microsoft-machine-learning-algorithm-cheat-sheet-v6.pdf
404.1 KB
#Machine_Learning
#Cheat_Sheet
💥Machine learning algorithm cheat sheet
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@autonomousvehicle
#Cheat_Sheet
💥Machine learning algorithm cheat sheet
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@machinelearning_tuts
@autonomousvehicle
Machine Learning Cheat Sheet 2015.pdf
1.9 MB
#Machine_Learning
#Cheat_Sheet
💥Machine learning algorithm cheat sheet, Classical Equations, Diagrams and Tricks
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@machinelearning_tuts
@autonomousvehicle
#Cheat_Sheet
💥Machine learning algorithm cheat sheet, Classical Equations, Diagrams and Tricks
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@machinelearning_tuts
@autonomousvehicle
1704.06825.pdf
1007 KB
#Deep_Learning_for_Medical #Image #Processing:Overview, Challenges and Future
#paper
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#paper
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Intention-based Information Retrieval of EHR.pdf
279.1 KB
#Intention_based_Information_Retrieval
of_Electronic_Healt_Records
#paper
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@machinelearning_tuts
of_Electronic_Healt_Records
#paper
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@machinelearning_tuts
Multi-agent System for Medical Records Processing .pdf
526.1 KB
#Towards a Multi-agent System for Medical Records Processing and
Knowledge Discovery
#paper
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@machinelearning_tuts
Knowledge Discovery
#paper
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@machinelearning_tuts
https://instagram-engineering.com/lessons-learned-at-instagram-stories-and-feed-machine-learning-54f3aaa09e56
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@machinelearning_tuts
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@machinelearning_tuts
Medium
Lessons Learned at Instagram Stories and Feed Machine Learning
Instagram machine learning has grown a lot since we announced Feed ranking back in 2016. Our recommender system serves over 1 billion users…
❄️ Welcome to "The GAN Zoo"
https://github.com/hindupuravinash/the-gan-zoo
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@machinelearning_tuts
https://github.com/hindupuravinash/the-gan-zoo
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@machinelearning_tuts
#resources
https://www.analyticsindiamag.com/the-best-resources-for-learning-deep-learning-for-beginners
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@machinelearning_tuts
https://www.analyticsindiamag.com/the-best-resources-for-learning-deep-learning-for-beginners
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@machinelearning_tuts
Analytics India Magazine
The Best Resources For Learning Deep Learning For Beginners
Over the last few years, Deep Learning has proven itself to be the game-changer. This area of data science is the only one responsible for the advancements
پنج الگوريتم خوشه بندي كه هر ديتاساينتيست بايد بداند
https://towardsdatascience.com/the-5-clustering-algorithms-data-scientists-need-to-know-a36d136ef68
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https://towardsdatascience.com/the-5-clustering-algorithms-data-scientists-need-to-know-a36d136ef68
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@machinelearning_tuts
Medium
The 5 Clustering Algorithms Data Scientists Need to Know
Clustering is a Machine Learning technique that involves the grouping of data points. Given a set of data points, we can use a clustering algorithm to classify each data point into a specific group…
Top 10 #deeplearning research papers as per this website
https://lnkd.in/dPYayt9
Of course the choice remains biased but we do like these besides a few hundred other papers.
Remember, it is not the popular but the meaningful and industry relevant research that is worth paying attention to.
Here's the list:
1. Universal Language Model Fine-tuning for Text Classification
https://lnkd.in/dhj5SyM
2. Obfuscated Gradients Give a False Sense of Security: Circumventing Defenses to Adversarial Examples
https://lnkd.in/d44kt3Q
3. Deep Contextualized Word Representations
https://lnkd.in/dkP68Fb
4. An Empirical Evaluation of Generic Convolutional and Recurrent Networks for Sequence Modeling
https://lnkd.in/dAhYzge
5. Delayed Impact of Fair Machine Learning
https://lnkd.in/dvTvG2s
6. World Models
7. Taskonomy: Disentangling Task Transfer Learning
https://lnkd.in/dYxMjAd
8. Know What You Don’t Know: Unanswerable Questions for SQuAD
https://lnkd.in/d--grME
9. Large Scale GAN Training for High Fidelity Natural Image Synthesis
https://lnkd.in/dY6psf4
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://lnkd.in/dgtnD7n
#machinelearning #research #deeplearning #artificialintelligence
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@machinelearning_tuts
https://lnkd.in/dPYayt9
Of course the choice remains biased but we do like these besides a few hundred other papers.
