Artificial Intelligence && Deep Learning

Decoding the Best Papers from ICLR 2019 – Neural Networks are Here to Rule

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https://www.analyticsvidhya.com/blog/2019/05/best-papers-iclr-2019/

Diving deeper into Reinforcement Learning with Q-Learning

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https://medium.com/free-code-camp/diving-deeper-into-reinforcement-learning-with-q-learning-c18d0db58efe

Few-Shot Adversarial Learning of Realistic Neural Talking Head Models

paper — arxiv👇👇👇
https://arxiv.org/pdf/1905.08233.pdf

video — youtube👇👇👇
https://www.youtube.com/watch?v=p1b5aiTrGzY

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Few-Shot Adversarial Learning of Realistic Neural Talking Head Models
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Few-Shot Adversarial Learning of Realistic Neural Talking Head Models
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Stanford Machine Learning

Content
01 and 02: Introduction, Regression Analysis and Gradient Descent

03: Linear Algebra - review

04: Linear Regression with Multiple Variables

05: Octave[incomplete]

06: Logistic Regression

07: Regularization

08: Neural Networks - Representation

09: Neural Networks - Learning

10: Advice for applying machine learning techniques

11: Machine Learning System Design

12: Support Vector Machines

13: Clustering

14: Dimensionality Reduction

15: Anomaly Detection

16: Recommender Systems

17: Large Scale Machine Learning

18: Application Example - Photo OCR

19: Course Summary

http://www.holehouse.org/mlclass/

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Deep Learning lecture
The full deck of (600+) slides, by Gilles Louppe:


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https://glouppe.github.io/info8010-deep-learning/pdf/lec-all.pdf

Deep learning lecture

Not just another GAN paper — SAGAN – Towards Data Science

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https://towardsdatascience.com/not-just-another-gan-paper-sagan-96e649f01a6b

Diving into Deep Convolutional Semantic Segmentation Networks and Deeplab_V3

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https://sthalles.github.io/deep_segmentation_network/

Algorithms online Course from PRINCETON UNIVERSITY

About this Course
This course covers the essential information that every serious programmer needs to know about algorithms and data structures, with emphasis on applications and scientific performance analysis of Java implementations. Part I covers elementary data structures, sorting, and searching algorithms. Part II focuses on graph- and string-processing algorithms.

All the features of this course are available for free. It does not offer a certificate upon completion

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https://www.coursera.org/learn/algorithms-part1?ranMID=40328&ranEAID=SAyYsTvLiGQ&ranSiteID=SAyYsTvLiGQ-ayH4CcL5jMTprP4tidKo4g&siteID=SAyYsTvLiGQ-ayH4CcL5jMTprP4tidKo4g&utm_content=10&utm_medium=partners&utm_source=linkshare&utm_campaign=SAyYsTvLiGQ

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https://towardsdatascience.com/the-quest-of-higher-accuracy-for-cnn-models-42df5d731faf

DeepMind & Google Graph Matching Network Outperforms GNN

DeepMind and Google researchers have proposed a powerful new graph matching network (GMN) model for the retrieval and matching of graph structured objects. GMN uses similarity learning for graph structured objects and outperforms graph neural network (GNN) models on graph similarity learning (GSL) tasks.

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https://medium.com/syncedreview/deepmind-google-graph-matching-network-outperforms-gnn-c277d3ca6f75

On Choosing a Deep Reinforcement Learning Library

As Deep Reinforcement Learning is becoming one of the most hyped strategies to achieve AGI (aka Artificial General Intelligence) more and more libraries are developed.
And choosing the best for your needs can be a daunting task…


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https://medium.com/data-from-the-trenches/choosing-a-deep-reinforcement-learning-library-890fb0307092

https://blog.evjang.com/2019/05/fun-with-snapchats-gender-swapping.html

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Fun with Snapchat's Gender Swapping Filter

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How to become an expert in NLP in 2019 (1)

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https://medium.com/@kushajreal/how-to-become-an-expert-in-nlp-in-2019-1-945f4e9073c0

My Top 5 Recommended Places to Learn about Deep Learning and Machine Learning

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https://medium.com/datadriveninvestor/my-top-5-recommended-places-to-learn-about-deep-learning-and-machine-learning-f95153a847e

Live Object Detection – Towards Data Science

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https://towardsdatascience.com/live-object-detection-26cd50cceffd