Open philusnarh opened 5 years ago
https://jakevdp.github.io/PythonDataScienceHandbook/04.08-multiple-subplots.html
Linear Regression Derivation https://towardsdatascience.com/linear-regression-derivation-d362ea3884c2
ASS https://launchpad.net/apsynsim
Juares' Work https://towardsdatascience.com/principal-component-analysis-for-dimensionality-reduction-115a3d157bad
Machine Learning Background Necessary for Deep Learning https://towardsdatascience.com/machine-learning-necessary-for-deep-learning-2095a345ec2c
Journal https://www.sciencedirect.com/journal/artificial-intelligence/vol/279/suppl/C https://www.springer.com/journal/12036 https://www.springer.com/gp/astronomy/astronomy-astrophysics-cosmology https://academic.oup.com/journals/pages/science_and_mathematics
ROC https://towardsdatascience.com/understanding-auc-roc-curve-68b2303cc9c5
PCA https://towardsdatascience.com/principal-component-analysis-for-dimensionality-reduction-115a3d157bad https://towardsdatascience.com/pca-vs-tsne-el-cl%C3%A1sico-9948181a5f87 https://jakevdp.github.io/PythonDataScienceHandbook/05.09-principal-component-analysis.html https://towardsdatascience.com/dimensionality-reduction-does-pca-really-improve-classification-outcome-6e9ba21f0a32 https://towardsdatascience.com/principal-component-analysis-for-dimensionality-reduction-115a3d157bad
feature extraction https://towardsdatascience.com/feature-extraction-techniques-d619b56e31be
General Lecture https://www.ritchieng.com/machine-learning/journals-library/ https://machine-learning-course.readthedocs.io/en/latest/content/deep_learning/autoencoder.html
Convolution cat & dog demo https://towardsdatascience.com/image-classifier-cats-vs-dogs-with-convolutional-neural-networks-cnns-and-google-colabs-4e9af21ae7a8 https://www.kaggle.com/c/dogs-vs-cats/data https://blog.keras.io/building-powerful-image-classification-models-using-very-little-data.html
GMM credit card https://www.kaggle.com/albertmistu/detect-anomalies-using-gmm
GMM Defn https://www.kaggle.com/dfoly1/gaussian-mixture-model/notebook https://github.com/PhilippeW83440/Coursera-AML3-Bayesian-Methods/blob/master/Coursera-BMML_-week-2.ipynb
pneumonia https://appliedmachinelearning.blog/2019/07/29/transfer-learning-using-feature-extraction-from-trained-models-food-images-classification/
https://medium.com/towards-artificial-intelligence/an-introduction-to-dropout-for-regularizing-deep-neural-networks-4e0826c10395 https://towardsdatascience.com/applied-deep-learning-part-1-artificial-neural-networks-d7834f67a4f6 https://towardsdatascience.com/introduction-to-artificial-neural-networks-ann-1aea15775ef9
Pipe https://scikit-learn.org/stable/tutorial/statistical_inference/putting_together.html LSTM https://stackabuse.com/time-series-analysis-with-lstm-using-pythons-keras-library/ https://scikit-learn.org/stable/supervised_learning.html https://www.kdnuggets.com/2019/05/machine-learning-time-series-forecasting.html
KL Divergence
https://bigdatascientistblog.wordpress.com/2017/09/11/a-simple-introduction-to-kullback-leibler-divergence-through-python-code/
https://machinelearningmastery.com/when-to-use-mlp-cnn-and-rnn-neural-networks/
lectures ml https://theclevermachine.wordpress.com/tag/backpropagation/ https://sebastianraschka.com/resources/dl-lectures.html http://www.cs.cmu.edu/~ninamf/courses/401sp18/lectures.shtml
An elegant way to represent forward propagation and backpropagation in a neural network https://www.datasciencecentral.com/profiles/blogs/an-elegant-way-to-represent-forward-propagation-and-back
Python args and kwargs https://medium.com/swlh/python-args-and-kwargs-a82b6480f287
Image download https://www.pyimagesearch.com/2017/12/04/how-to-create-a-deep-learning-dataset-using-google-images/ A single function to streamline image classification with Keras https://towardsdatascience.com/a-single-function-to-streamline-image-classification-with-keras-bd04f5cfe6df
The Advantages of DenseNet https://medium.com/the-advantages-of-densenet/the-advantages-of-densenet-98de3019cdac
Discrete Probability Distributions for Machine Learning https://machinelearningmastery.com/discrete-probability-distributions-for-machine-learning/
Beginners Guide to Convolutional Neural Networks https://towardsdatascience.com/beginners-guide-to-understanding-convolutional-neural-networks-ae9ed58bb17d
