All the Super-Resolution algorithms in one place.
"A Deep Journey into Super-resolution: A Survey"
#pytorch #ai #algorithms
https://lnkd.in/dfnd5se
β΄οΈ @AI_Python_EN
"A Deep Journey into Super-resolution: A Survey"
#pytorch #ai #algorithms
https://lnkd.in/dfnd5se
β΄οΈ @AI_Python_EN
***Anomaly Detection Cheat sheet***
β΄οΈ @AI_Python_EN
β΄οΈ @AI_Python_EN
paper "Feature Grouping as a Stochastic Regularizer for High-Dimensional Structured Data" with BertrandThirion and Gael Varoquaux got accepted to #ICML2019 ! Arxiv: https://arxiv.org/abs/1807.11718# code: https://github.com/sergulaydore/Feature-Grouping-Regularizer
β΄οΈ @AI_Python_EN
β΄οΈ @AI_Python_EN
HOW DO YOU KNOW THAT YOU HAVE ENOUGH TRAINING DATA? Check out my MEDIUM post:
https://lnkd.in/eVadKPb
#machineleaning
β΄οΈ @AI_Python_EN
https://lnkd.in/eVadKPb
#machineleaning
β΄οΈ @AI_Python_EN
What does a machine learning engineers day look like?
Someone asked me what skills they should learn for the rest of the year if they wanted to get into machine learning.
It's hard to narrow it down so I shared what my days usually look like.
9 am - reading articles/papers online about machine learning.
10 am - working on the current project and (sometimes) applying what I've just been reading online.
4 pm - pushing code to GitHub and writing down experiments for the next day.
5 pm - sending a small report to the team about what I've been working on during the day.
(these are all ideal scenarios)
Now, what happens during the 10-4pm?
Usually, it will be all be Python code within a Jupyter Notebook playing with different datasets.
Right now, I'm working on a text classification problem using the Flair library.
So what should you learn?
In my case, the following have been the most valuable.
1. Exploring and analysing new datasets, this notebook by Daniel Formosso is a great example: https://lnkd.in/gbayWcQ
2. Researching and mixing together existing methods and applying them to solve problems.
So how can you practice these outside of a job?
Kaggle and your own projects (even if they don't work).
#machinelearning #datascience
β΄οΈ @AI_Python_EN
Someone asked me what skills they should learn for the rest of the year if they wanted to get into machine learning.
It's hard to narrow it down so I shared what my days usually look like.
9 am - reading articles/papers online about machine learning.
10 am - working on the current project and (sometimes) applying what I've just been reading online.
4 pm - pushing code to GitHub and writing down experiments for the next day.
5 pm - sending a small report to the team about what I've been working on during the day.
(these are all ideal scenarios)
Now, what happens during the 10-4pm?
Usually, it will be all be Python code within a Jupyter Notebook playing with different datasets.
Right now, I'm working on a text classification problem using the Flair library.
So what should you learn?
In my case, the following have been the most valuable.
1. Exploring and analysing new datasets, this notebook by Daniel Formosso is a great example: https://lnkd.in/gbayWcQ
2. Researching and mixing together existing methods and applying them to solve problems.
So how can you practice these outside of a job?
Kaggle and your own projects (even if they don't work).
#machinelearning #datascience
β΄οΈ @AI_Python_EN
Don't stop sharing, done is better than perfect
For people who actively continue to blame, condemn and complain online, especially when reacting to content containing statistics, programming and machine learning that has been simplified, look for value in the imperfections of others.
We both know that machine learning models will never be perfect, as George P.Box said, "there are no perfect models, but some are useful". As with the content mentioned above, there are often reduced details to facilitate understanding, actionability, business value and expand the spread of knowledge.
Not all of us will face cases that are on each topic of the content mentioned above, but if we know in part, we can get the opportunity to work on a better process, even helping people.