Remember, it is not the popular but the meaningful and industry relevant research that is worth paying attention to.
Here's the list:
1. Universal Language Model Fine-tuning for Text Classification
https://lnkd.in/dhj5SyM
2. Obfuscated Gradients Give a False Sense of Security: Circumventing Defenses to Adversarial Examples
https://lnkd.in/d44kt3Q
3. Deep Contextualized Word Representations
https://lnkd.in/dkP68Fb
4. An Empirical Evaluation of Generic Convolutional and Recurrent Networks for Sequence Modeling
https://lnkd.in/dAhYzge
5. Delayed Impact of Fair Machine Learning
https://lnkd.in/dvTvG2s
6. World Models
7. Taskonomy: Disentangling Task Transfer Learning
https://lnkd.in/dYxMjAd
8. Know What You Don’t Know: Unanswerable Questions for SQuAD
https://lnkd.in/d--grME
9. Large Scale GAN Training for High Fidelity Natural Image Synthesis
https://lnkd.in/dY6psf4
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://lnkd.in/dgtnD7n
#machinelearning #research #deeplearning #artificialintelligence
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@machinelearning_tuts
TOPBOTS
Easy-To-Read Summary of Important AI Research Papers of 2018
Trying to keep up with AI research papers can feel like an exercise in futility given how quickly the industry moves. If you’re buried in papers to read that you haven’t quite gotten around to, you’re in luck. To help you catch up, we’ve summarized 10 important…
#datascience #machinelearning #learning #beginner #python
Learn Python in 30 days.
https://www.datasciencecentral.com/m/blogpost?id=6448529:BlogPost:792505
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Learn Python in 30 days.
https://www.datasciencecentral.com/m/blogpost?id=6448529:BlogPost:792505
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@machinelearning_tuts
Deep Learning in Software Engineering
--Abstract
Recent years, deep learning is increasingly prevalent in the field ofSoftware Engineering (SE). However, many open issues still remain to beinvestigated. How do researchers integrate deep learning into SE problems?Which SE phases are facilitated by deep learning? Do practitioners benefit fromdeep learning? The answers help practitioners and researchers develop practicaldeep learning models for SE tasks. To answer these questions, we conduct abibliography analysis on 98 research papers in SE that use deep learningtechniques. We find that 41 SE tasks in all SE phases have been facilitated bydeep learning integrated solutions. In which, 84.7% papers only use standarddeep learning models and their variants to solve SE problems. Thepracticability becomes a concern in utilizing deep learning techniques. How toimprove the effectiveness, efficiency, understandability, and testability ofdeep learning based solutions may attract more SE researchers in the future.
@selfdrivecar
@autonomousvehicle
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Link : http://arxiv.org/abs/1805.04825v1
--Abstract
Recent years, deep learning is increasingly prevalent in the field ofSoftware Engineering (SE). However, many open issues still remain to beinvestigated. How do researchers integrate deep learning into SE problems?Which SE phases are facilitated by deep learning? Do practitioners benefit fromdeep learning? The answers help practitioners and researchers develop practicaldeep learning models for SE tasks. To answer these questions, we conduct abibliography analysis on 98 research papers in SE that use deep learningtechniques. We find that 41 SE tasks in all SE phases have been facilitated bydeep learning integrated solutions. In which, 84.7% papers only use standarddeep learning models and their variants to solve SE problems. Thepracticability becomes a concern in utilizing deep learning techniques. How toimprove the effectiveness, efficiency, understandability, and testability ofdeep learning based solutions may attract more SE researchers in the future.
2018-05-13T06:01:39Z@machinelearning_tuts
@selfdrivecar
@autonomousvehicle
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Link : http://arxiv.org/abs/1805.04825v1
arXiv.org
Deep Learning in Software Engineering
Recent years, deep learning is increasingly prevalent in the field of Software Engineering (SE). However, many open issues still remain to be investigated. How do researchers integrate deep...