Visualizing 100,000 Amazon Products https://towardsdatascience.com/vis-amz-83dea6fcb059
GMM https://towardsdatascience.com/how-to-code-gaussian-mixture-models-from-scratch-in-python-9e7975df5252 https://towardsdatascience.com/medical-image-analysis-with-deep-learning-9557cad44944 https://towardsdatascience.com/maximum-likelihood-estimation-from-bayes-theorem-6cc7f0db9adb https://machinelearningmastery.com/bayes-theorem-for-machine-learning/
Decision Trees and Random Forests https://towardsdatascience.com/decision-trees-and-random-forests-74b89a374db
What is Web Scraping https://www.coriers.com/what-is-web-scraping/
p-value https://www.analyticsvidhya.com/blog/2019/09/everything-know-about-p-value-from-scratch-data-science/ https://towardsdatascience.com/hypothesis-tests-and-p-value-a-gentle-introduction-4b52322bfc50
Survival Modeling https://towardsdatascience.com/survival-modeling-accelerated-failure-time-xgboost-971aaa1ba794 https://medium.com/data-science-bridge/random-forest-regression-ddfc88c92689
Introduction to Genetic Algorithm https://itnext.io/introduction-to-genetic-algorithm-ce03b5865dc0 https://medium.com/analytics-vidhya/understanding-genetic-algorithms-in-the-artificial-intelligence-spectrum-7021b7cc25e7
Ensemble https://www.kdnuggets.com/2019/09/ensemble-learning.html https://medium.com/hal24k-techblog/how-to-generate-neural-network-confidence-intervals-with-keras-e4c0b78ebbdf
GAN https://towardsdatascience.com/an-easy-introduction-to-generative-adversarial-networks-6f8498dc4bcd https://towardsdatascience.com/gans-vs-autoencoders-comparison-of-deep-generative-models-985cf15936ea https://towardsdatascience.com/generating-images-with-autoencoders-77fd3a8dd368 https://github.com/adjidieng/PresGANs
Churn Prediction and Prevention in Python https://towardsdatascience.com/churn-prediction-and-prevention-in-python-2d454e5fd9a5 https://towardsdatascience.com/customer-churn-classification-using-predictive-machine-learning-models-ab7ba165bf56
Data Vis http://cican17.com/data-visualization-with-python/ https://towardsdatascience.com/exploratory-data-analysis-tutorial-in-python-15602b417445
Python Tutorial https://docs.python.org/2.0/tut/tut.html
Normalizing https://towardsdatascience.com/scale-standardize-or-normalize-with-scikit-learn-6ccc7d176a02 https://towardsdatascience.com/outlier-detection-with-isolation-forest-3d190448d45e
LR https://www.kaggle.com/jeppbautista/logistic-regression-from-scratch-python https://medium.com/greyatom/logistic-regression-89e496433063 https://www.datacamp.com/community/tutorials/understanding-logistic-regression-python https://blog.hyperiondev.com/index.php/2019/02/18/machine-learning/ https://cntk.ai/pythondocs/CNTK_101_LogisticRegression.html https://github.com/philusnarh/PROJECT/issues/43#issue-475635418
GAN https://towardsdatascience.com/gans-vs-autoencoders-comparison-of-deep-generative-models-985cf15936ea
Dimensionality Reduction 101 for Dummies like Me https://towardsdatascience.com/dimensionality-reduction-101-for-dummies-like-me-abcfb2551794
Hypothesis tests with Python https://medium.com/analytics-vidhya/hypothesis-tests-with-python-bfff05955c43
How Machine Learning, Big Data & AI are Changing Energy https://rapidminer.com/blog/machine-learning-big-data-ai-energy/amp/
A Comprehensive Guide to Seaborn in Python https://www.analyticsvidhya.com/blog/2019/09/comprehensive-data-visualization-guide-seaborn-python/
P-value Explained Simply for Data Scientists https://towardsdatascience.com/p-value-explained-simply-for-data-scientists-4c0cd7044f14 https://towardsdatascience.com/what-is-a-p-value-b9e6c207247f
Everything you Should Know about p-value from Scratch for Data Science https://medium.com/analytics-vidhya/everything-you-should-know-about-p-value-from-scratch-for-data-science-f3c0bfa3c4cc
Visualizing SVM with Python https://medium.com/swlh/visualizing-svm-with-python-4b4b238a7a92
The Simple Math behind 3 Decision Tree Splitting criterions https://towardsdatascience.com/the-simple-math-behind-3-decision-tree-splitting-criterions-85d4de2a75fe
A brief introduction to reinforcement learning https://medium.com/free-code-camp/a-brief-introduction-to-reinforcement-learning-7799af5840db
Cross-Validation for Imbalanced Datasets https://medium.com/lumiata/cross-validation-for-imbalanced-datasets-9d203ba47e8
