AI, Python, Cognitive Neuroscience

Lesson 5: Deep Dream implementation using Swift for #TensorFlow.

Colab: https://colab.research.google.com/github/zaidalyafeai/Swift4TF/blob/master/Swift4TF_DeepDream.ipynb …

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Generative adversarial networks for text generation: non-RL methods
https://medium.com/@karthik.chintapalli/generative-adversarial-networks-for-text-generation-part-3-non-rl-methods-70d1be02350b
#NLP

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When the paper you’re reading keeps citing another paper.

#deeplearning #jokes
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A deep learning model developed by NVIDIA Research turns rough doodles into highly realistic scenes using generative adversarial networks (GANs). Dubbed GauGAN, the tool is like a smart paintbrush, converting segmentation maps into lifelike images.

#GAN #deeplearning

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"Endlessly Generating Increasingly Complex and Diverse Learning Environments and their Solutions through the Paired Open-Ended Trailblazer (POET)"

Slides by Jeff Clune: https://lnkd.in/ePpcNQS

#neuroevolution #evolutionstrategy #machinelearning

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Another lovely development in #Healthcare #DeepLearning

Building a Benchmark Dataset and Classifiers for Sentence-Level Findings in AP Chest X-rays.

#datasets
Arxiv: https://lnkd.in/dxx5iCY

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TensorFlow 2.0 Beta has just been released!! This time, I am a big fan. The new version is so good, so easy & intuitive, and game changing compared to the previous TensorFlow 1 versions. It has such massive value that I decided to make a huge course on TensorFlow 2.0, covering most of the useful models in Deep Learning and Artificial Intelligence. Seriously this is one of the most complete guides I’ve ever made: inside we implement ANNs, CNNs, RNNs, Deep Q-Learning, Transfer Learning, Fine Tuning, APIs for Mobile Apps, Computer Vision, Deep NLP, Data Validation, TensorFlow Extended and even Distributed Training handling multiple GPUs, all that in TensorFlow 2.0!

And that’s not all, during these first 72 hours you get three amazing Bonuses, including the highly demanded Yolo v3, one of the most powerful models in Computer Vision.

Link here:
https://lnkd.in/gBtZuMN

#machinelearning #deeplearning, #artificialintelligence #computervision #nlp #completeguide
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The same statistical or machine learning method can be programmed (implemented) in different ways, and this can have an impact on the results. (I'm not referring to programing errors.)

Moreover, the initial start seed can strongly affect a routine - change the start seed and the results may vary substantially.

So, the same method programmed the same way may give different results on the same data if you change the start seed.

Most (hopefully all) statisticians are aware of this, but I suspect most users (e.g., decision makers) are not. "AI" is not immune to this.

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Human in the Loop: Deep Learning without Wasteful Labelling

Kirsch et al.: https://lnkd.in/eP323W3

Code: https://lnkd.in/e7-wbxD

#activelearning #deeplearning #informationtheory
#machinelearning

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Quick links for all things #R and #Python:

1. Overview of using python with RStudio: https://lnkd.in/d5NkJAt
2. Python & #shiny: https://lnkd.in/dVfkE6b
3. Python & #rmarkdown: https://lnkd.in/dXpSd7i
4. Python with #plumber: https://lnkd.in/dn2pEAQ

For a central location to publish all of your team's data products (R artifacts, R & python mixed assets, and #jupyternotebooks), check out RStudio Connect: https://lnkd.in/dXW7iPG

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talk at PyData Amsterdam 2019 on our low-to-high resolution project is out! If you missed his talk at PyData Amsterdam or in general if you're interested in image super resolution, check out his video and also of course our Github repo for more information. #deeplearning #machinelearning #AxelSpringerAI

▶️ YouTube Video: https://lnkd.in/d6YHaFS
🔤 Code: https://lnkd.in/dkJUaQe

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Stunning big picture #infographic depicting how #MachineLearning works, its relationship to #AI, and where companies are putting it to work.

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eleased additional "image-wise" models from our paper "Deep Neural Networks Improve Radiologists' Performance in Breast Cancer Screening". These models act on images individually making them well suited for transfer learning.
https://github.com/nyukat/breast_cancer_classifier

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Look at this amazing collection of resources for teaching reproducible research to university students! 👨‍🎓.
https://guides.lib.uw.edu/research/reproducibility/teaching

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decentralized decision making is out on arxiv: "Reinforcement Learning with Competitive Ensembles of Information-Constrained Primitives".
Link: https://arxiv.org/abs/1906.10667 .

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summary of interesting optimization papers from ICLR 2019:
part I :
https://medium.com/@yaroslavvb/iclr-optimization-papers-i-fluctuation-dissipation-relations-for-sgd-a638ad9964cc
part II
https://medium.com/@yaroslavvb/iclr-optimization-papers-ii-44f03b98dc5f?postPublishedType=repub
part III
https://medium.com/@yaroslavvb/iclr-optimization-papers-iii-1e1edc050ba6

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All the datasets (there are a lot) released at #cvpr2019 are now indexed in
http://visualdata.io . Check them out!
#computervision #machinelearning #dataset

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Using #DeepLearning to produce an #AutonomousSystem for detecting traffic signs on Google Street View images. The system could help to monitor street signs and identify those in need of replacement or repair.
Read: http://ow.ly/WW4630oZzJu

https://doi.org/10.1016/j.compenvurbsys.2019.101350

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This is incredible. This paper from MIT Computer Science & Artificial Intelligence Lab presented at #cvpr2019 shows how to reconstruct a face from speech patterns.
https://speech2face.github.io

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The #xtensor article series continues! Learn everything about xtensor constructors and initializer lists in

https://medium.com/@johan.mabille/how-we-wrote-xtensor-3-n-the-constructors-65a177260638


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