Deep Learning Weekly | Issue #8: Deep Learning School, Neural Photo Editing, Object Detection and more

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Wow, we really needed to whittle it down this week!This industry is full of individual powerhouses in
September 27 · Issue #8 · View online
Deep Learning Weekly
Wow, we really needed to whittle it down this week!
This industry is full of individual powerhouses innovating, researching, optimizing and sharing new technology, applications, and knowledge.
We were particularly impressed this week by neural photo editing (magic!) and the quality of teaching at last weekend’s Deep Learning School, the video recordings of both days are available on youtube.

Here’s to hoping next week will be half as exciting as this one.

As always if you enjoy receiving this newsletter, please considering sharing it with friends and colleagues, your support is very much appreciated.

Google acquires natural language understanding startup | VentureBeat | Bots | by Jordan Novet
Deep Learning Obstacles: What's the Lesson?
Putting patients at the heart of DeepMind Health | DeepMind
Baidu Releases AI Benchmark | EE Times
Object Detection using Deep Learning for advanced users (Part-1)
GitHub - alrojo/tensorflow-tutorial: Practical tutorials and labs for TensorFlow used by Nvidia, FFN, CNN, RNN, Kaggle, AE
Deep Learning School
Prof. Yann LeCun - Deep Learning and the Future of AI - YouTube
Libraries & Code
Show and Tell: image captioning open sourced in TensorFlow
Improvements allow more detailed and accurate descriptions
GitHub - tomlepaine/fast-wavenet: Efficient implementation of Wavenet generation.
GitHub - openai/cleverhans: A library for benchmarking vulnerability to adversarial examples
GitHub - junyanz/iGAN: A deep learning software that easily generates images with a few brushstrokes (from UC Berkeley and Adobe CTL)
Papers & Publications
Show and Tell: Lessons learned from the 2015 MSCOCO Image Captioning Challenge
Neural Photo Editing with Introspective Adversarial Networks
Google's Neural Machine Translation System: Bridging the Gap between Human and Machine Translation