Get started with Bayesian Inference https://medium.com/@andreasherman/get-started-with-bayesian-inference-cec9ad4ccd55 https://machinelearningmastery.com/bayes-theorem-for-machine-learning/ Illustrated Guide to LSTM’s and GRU’s https://towardsdatascience.com/illustrated-guide-to-lstms-and-gru-s-a-step-by-step-explanation-44e9eb85bf21
Understanding Variational Autoencoders (VAEs) https://towardsdatascience.com/understanding-variational-autoencoders-vaes-f70510919f73
Outlier Detection with Hampel Filter https://towardsdatascience.com/outlier-detection-with-hampel-filter-85ddf523c73d
warnings https://machinelearningmastery.com/how-to-fix-futurewarning-messages-in-scikit-learn/
How to use a clustering technique for synthetic data generation https://towardsdatascience.com/how-to-use-a-clustering-technique-for-synthetic-data-generation-7c84b6b678ea
Exploring your data with just 1 line of Python https://towardsdatascience.com/exploring-your-data-with-just-1-line-of-python-4b35ce21a82d
Transfer Learning — part 1 https://medium.com/dataswati-garage/transfer-learning-part-1-c2f87de8df38
Dimensionality Reduction For Dummies — Part 1: Intuition https://towardsdatascience.com/https-medium-com-abdullatif-h-dimensionality-reduction-for-dummies-part-1-a8c9ec7b7e79
Making an image dataset from Youtube videos https://towardsdatascience.com/making-an-image-dataset-from-youtube-videos-5116252d20a3
Moment Generating Function Explained https://medium.com/@aerinykim/moment-generating-function-explained-27821a739035
Consuming Web APIs with Python https://itnext.io/consuming-web-apis-with-python-fa9b751d2c75
Multi-Label Image Classification with Neural Network https://towardsdatascience.com/multi-label-image-classification-with-neural-network-keras-ddc1ab1afede
Time Series Analysis with Pandas https://dev.to/kite/time-series-analysis-with-pandas-3472 https://towardsdatascience.com/an-overview-of-time-series-forecasting-models-a2fa7a358fcb
Scikit-Learn Design Principles https://towardsdatascience.com/scikit-learn-design-principles-d1371958059b
Building a Language Toxicity Classification Model https://towardsdatascience.com/building-a-language-toxicity-classification-model-b006ae6981a4
From Scratch: Bayesian Inference, Markov Chain Monte Carlo and Metropolis Hastings, in python https://towardsdatascience.com/from-scratch-bayesian-inference-markov-chain-monte-carlo-and-metropolis-hastings-in-python-ef21a29e25a https://towardsdatascience.com/a-zero-math-introduction-to-markov-chain-monte-carlo-methods-dcba889e0c50
What Is Balanced And Imbalanced Dataset? https://medium.com/analytics-vidhya/what-is-balance-and-imbalance-dataset-89e8d7f46bc5
Classify Toxic Online Comments with LSTM and GloVe https://towardsdatascience.com/classify-toxic-online-comments-with-lstm-and-glove-e455a58da9c7
How to Deploy Your Machine Learning Web App to Digital Ocean https://towardsdatascience.com/how-to-deploy-your-machine-learning-web-app-to-digital-ocean-64bd19ce15e2
Continuous Probability Distributions for Machine Learning https://machinelearningmastery.com/continuous-probability-distributions-for-machine-learning/
Reinforcement Learning Explained: Overview, Comparisons and Applications in Business https://medium.com/@AltexSoft/reinforcement-learning-explained-overview-comparisons-and-applications-in-business-7ecc8549a39a
What’s the Difference Between AI, ML, Deep Learning, and Active Learning? https://www.datanami.com/2019/09/17/whats-the-difference-between-ai-ml-deep-learning-and-active-learning/
A Comprehensive Guide to Pandas’ Advanced Features in 20 Minutes https://towardsdatascience.com/learn-advanced-features-for-pythons-main-data-analysis-library-in-20-minutes-d0eedd90d086
Understanding 1D and 3D Convolution Neural NetworkUnderstanding 1D and 3D Convolution Neural Network https://towardsdatascience.com/understanding-1d-and-3d-convolution-neural-network-keras-9d8f76e29610
Introduction to Monte Carlo Tree Search: https://www.analyticsvidhya.com/blog/2019/01/monte-carlo-tree-search-introduction-algorithm-deepmind-alphago/
Hierarchical Bayesian Models https://medium.com/@ODSC/hierarchical-bayesian-models-in-r-9a18e6acdf2b https://towardsdatascience.com/markov-chain-monte-carlo-in-python-44f7e609be98
