A must-read for every non-programmer who aspires to become a data scientist ! Check out this article which lists down various tools that anyone with minimal knowledge of algorithms can use to build high quality machine learning models! #RapidMiner #DataRobot #BigML #GoogleCloudAutoML
https://lnkd.in/fgFAq8V
β΄οΈ @AI_Python_EN
https://lnkd.in/fgFAq8V
β΄οΈ @AI_Python_EN
Minimal implementation of Deep Dream in TensorFlow 2.0
By Josh Gordon: https://lnkd.in/eacj7KD
#art #aiart #deeplearning #tensorflow #technology
β΄οΈ @AI_Python_EN
By Josh Gordon: https://lnkd.in/eacj7KD
#art #aiart #deeplearning #tensorflow #technology
β΄οΈ @AI_Python_EN
I try to organize Cheatsheet that I share, here's the list
1. Data Science Implementation Cheatsheet
https://lnkd.in/fMHtxYP
2. Discovery Analytics Cheatsheet
https://lnkd.in/f396Dqg
3. SQL Cheatsheet
https://lnkd.in/fKyki2j
4. Machine Learning Cheatsheet
https://lnkd.in/fezaQme
5. Docker Chetasheet
https://lnkd.in/ffMrZXj
6. Tutorial Biglist
https://lnkd.in/fyFxQsM
7. Git Cheatsheet
https://lnkd.in/fWSHH_x
8. Self Driving Car
https://lnkd.in/fxMNBEh
9. Heathcare
https://lnkd.in/fn-yWSD
10. Data Science Cheatsheet
https://lnkd.in/fJgruHJ
#machinelearning #datascience #technology
β΄οΈ @AI_Python_EN
1. Data Science Implementation Cheatsheet
https://lnkd.in/fMHtxYP
2. Discovery Analytics Cheatsheet
https://lnkd.in/f396Dqg
3. SQL Cheatsheet
https://lnkd.in/fKyki2j
4. Machine Learning Cheatsheet
https://lnkd.in/fezaQme
5. Docker Chetasheet
https://lnkd.in/ffMrZXj
6. Tutorial Biglist
https://lnkd.in/fyFxQsM
7. Git Cheatsheet
https://lnkd.in/fWSHH_x
8. Self Driving Car
https://lnkd.in/fxMNBEh
9. Heathcare
https://lnkd.in/fn-yWSD
10. Data Science Cheatsheet
https://lnkd.in/fJgruHJ
#machinelearning #datascience #technology
β΄οΈ @AI_Python_EN
Getting started with #NLP using the #PyTorch framework; Building a #RecommenderSystem; Advice for New Data Scientists; All you need to know about text preprocessing for NLP and #MachineLearning; Advanced Keras - Constructing Complex Custom Losses and Metrics; Top 8 Data Science Use Cases in Gaming
https://bit.ly/2X1OW7E
β΄οΈ @AI_Python_EN
https://bit.ly/2X1OW7E
β΄οΈ @AI_Python_EN
Reinforcement Learning Applications in Business
Detail by Yuxi Li
https://lnkd.in/fR7uDNN
#reinforcementlearning #technology #ai
β΄οΈ @AI_Python_EN
Detail by Yuxi Li
https://lnkd.in/fR7uDNN
#reinforcementlearning #technology #ai
β΄οΈ @AI_Python_EN
image_2019-04-11_13-54-58.png
6.6 MB
Which is the best tool amongst #Python, #R and #SAS for the job? If you are also looking for an answer, then this Infographic is what you should follow. https://lnkd.in/frqar5E
β΄οΈ @AI_Python_EN
β΄οΈ @AI_Python_EN
Forest Fire Prediction with Artificial Neural Network (Part 2)
https://bit.ly/2G3iOte
#ANN
β΄οΈ @AI_Python_EN
https://bit.ly/2G3iOte
#ANN
β΄οΈ @AI_Python_EN
β³οΈ " All models are wrong, some are useful "
βΆοΈ βA model is a simplification or approximation of reality and hence will not reflect all of reality β¦ While a model can never be βtruth,β a model might be ranked from very useful, to useful, to somewhat useful to, finally, essentially useless.β
- Ken Burnham and David Anderson
βΆοΈ βA model which took account of all the variation of reality would be of no more use than a map at the scale of one to one.β
- Joan Robinson
βΆοΈ βThe world doesnβt have the luxury of waiting for complete answers before it takes action.β
β Daniel Gilbert
βΆοΈ βScientists generally agree that no theory is 100 percent correct. Thus, the real test of knowledge is not truth, but utility. Science gives us power. The more useful that power, the better the science.β
β Yuval Noah Harari
βοΈ How Do We Know If A Model Is Useful ?