Don't stop sharing, done is better than perfect
#programming #statistics #machinelearning
β΄οΈ @AI_Python_EN
For people who actively continue to blame, condemn and complain online, especially when reacting to content containing statistics, programming and machine learning that has been simplified, look for value in the imperfections of others.
We both know that machine learning models will never be perfect, as George P.Box said, "there are no perfect models, but some are useful". As with the content mentioned above, there are often reduced details to facilitate understanding, actionability, business value and expand the spread of knowledge.
Not all of us will face cases that are on each topic of the content mentioned above, but if we know in part, we can get the opportunity to work on a better process, even helping people.
Don't stop sharing, done is better than perfect
#programming #statistics #machinelearning
β΄οΈ @AI_Python_EN
Introduction to Applied Linear Algebra β Vectors, Matrices, and Least Squares
By Stephen Boyd and Lieven Vandenberghe, Cambridge University Press: https://lnkd.in/eQnqVQ9
#ArtificialIntelligence #LinearAlgebra #Vectors #Matrices #MachineLearning
β΄οΈ @AI_Python_EN
By Stephen Boyd and Lieven Vandenberghe, Cambridge University Press: https://lnkd.in/eQnqVQ9
#ArtificialIntelligence #LinearAlgebra #Vectors #Matrices #MachineLearning
β΄οΈ @AI_Python_EN
All ***Cheat Sheets*** in one place.
Github link - https://lnkd.in/fGeGXQs
#datascience #machinelearning #excel #deeplearning #python #R #sql #matlab #datamining #datawarehousing
β΄οΈ @AI_Python_EN
Github link - https://lnkd.in/fGeGXQs
#datascience #machinelearning #excel #deeplearning #python #R #sql #matlab #datamining #datawarehousing
β΄οΈ @AI_Python_EN
Learn how to train BERT faster with Tensor Cores for optimized #NLP in this technical blog. Code now available from GitHub.
β΄οΈ @AI_Python_EN
β΄οΈ @AI_Python_EN
In a simple key driver analysis, we may have a single dependent variable and a dozen or so predictors.
Even in this simple case, there are many ways to analyze the data. We might, for instance, realize, that one or more of the predictors is really endogenous, i.e., itself a dependent variable, or that it does not belong in our analysis at all.
Multicollinearity is common in many kinds of data and can be a major headache. Curvilinear relationships, interaction effects, missing data and clustering are other things we need to think about.
Some recommend machine learning as the solution. Indeed, this may be an option, but we must remember that there are many types of #machinelearning . Each may give very different answers. Machine learners can also be hard to interpret, and explanation is the main purpose of key driver.
Others may be tempted to just use cross tabs. But that too, in a sense, is a model and it may be a very inappropriate one that seriously misleads us.
There often is no simple answer to "simple" problems. Understanding decision makers needs and expectations is a fundamental first step.
Extensive data cleaning may also be necessarily and, in the case of surveys, we may need to adjust for response styles. At the end of our exploratory data analysis, we might also conclude that the data we have aren't right for the task. It's important to bear in mind that key driver analysis is a form of causal analysis, which is usually very challenging.
β΄οΈ @AI_Python_EN
Even in this simple case, there are many ways to analyze the data. We might, for instance, realize, that one or more of the predictors is really endogenous, i.e., itself a dependent variable, or that it does not belong in our analysis at all.
Multicollinearity is common in many kinds of data and can be a major headache. Curvilinear relationships, interaction effects, missing data and clustering are other things we need to think about.
Some recommend machine learning as the solution. Indeed, this may be an option, but we must remember that there are many types of #machinelearning . Each may give very different answers. Machine learners can also be hard to interpret, and explanation is the main purpose of key driver.
Others may be tempted to just use cross tabs. But that too, in a sense, is a model and it may be a very inappropriate one that seriously misleads us.
There often is no simple answer to "simple" problems. Understanding decision makers needs and expectations is a fundamental first step.
Extensive data cleaning may also be necessarily and, in the case of surveys, we may need to adjust for response styles. At the end of our exploratory data analysis, we might also conclude that the data we have aren't right for the task. It's important to bear in mind that key driver analysis is a form of causal analysis, which is usually very challenging.