Variational Autoencoders Explained in Detail http://anotherdatum.com/vae2.html
2D FT https://www.youtube.com/watch?v=YYGltoYEmKo https://www.youtube.com/watch?v=r18Gi8lSkfM
Math of SGB https://towardsdatascience.com/step-by-step-tutorial-on-linear-regression-with-stochastic-gradient-descent-1d35b088a843
gridsearchcv https://chrisalbon.com/machine_learning/model_selection/model_selection_using_grid_search/
XGBRegressor with GridSearchCV https://www.kaggle.com/jayatou/xgbregressor-with-gridsearchcv https://www.kaggle.com/jack89roberts/top-7-using-elasticnet-with-interactions https://medium.com/usf-msds/choosing-the-right-metric-for-machine-learning-models-part-1-a99d7d7414e4 https://chrisalbon.com/machine_learning/model_selection/model_selection_using_grid_search/ https://scikit-learn.org/stable/modules/model_evaluation.html https://scikit-learn.org/stable/supervised_learning.html https://scikit-learn.org/stable/modules/sgd.html#regression https://www.kaggle.com/residentmario/model-fit-metrics https://mlfromscratch.com/random-forest-gridsearchcv-python/#/ https://www.kaggle.com/residentmario/model-fit-metrics https://mode.com/blog/violin-plot-examples https://www.kdnuggets.com/2018/01/managing-machine-learning-workflows-scikit-learn-pipelines-part-3.html
Modifying Adam to use Nesterov Accelerated Gradients: Nesterov-accelerated Adaptive Moment Estimation https://medium.com/konvergen/modifying-adam-to-use-nesterov-accelerated-gradients-nesterov-accelerated-adaptive-moment-67154177e1fd
Curse of D https://www.visiondummy.com/2014/04/curse-dimensionality-affect-classification/
Why Use K-Means for Time Series Data? https://www.influxdata.com/blog/why-use-k-means-for-time-series-data-part-one/ https://medium.com/datadriveninvestor/dynamic-time-warping-dtw-d51d1a1e4afc
From scratch — An LSTM model to predict commodity prices https://medium.com/@vinayarun/from-scratch-an-lstm-model-to-predict-commodity-prices-179e12445c5a
How to write Web apps using simple Python https://towardsdatascience.com/how-to-write-web-apps-using-simple-python-for-data-scientists-a227a1a01582
Recurrent Neural Network to Predict Multivariate Commodity Prices https://towardsdatascience.com/recurrent-neural-network-to-predict-multivariate-commodity-prices-8a8202afd853
Intro to optimization in deep learning: Momentum, RMSProp and Adam https://blog.paperspace.com/intro-to-optimization-momentum-rmsprop-adam/amp/
Create your first Image Recognition Classifier using CNN, Keras https://medium.com/nybles/create-your-first-image-recognition-classifier-using-cnn-keras-and-tensorflow-backend-6eaab98d14dd
Introduction to anomaly detection in python https://blog.floydhub.com/introduction-to-anomaly-detection-in-python/amp/
Outlier Detection with Isolation ForestOutlier Detection with Isolation Forest https://towardsdatascience.com/outlier-detection-with-isolation-forest-3d190448d45e
Feature Extraction Techniques https://towardsdatascience.com/feature-extraction-techniques-d619b56e31be
Deep Learning: Predicting Customer Churn https://medium.com/swlh/building-an-artificial-neural-network-in-less-than-10-minutes-cbe59dbb903c
NLP basics, hands-on: a language classifier https://towardsdatascience.com/nlp-basics-hands-on-a-portuguese-dialect-classifier-deployed-online-in-3-steps-53a8b3b88ea9
Image classification with Keras and deep learning https://www.pyimagesearch.com/2017/12/11/image-classification-with-keras-and-deep-learning/
Feature Selection and Dimensionality Reduction Using Covariance Matrix Plot https://medium.com/towards-artificial-intelligence/feature-selection-and-dimensionality-reduction-using-covariance-matrix-plot-b4c7498abd07
Maximum Likelihood Estimation VS Maximum A Posterior https://towardsdatascience.com/mle-vs-map-a989f423ae5c
How Did Linear Discriminant Analysis Get It’s Name https://towardsdatascience.com/mathematical-insights-into-classification-using-linear-discriminant-analysis-9c822ad2fce2
Introduction to Classification Algorithms https://dzone.com/articles/introduction-to-classification-algorithms
How to Implement Bayesian Optimization from Scratch https://machinelearningmastery.com/what-is-bayesian-optimization/ https://towardsdatascience.com/intro-to-bayesian-statistics-5056b43d248d
Speech recognition using artificial neural networks and artificial bee colony optimization https://m.techxplore.com/news/2019-10-speech-recognition-artificial-neural-networks.html