#LearningthroughPN excerpts of things created by others
β΄οΈ @AI_Python_EN
βΆοΈ βA model is a simplification or approximation of reality and hence will not reflect all of reality β¦ While a model can never be βtruth,β a model might be ranked from very useful, to useful, to somewhat useful to, finally, essentially useless.β
- Ken Burnham and David Anderson
βΆοΈ βA model which took account of all the variation of reality would be of no more use than a map at the scale of one to one.β
- Joan Robinson
βΆοΈ βThe world doesnβt have the luxury of waiting for complete answers before it takes action.β
β Daniel Gilbert
βΆοΈ βScientists generally agree that no theory is 100 percent correct. Thus, the real test of knowledge is not truth, but utility. Science gives us power. The more useful that power, the better the science.β
β Yuval Noah Harari
βοΈ How Do We Know If A Model Is Useful ?
#LearningthroughPN excerpts of things created by others
β΄οΈ @AI_Python_EN
Important Machine Learning algorithms and their Hyperparameters
#machinelearning #datascience #statistics #algorithms
β΄οΈ @AI_Python_EN
#machinelearning #datascience #statistics #algorithms
β΄οΈ @AI_Python_EN
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THE IMPACT OF OVERFITTING AND HOW AVOID THEM?
12-years-old Tanmay Bakhsi (today 14) tell about Incredibly well spoken manner about what is overfitting and how to avoid them. He told difference about deep vs shallow neural network and difference about how to train them
Qlue: The solution is early stopping algorithm
Interested to know more, Full video with best resolution is avaliable in his youtube channel: https://lnkd.in/fzrFCvU
#deepleaning #neuralnetwork #artificailintelligence
β΄οΈ @AI_Python_EN
12-years-old Tanmay Bakhsi (today 14) tell about Incredibly well spoken manner about what is overfitting and how to avoid them. He told difference about deep vs shallow neural network and difference about how to train them
Qlue: The solution is early stopping algorithm
Interested to know more, Full video with best resolution is avaliable in his youtube channel: https://lnkd.in/fzrFCvU
#deepleaning #neuralnetwork #artificailintelligence
β΄οΈ @AI_Python_EN
Great MLT workshop on TensorFlow.js with Rei and Kai at Google Japan! In case you couldn't get a spot or want to revisit the resources and build a new application you can check out the GitHub repo https://lnkd.in/fnbyT8k
And some more TensorFlow.js examples https://lnkd.in/fVhhzAt
Join us for more Deep Learning workshops! https://lnkd.in/fmhgHMG
#deeplearning #ai #machinelearning
β΄οΈ @AI_Python_EN
And some more TensorFlow.js examples https://lnkd.in/fVhhzAt
Join us for more Deep Learning workshops! https://lnkd.in/fmhgHMG
#deeplearning #ai #machinelearning
β΄οΈ @AI_Python_EN
Why statistics should make you suspicious
Spiegelhalter on algorithm, luck, bias, probabilities, machine learning and AI.
https://lnkd.in/e-X9hXJ
#artificialintelligence #bias #ai #statistics #ai #bigdata
β΄οΈ @AI_Python_EN
Spiegelhalter on algorithm, luck, bias, probabilities, machine learning and AI.
https://lnkd.in/e-X9hXJ
#artificialintelligence #bias #ai #statistics #ai #bigdata
β΄οΈ @AI_Python_EN
victorzhou.com/blog/intro-to-neural-networks/ An Introduction to
#NeuralNetworks #deeplearning #machinelearning #python
β΄οΈ @AI_Python_EN
#NeuralNetworks #deeplearning #machinelearning #python
β΄οΈ @AI_Python_EN
Why are Scikit-learn machine learning models not as widely used in industry as TensorFlow or PyTorch?
The algorithms in scikit-learn are kind of like toy algorithms.
The neural networks are a joke. They were introduced only a couple of years ago and come in two flavors: MLPClassifier and MLPRegressor. MLP is for Multi-layer Perceptron. The name alone should be enough to tell you that this isnβt the greatest implementation. Scikit-learn doesnβt support GPUs and the neural networks donβt scale at all. No one in their right mind would use this in production.