β΄οΈ @AI_Python_EN
If your data makes sense then it is either fake or generated.
β΄οΈ @AI_Python_EN
β΄οΈ @AI_Python_EN
LBS Autoencoder: Self-supervised Fitting of Articulated Meshes to Point Clouds
Paper: http://ow.ly/ga4c50rqgsN
#artificialinteligence #machineleaning #bigdata #machinelearning #deeplearning #technology
β΄οΈ @AI_Python_EN
Paper: http://ow.ly/ga4c50rqgsN
#artificialinteligence #machineleaning #bigdata #machinelearning #deeplearning #technology
β΄οΈ @AI_Python_EN
Machine Learning (ML) & Artificial Intelligence (AI): From Black Box to White Box Models in 4 Steps - Resources for Explainable AI & ML Model Interpretability.
βοΈSTEP 1 - ARTICLES
- (short) KDnuggets article: https://lnkd.in/eRyTXcQ
- (long) O'Reilly article: https://lnkd.in/ehMHYsr
βοΈSTEP 2 - BOOKS
- Interpretable Machine Learning: A Guide for Making Black Box Models Explainable (free e-book): https://lnkd.in/eUWfa5y
- An Introduction to Machine Learning Interpretability: An Applied Perspective on Fairness, Accountability, Transparency, and Explainable AI (free e-book): https://lnkd.in/dJm595N
βοΈSTEP 3 - COLLABORATE
- Join Explainable AI (XAI) Group: https://lnkd.in/dQjmhZQ
βοΈSTEP 4 - PRACTICE
- Hands-On Practice: Open-Source Tools & Tutorials for ML Interpretability (Python/R): https://lnkd.in/d5bXgV7
- Python Jupyter Notebooks: https://lnkd.in/dETegUH
#machinelearning #datascience #analytics #bigdata #statistics #artificialintelligence #ai #datamining #deeplearning #neuralnetworks #interpretability #science #research #technology #business #healthcare
β΄οΈ @AI_Python_EN
βοΈSTEP 1 - ARTICLES
- (short) KDnuggets article: https://lnkd.in/eRyTXcQ
- (long) O'Reilly article: https://lnkd.in/ehMHYsr
βοΈSTEP 2 - BOOKS
- Interpretable Machine Learning: A Guide for Making Black Box Models Explainable (free e-book): https://lnkd.in/eUWfa5y
- An Introduction to Machine Learning Interpretability: An Applied Perspective on Fairness, Accountability, Transparency, and Explainable AI (free e-book): https://lnkd.in/dJm595N
βοΈSTEP 3 - COLLABORATE
- Join Explainable AI (XAI) Group: https://lnkd.in/dQjmhZQ
βοΈSTEP 4 - PRACTICE
- Hands-On Practice: Open-Source Tools & Tutorials for ML Interpretability (Python/R): https://lnkd.in/d5bXgV7
- Python Jupyter Notebooks: https://lnkd.in/dETegUH
#machinelearning #datascience #analytics #bigdata #statistics #artificialintelligence #ai #datamining #deeplearning #neuralnetworks #interpretability #science #research #technology #business #healthcare
β΄οΈ @AI_Python_EN
Interpretable and Generalizable Deep Image Matching with Adaptive Convolutions
Researchers: Shengcai Liao, Ling Shao
Paper: http://ow.ly/5z8j50rqdiJ
#artificialinteligence #machineleaning #bigdata #machinelearning #deeplearning
β΄οΈ @AI_Python_EN
Researchers: Shengcai Liao, Ling Shao
Paper: http://ow.ly/5z8j50rqdiJ
#artificialinteligence #machineleaning #bigdata #machinelearning #deeplearning
β΄οΈ @AI_Python_EN
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Today, #LIDAR is used in all autonomous cars except in Tesla
Lidar sensors are big, bulky, expensive, and ugly to look at. Not only that, they do a poor job in snow, sleet, hail, smoke, and smog. If you canβt see the road ahead, neither can LIDAR!.
