are also great at unsupervised classification": Now, it's common sense - ImageNet feature works https://arxiv.org/abs/1707.01700

Interesting! How AI detectives are cracking open the black box of deep learning http://www.sciencemag.org/news/2017/07/how-ai-detectives-are-cracking-open-black-box-deep-learning #DeepLearning #AI

The most cited (top 100) deep learning papers (2012-2016)
I'm maintaining this list. Please let me know good papers.
https://github.com/terryum/awesome-deep-learning-papers