The implementation of the popular gradient boosting algorithm is useless too. Known as GradientBoostingClassifier and GradientBoostingRegressor, itβs a painfully slow implementation that gets completely embarrassed by libraries like XGBoost, LightGBM and CatBoost. I should note that the scikit-learn team is working on a new implementation of gradient boosting that borrows heavily from LightGBM and XGBoost.
The random forest implementation is decent enough, but it generally gets outperformed by gradient boosting on almost any #machinelearning task anyway.
The #SVM implementation with #nonlinear kernels is extremely slow too, and generally useless.
The Naive Bayes implementation is okay, I guess, but itβs not a type of model that one would realistically use in production.
#Logisticregression can actually be useful. If the requirement is a simple classifier thatβs fast to train and easy to interpret, this can be a good choice, even in production. I mean, itβs pretty hard to get a dead simple algorithm like that wrong.
The linear regression algorithms are completely fine too. OLS, ridge regression, lasso, elastic nets and what have you. These can be useful for simple tasks that need interpretability.
I love scikit-learn for its helper functions for things like preprocessing, cross-validation, hyperparameter tuning and so on, but itβs generally not a library thatβs suited for any sort of heavy lifting when it comes to model training.
β΄οΈ @AI_Python_EN
The algorithms in scikit-learn are kind of like toy algorithms.
The neural networks are a joke. They were introduced only a couple of years ago and come in two flavors: MLPClassifier and MLPRegressor. MLP is for Multi-layer Perceptron. The name alone should be enough to tell you that this isnβt the greatest implementation. Scikit-learn doesnβt support GPUs and the neural networks donβt scale at all. No one in their right mind would use this in production.
The implementation of the popular gradient boosting algorithm is useless too. Known as GradientBoostingClassifier and GradientBoostingRegressor, itβs a painfully slow implementation that gets completely embarrassed by libraries like XGBoost, LightGBM and CatBoost. I should note that the scikit-learn team is working on a new implementation of gradient boosting that borrows heavily from LightGBM and XGBoost.
The random forest implementation is decent enough, but it generally gets outperformed by gradient boosting on almost any #machinelearning task anyway.
The #SVM implementation with #nonlinear kernels is extremely slow too, and generally useless.
The Naive Bayes implementation is okay, I guess, but itβs not a type of model that one would realistically use in production.
#Logisticregression can actually be useful. If the requirement is a simple classifier thatβs fast to train and easy to interpret, this can be a good choice, even in production. I mean, itβs pretty hard to get a dead simple algorithm like that wrong.
The linear regression algorithms are completely fine too. OLS, ridge regression, lasso, elastic nets and what have you. These can be useful for simple tasks that need interpretability.
I love scikit-learn for its helper functions for things like preprocessing, cross-validation, hyperparameter tuning and so on, but itβs generally not a library thatβs suited for any sort of heavy lifting when it comes to model training.
β΄οΈ @AI_Python_EN
Machine Learning from scratch!
Implementation of some classic Machine Learning model from scratch and benchmarking against popular ML library, by Quan Tran: https://lnkd.in/er_ZNgY
#ArtificialIntelligence #DeepLearning #NeuralNetworks
#MachineLearning
Implementation of some classic Machine Learning model from scratch and benchmarking against popular ML library, by Quan Tran: https://lnkd.in/er_ZNgY
#ArtificialIntelligence #DeepLearning #NeuralNetworks
#MachineLearning
HOW TO IMPROVE YOUR SKILL ON TEXT DATA?
Rubens Zimbres, PhD compile amazing resources on Machine Learning, NLP, and Computer Vision. On NLP Side he cover pretty much every common topic on NLP, this is very useful because as data scientist we often dealing with text data.
Yo can see the repository here https://lnkd.in/fyyvZYt
#repository #machinelearning #patternrecognition #artificialintellegence
β΄οΈ @AI_Python_EN
Rubens Zimbres, PhD compile amazing resources on Machine Learning, NLP, and Computer Vision. On NLP Side he cover pretty much every common topic on NLP, this is very useful because as data scientist we often dealing with text data.