That last part is one of the reasons Elon Musk refuses to incorporate lidar sensors into the self-driving hardware package for Tesla cars.
Apple & Cornell University have solved the problem of depth precision and this paves the way for faster adoption for safer yet cheaper cars!
Read more here: https://lnkd.in/dZgS6id
Research paper: https://lnkd.in/djRhzq3
#research #selfdriving #deeplearning
β΄οΈ @AI_Python_EN
Lidar sensors are big, bulky, expensive, and ugly to look at. Not only that, they do a poor job in snow, sleet, hail, smoke, and smog. If you canβt see the road ahead, neither can LIDAR!.
That last part is one of the reasons Elon Musk refuses to incorporate lidar sensors into the self-driving hardware package for Tesla cars.
Apple & Cornell University have solved the problem of depth precision and this paves the way for faster adoption for safer yet cheaper cars!
Read more here: https://lnkd.in/dZgS6id
Research paper: https://lnkd.in/djRhzq3
#research #selfdriving #deeplearning
β΄οΈ @AI_Python_EN
Despite attempts at standardisation of DL libraries, there are only a few that integrate classification, segmentation, GAN's and detection. And everything is in #PyTorch :)
https://lnkd.in/eTsqKWZ
#ai #objectdetection #machinelearning #gpu #classification #dl
β΄οΈ @AI_Python_EN
https://lnkd.in/eTsqKWZ
#ai #objectdetection #machinelearning #gpu #classification #dl
β΄οΈ @AI_Python_EN
Linked Dynamic Graph CNN: Learning on Point Cloud via Linking Hierarchical Features
Zhang et al.: https://lnkd.in/daMV2RX
#ArtificialIntelligence #DeepLearning #MachineLearning
β΄οΈ @AI_Python_EN
Zhang et al.: https://lnkd.in/daMV2RX
#ArtificialIntelligence #DeepLearning #MachineLearning
β΄οΈ @AI_Python_EN
The ability to deal with imbalanced datasets is a must-have for any #datascientist. Here are 4 tutorials to learn the different techniques of handling imbalanced data:
How to handle Imbalanced #Classification Problems in #MachineLearning? - https://buff.ly/2sIsR0M
Investigation on Handling Structured & Imbalanced Datasets with #DeepLearning - https://buff.ly/2MpxuG1
This Machine Learning Project on Imbalanced Data Can Add Value to Your #DataScience #Resume - https://buff.ly/2Mpr2i0
Practical Guide to deal with Imbalanced Classification Problems in #R - https://buff.ly/2MrS8Fr
β΄οΈ @AI_Python_EN
How to handle Imbalanced #Classification Problems in #MachineLearning? - https://buff.ly/2sIsR0M
Investigation on Handling Structured & Imbalanced Datasets with #DeepLearning - https://buff.ly/2MpxuG1
This Machine Learning Project on Imbalanced Data Can Add Value to Your #DataScience #Resume - https://buff.ly/2Mpr2i0
Practical Guide to deal with Imbalanced Classification Problems in #R - https://buff.ly/2MrS8Fr
β΄οΈ @AI_Python_EN
βοΈTop #GAN Research Papers Every Machine Learning Enthusiast Must Peruse
https://www.analyticsindiamag.com/top-gan-research-papers-every-machine-learning-enthusiast-must-peruse/
β΄οΈ @AI_Python_EN
https://www.analyticsindiamag.com/top-gan-research-papers-every-machine-learning-enthusiast-must-peruse/
β΄οΈ @AI_Python_EN
How can we make #computervision networks more robust against image distortions so small that theyβre undetectable to the human eye? Check out this paper on stability training as a potential solution and alternative to data augmentation techniques:
http://bit.ly/2XKA7Xj
β΄οΈ @AI_Python_EN
http://bit.ly/2XKA7Xj
β΄οΈ @AI_Python_EN