A Comprehensive Guide to Neural Networks for Beginners https://towardsdatascience.com/a-comprehensive-guide-on-neural-networks-for-beginners-a4ca07cee1b7
Curse of Dimensionality https://towardsdatascience.com/the-curse-of-dimensionality-50dc6e49aa1e https://www.datacamp.com/community/tutorials/principal-component-analysis-in-python
NLP Applications Sarcasm Classification:: https://towardsdatascience.com/sarcasm-classification-using-fasttext-788ffbacb77b
Stock Price modelling
Using the Vector Error Correction Model to predict FANG Stock Prices:: https://towardsdatascience.com/predicting-fang-stock-prices-ecac4ddd27c1
*Decorators in Py https://towardsdatascience.com/10-fabulous-python-decorators-ab674a732871
philusnarh
commented
3 years ago philusnarh
commented
3 years ago
https://jakevdp.github.io/PythonDataScienceHandbook/04.08-multiple-subplots.html
Linear Regression Derivation https://towardsdatascience.com/linear-regression-derivation-d362ea3884c2
ASS https://launchpad.net/apsynsim
Juares' Work https://towardsdatascience.com/principal-component-analysis-for-dimensionality-reduction-115a3d157bad
Machine Learning Background Necessary for Deep Learning https://towardsdatascience.com/machine-learning-necessary-for-deep-learning-2095a345ec2c
Journal https://www.sciencedirect.com/journal/artificial-intelligence/vol/279/suppl/C https://www.springer.com/journal/12036 https://www.springer.com/gp/astronomy/astronomy-astrophysics-cosmology https://academic.oup.com/journals/pages/science_and_mathematics
ROC https://towardsdatascience.com/understanding-auc-roc-curve-68b2303cc9c5
PCA https://towardsdatascience.com/principal-component-analysis-for-dimensionality-reduction-115a3d157bad https://towardsdatascience.com/pca-vs-tsne-el-cl%C3%A1sico-9948181a5f87 https://jakevdp.github.io/PythonDataScienceHandbook/05.09-principal-component-analysis.html https://towardsdatascience.com/dimensionality-reduction-does-pca-really-improve-classification-outcome-6e9ba21f0a32 https://towardsdatascience.com/principal-component-analysis-for-dimensionality-reduction-115a3d157bad
feature extraction https://towardsdatascience.com/feature-extraction-techniques-d619b56e31be
General Lecture https://www.ritchieng.com/machine-learning/journals-library/ https://machine-learning-course.readthedocs.io/en/latest/content/deep_learning/autoencoder.html
Convolution cat & dog demo https://towardsdatascience.com/image-classifier-cats-vs-dogs-with-convolutional-neural-networks-cnns-and-google-colabs-4e9af21ae7a8 https://www.kaggle.com/c/dogs-vs-cats/data https://blog.keras.io/building-powerful-image-classification-models-using-very-little-data.html
GMM credit card https://www.kaggle.com/albertmistu/detect-anomalies-using-gmm
GMM Defn https://www.kaggle.com/dfoly1/gaussian-mixture-model/notebook https://github.com/PhilippeW83440/Coursera-AML3-Bayesian-Methods/blob/master/Coursera-BMML_-week-2.ipynb
pneumonia https://appliedmachinelearning.blog/2019/07/29/transfer-learning-using-feature-extraction-from-trained-models-food-images-classification/
https://medium.com/towards-artificial-intelligence/an-introduction-to-dropout-for-regularizing-deep-neural-networks-4e0826c10395 https://towardsdatascience.com/applied-deep-learning-part-1-artificial-neural-networks-d7834f67a4f6 https://towardsdatascience.com/introduction-to-artificial-neural-networks-ann-1aea15775ef9
Pipe https://scikit-learn.org/stable/tutorial/statistical_inference/putting_together.html LSTM https://stackabuse.com/time-series-analysis-with-lstm-using-pythons-keras-library/ https://scikit-learn.org/stable/supervised_learning.html https://www.kdnuggets.com/2019/05/machine-learning-time-series-forecasting.html
KL Divergence
https://bigdatascientistblog.wordpress.com/2017/09/11/a-simple-introduction-to-kullback-leibler-divergence-through-python-code/
https://machinelearningmastery.com/when-to-use-mlp-cnn-and-rnn-neural-networks/
lectures ml https://theclevermachine.wordpress.com/tag/backpropagation/ https://sebastianraschka.com/resources/dl-lectures.html http://www.cs.cmu.edu/~ninamf/courses/401sp18/lectures.shtml
An elegant way to represent forward propagation and backpropagation in a neural network https://www.datasciencecentral.com/profiles/blogs/an-elegant-way-to-represent-forward-propagation-and-back