Yo can see the repository here https://lnkd.in/fyyvZYt
#repository #machinelearning #patternrecognition #artificialintellegence
β΄οΈ @AI_Python_EN
Here are some #statistics and research #journals I can recommend:
- Statistical Analysis and Data Mining (ASA)ο»Ώ
- Analytics Journal (DMA)
- The American Statistician (ASA)
- Journal of the American Statistical Association (ASA)
- Statistics in Biopharmaceutical Research (ASA)ο»Ώ
- Journal of Agricultural, Biological, and Environmental Statistics (ASA)
- Journal of Statistics Education (ASA)
- Statistics and Public Policy (ASA)
- Journal of Survey Statistics and Methodology (AAPOR and ASA)ο»Ώ
- Journal of Educational and Behavioral Statisticsο»Ώ (ASA)ο»Ώ
- British Journal of Mathematical and Statistical Psychology (Wiley)ο»Ώ
- Statistics Surveys (IMS)ο»Ώ
- Stata Journal (StataCorp)ο»Ώ
- The R Journal (R Project)
- Structural Equation Modeling: A Multidisciplinary Journal (Routledge)
- Journal of Business & Economic Statistics (ASA)
- Journal of Marketing Research (AMA)
- Journal of Computational and Graphical Statistics (ASA)
ο»Ώ- Journal of Artificial General Intelligence (AGIS)
These are not purely theoretical publications and provide plenty of examples I can adapt for my own work. I try to read them as regularly as I can.
There's so much innovation happening in analytics that it's hard to keep up!
β΄οΈ @AI_Python_EN
- Statistical Analysis and Data Mining (ASA)ο»Ώ
- Analytics Journal (DMA)
- The American Statistician (ASA)
- Journal of the American Statistical Association (ASA)
- Statistics in Biopharmaceutical Research (ASA)ο»Ώ
- Journal of Agricultural, Biological, and Environmental Statistics (ASA)
- Journal of Statistics Education (ASA)
- Statistics and Public Policy (ASA)
- Journal of Survey Statistics and Methodology (AAPOR and ASA)ο»Ώ
- Journal of Educational and Behavioral Statisticsο»Ώ (ASA)ο»Ώ
- British Journal of Mathematical and Statistical Psychology (Wiley)ο»Ώ
- Statistics Surveys (IMS)ο»Ώ
- Stata Journal (StataCorp)ο»Ώ
- The R Journal (R Project)
- Structural Equation Modeling: A Multidisciplinary Journal (Routledge)
- Journal of Business & Economic Statistics (ASA)
- Journal of Marketing Research (AMA)
- Journal of Computational and Graphical Statistics (ASA)
ο»Ώ- Journal of Artificial General Intelligence (AGIS)
These are not purely theoretical publications and provide plenty of examples I can adapt for my own work. I try to read them as regularly as I can.
There's so much innovation happening in analytics that it's hard to keep up!
β΄οΈ @AI_Python_EN
Not everyone knows but my #book has its Github repository where all #Python code used to build illustrations is gathered.
So, while reading the book, you can actually run the described #algorithms, play with hyperparameters and #datasets, and generate your versions of illustrations.
https://github.com/aburkov/theMLbook
β΄οΈ @AI_Python_EN
So, while reading the book, you can actually run the described #algorithms, play with hyperparameters and #datasets, and generate your versions of illustrations.
https://github.com/aburkov/theMLbook
β΄οΈ @AI_Python_EN
Welcome to Word Vector Space (visualization)
Demo: https://lnkd.in/eWTHCEd
Blog: https://lnkd.in/e4WM8qy
#machinelearning #word2vec #nlp
β΄οΈ @AI_Python_EN
Demo: https://lnkd.in/eWTHCEd
Blog: https://lnkd.in/e4WM8qy
#machinelearning #word2vec #nlp
β΄οΈ @AI_Python_EN
Prior-aware Neural Network for Partially-Supervised Multi-Organ Segmentation
Researchers: Yuyin Zhou, Zhe Li, Song Bai, Chong Wang, Xinlei Chen, Mei Han, Elliot Fishman, Alan Yuille
Paper: http://ow.ly/IdmR50qiURd
#technology #artificialinteligence #machineleaning #bigdata #machinelearning #deeplearning
β΄οΈ @AI_Python_EN
Researchers: Yuyin Zhou, Zhe Li, Song Bai, Chong Wang, Xinlei Chen, Mei Han, Elliot Fishman, Alan Yuille
Paper: http://ow.ly/IdmR50qiURd
#technology #artificialinteligence #machineleaning #bigdata #machinelearning #deeplearning
β΄οΈ @AI_Python_EN