Python args and kwargs https://medium.com/swlh/python-args-and-kwargs-a82b6480f287
Image download https://www.pyimagesearch.com/2017/12/04/how-to-create-a-deep-learning-dataset-using-google-images/ A single function to streamline image classification with Keras https://towardsdatascience.com/a-single-function-to-streamline-image-classification-with-keras-bd04f5cfe6df
The Advantages of DenseNet https://medium.com/the-advantages-of-densenet/the-advantages-of-densenet-98de3019cdac
Discrete Probability Distributions for Machine Learning https://machinelearningmastery.com/discrete-probability-distributions-for-machine-learning/
Beginners Guide to Convolutional Neural Networks https://towardsdatascience.com/beginners-guide-to-understanding-convolutional-neural-networks-ae9ed58bb17d
Visualizing 100,000 Amazon Products https://towardsdatascience.com/vis-amz-83dea6fcb059
GMM https://towardsdatascience.com/how-to-code-gaussian-mixture-models-from-scratch-in-python-9e7975df5252 https://towardsdatascience.com/medical-image-analysis-with-deep-learning-9557cad44944 https://towardsdatascience.com/maximum-likelihood-estimation-from-bayes-theorem-6cc7f0db9adb https://machinelearningmastery.com/bayes-theorem-for-machine-learning/
Decision Trees and Random Forests https://towardsdatascience.com/decision-trees-and-random-forests-74b89a374db
What is Web Scraping https://www.coriers.com/what-is-web-scraping/
p-value https://www.analyticsvidhya.com/blog/2019/09/everything-know-about-p-value-from-scratch-data-science/ https://towardsdatascience.com/hypothesis-tests-and-p-value-a-gentle-introduction-4b52322bfc50
Survival Modeling https://towardsdatascience.com/survival-modeling-accelerated-failure-time-xgboost-971aaa1ba794 https://medium.com/data-science-bridge/random-forest-regression-ddfc88c92689
Introduction to Genetic Algorithm https://itnext.io/introduction-to-genetic-algorithm-ce03b5865dc0 https://medium.com/analytics-vidhya/understanding-genetic-algorithms-in-the-artificial-intelligence-spectrum-7021b7cc25e7
Ensemble https://www.kdnuggets.com/2019/09/ensemble-learning.html https://medium.com/hal24k-techblog/how-to-generate-neural-network-confidence-intervals-with-keras-e4c0b78ebbdf
GAN https://towardsdatascience.com/an-easy-introduction-to-generative-adversarial-networks-6f8498dc4bcd https://towardsdatascience.com/gans-vs-autoencoders-comparison-of-deep-generative-models-985cf15936ea https://towardsdatascience.com/generating-images-with-autoencoders-77fd3a8dd368 https://github.com/adjidieng/PresGANs
Churn Prediction and Prevention in Python https://towardsdatascience.com/churn-prediction-and-prevention-in-python-2d454e5fd9a5 https://towardsdatascience.com/customer-churn-classification-using-predictive-machine-learning-models-ab7ba165bf56
Data Vis http://cican17.com/data-visualization-with-python/ https://towardsdatascience.com/exploratory-data-analysis-tutorial-in-python-15602b417445
Python Tutorial https://docs.python.org/2.0/tut/tut.html
Normalizing https://towardsdatascience.com/scale-standardize-or-normalize-with-scikit-learn-6ccc7d176a02 https://towardsdatascience.com/outlier-detection-with-isolation-forest-3d190448d45e
LR https://www.kaggle.com/jeppbautista/logistic-regression-from-scratch-python https://medium.com/greyatom/logistic-regression-89e496433063 https://www.datacamp.com/community/tutorials/understanding-logistic-regression-python https://blog.hyperiondev.com/index.php/2019/02/18/machine-learning/ https://cntk.ai/pythondocs/CNTK_101_LogisticRegression.html https://github.com/philusnarh/PROJECT/issues/43#issue-475635418
GAN https://towardsdatascience.com/gans-vs-autoencoders-comparison-of-deep-generative-models-985cf15936ea
Dimensionality Reduction 101 for Dummies like Me https://towardsdatascience.com/dimensionality-reduction-101-for-dummies-like-me-abcfb2551794
Hypothesis tests with Python https://medium.com/analytics-vidhya/hypothesis-tests-with-python-bfff05955c43
How Machine Learning, Big Data & AI are Changing Energy https://rapidminer.com/blog/machine-learning-big-data-ai-energy/amp/
A Comprehensive Guide to Seaborn in Python https://www.analyticsvidhya.com/blog/2019/09/comprehensive-data-visualization-guide-seaborn-python/
P-value Explained Simply for Data Scientists https://towardsdatascience.com/p-value-explained-simply-for-data-scientists-4c0cd7044f14 https://towardsdatascience.com/what-is-a-p-value-b9e6c207247f
Everything you Should Know about p-value from Scratch for Data Science https://medium.com/analytics-vidhya/everything-you-should-know-about-p-value-from-scratch-for-data-science-f3c0bfa3c4cc
Visualizing SVM with Python https://medium.com/swlh/visualizing-svm-with-python-4b4b238a7a92
The Simple Math behind 3 Decision Tree Splitting criterions https://towardsdatascience.com/the-simple-math-behind-3-decision-tree-splitting-criterions-85d4de2a75fe
A brief introduction to reinforcement learning https://medium.com/free-code-camp/a-brief-introduction-to-reinforcement-learning-7799af5840db
Cross-Validation for Imbalanced Datasets https://medium.com/lumiata/cross-validation-for-imbalanced-datasets-9d203ba47e8
Get started with Bayesian Inference https://medium.com/@andreasherman/get-started-with-bayesian-inference-cec9ad4ccd55 https://machinelearningmastery.com/bayes-theorem-for-machine-learning/ Illustrated Guide to LSTM’s and GRU’s https://towardsdatascience.com/illustrated-guide-to-lstms-and-gru-s-a-step-by-step-explanation-44e9eb85bf21
Understanding Variational Autoencoders (VAEs) https://towardsdatascience.com/understanding-variational-autoencoders-vaes-f70510919f73
Outlier Detection with Hampel Filter https://towardsdatascience.com/outlier-detection-with-hampel-filter-85ddf523c73d
warnings https://machinelearningmastery.com/how-to-fix-futurewarning-messages-in-scikit-learn/
How to use a clustering technique for synthetic data generation https://towardsdatascience.com/how-to-use-a-clustering-technique-for-synthetic-data-generation-7c84b6b678ea
Exploring your data with just 1 line of Python https://towardsdatascience.com/exploring-your-data-with-just-1-line-of-python-4b35ce21a82d
Transfer Learning — part 1 https://medium.com/dataswati-garage/transfer-learning-part-1-c2f87de8df38
Dimensionality Reduction For Dummies — Part 1: Intuition https://towardsdatascience.com/https-medium-com-abdullatif-h-dimensionality-reduction-for-dummies-part-1-a8c9ec7b7e79
Making an image dataset from Youtube videos https://towardsdatascience.com/making-an-image-dataset-from-youtube-videos-5116252d20a3
Moment Generating Function Explained https://medium.com/@aerinykim/moment-generating-function-explained-27821a739035
Consuming Web APIs with Python https://itnext.io/consuming-web-apis-with-python-fa9b751d2c75
Multi-Label Image Classification with Neural Network https://towardsdatascience.com/multi-label-image-classification-with-neural-network-keras-ddc1ab1afede
Time Series Analysis with Pandas https://dev.to/kite/time-series-analysis-with-pandas-3472 https://towardsdatascience.com/an-overview-of-time-series-forecasting-models-a2fa7a358fcb
Scikit-Learn Design Principles https://towardsdatascience.com/scikit-learn-design-principles-d1371958059b
Building a Language Toxicity Classification Model https://towardsdatascience.com/building-a-language-toxicity-classification-model-b006ae6981a4
From Scratch: Bayesian Inference, Markov Chain Monte Carlo and Metropolis Hastings, in python https://towardsdatascience.com/from-scratch-bayesian-inference-markov-chain-monte-carlo-and-metropolis-hastings-in-python-ef21a29e25a https://towardsdatascience.com/a-zero-math-introduction-to-markov-chain-monte-carlo-methods-dcba889e0c50
What Is Balanced And Imbalanced Dataset? https://medium.com/analytics-vidhya/what-is-balance-and-imbalance-dataset-89e8d7f46bc5
Classify Toxic Online Comments with LSTM and GloVe https://towardsdatascience.com/classify-toxic-online-comments-with-lstm-and-glove-e455a58da9c7
How to Deploy Your Machine Learning Web App to Digital Ocean https://towardsdatascience.com/how-to-deploy-your-machine-learning-web-app-to-digital-ocean-64bd19ce15e2
Discrete Probability Distributions for Machine Learning https://machinelearningmastery.com/discrete-probability-distributions-for-machine-learning/
Continuous Probability Distributions for Machine Learning https://machinelearningmastery.com/continuous-probability-distributions-for-machine-learning/
Reinforcement Learning Explained: Overview, Comparisons and Applications in Business https://medium.com/@AltexSoft/reinforcement-learning-explained-overview-comparisons-and-applications-in-business-7ecc8549a39a
What’s the Difference Between AI, ML, Deep Learning, and Active Learning? https://www.datanami.com/2019/09/17/whats-the-difference-between-ai-ml-deep-learning-and-active-learning/
A Comprehensive Guide to Pandas’ Advanced Features in 20 Minutes https://towardsdatascience.com/learn-advanced-features-for-pythons-main-data-analysis-library-in-20-minutes-d0eedd90d086
Understanding 1D and 3D Convolution Neural NetworkUnderstanding 1D and 3D Convolution Neural Network https://towardsdatascience.com/understanding-1d-and-3d-convolution-neural-network-keras-9d8f76e29610
Introduction to Monte Carlo Tree Search: https://www.analyticsvidhya.com/blog/2019/01/monte-carlo-tree-search-introduction-algorithm-deepmind-alphago/
Hierarchical Bayesian Models https://medium.com/@ODSC/hierarchical-bayesian-models-in-r-9a18e6acdf2b https://towardsdatascience.com/markov-chain-monte-carlo-in-python-44f7e609be98
Variational Autoencoders Explained in Detail http://anotherdatum.com/vae2.html
2D FT https://www.youtube.com/watch?v=YYGltoYEmKo https://www.youtube.com/watch?v=r18Gi8lSkfM
Math of SGB https://towardsdatascience.com/step-by-step-tutorial-on-linear-regression-with-stochastic-gradient-descent-1d35b088a843
gridsearchcv https://chrisalbon.com/machine_learning/model_selection/model_selection_using_grid_search/
XGBRegressor with GridSearchCV https://www.kaggle.com/jayatou/xgbregressor-with-gridsearchcv https://www.kaggle.com/jack89roberts/top-7-using-elasticnet-with-interactions https://medium.com/usf-msds/choosing-the-right-metric-for-machine-learning-models-part-1-a99d7d7414e4 https://chrisalbon.com/machine_learning/model_selection/model_selection_using_grid_search/ https://scikit-learn.org/stable/modules/model_evaluation.html https://scikit-learn.org/stable/supervised_learning.html https://scikit-learn.org/stable/modules/sgd.html#regression https://www.kaggle.com/residentmario/model-fit-metrics https://mlfromscratch.com/random-forest-gridsearchcv-python/#/ https://www.kaggle.com/residentmario/model-fit-metrics https://mode.com/blog/violin-plot-examples https://www.kdnuggets.com/2018/01/managing-machine-learning-workflows-scikit-learn-pipelines-part-3.html
Modifying Adam to use Nesterov Accelerated Gradients: Nesterov-accelerated Adaptive Moment Estimation https://medium.com/konvergen/modifying-adam-to-use-nesterov-accelerated-gradients-nesterov-accelerated-adaptive-moment-67154177e1fd
Curse of D https://www.visiondummy.com/2014/04/curse-dimensionality-affect-classification/
Why Use K-Means for Time Series Data? https://www.influxdata.com/blog/why-use-k-means-for-time-series-data-part-one/ https://medium.com/datadriveninvestor/dynamic-time-warping-dtw-d51d1a1e4afc
From scratch — An LSTM model to predict commodity prices https://medium.com/@vinayarun/from-scratch-an-lstm-model-to-predict-commodity-prices-179e12445c5a
How to write Web apps using simple Python https://towardsdatascience.com/how-to-write-web-apps-using-simple-python-for-data-scientists-a227a1a01582
Recurrent Neural Network to Predict Multivariate Commodity Prices https://towardsdatascience.com/recurrent-neural-network-to-predict-multivariate-commodity-prices-8a8202afd853
Intro to optimization in deep learning: Momentum, RMSProp and Adam https://blog.paperspace.com/intro-to-optimization-momentum-rmsprop-adam/amp/
Create your first Image Recognition Classifier using CNN, Keras https://medium.com/nybles/create-your-first-image-recognition-classifier-using-cnn-keras-and-tensorflow-backend-6eaab98d14dd
Introduction to anomaly detection in python https://blog.floydhub.com/introduction-to-anomaly-detection-in-python/amp/
Outlier Detection with Isolation ForestOutlier Detection with Isolation Forest https://towardsdatascience.com/outlier-detection-with-isolation-forest-3d190448d45e
Feature Extraction Techniques https://towardsdatascience.com/feature-extraction-techniques-d619b56e31be
Deep Learning: Predicting Customer Churn https://medium.com/swlh/building-an-artificial-neural-network-in-less-than-10-minutes-cbe59dbb903c
NLP basics, hands-on: a language classifier https://towardsdatascience.com/nlp-basics-hands-on-a-portuguese-dialect-classifier-deployed-online-in-3-steps-53a8b3b88ea9
Image classification with Keras and deep learning https://www.pyimagesearch.com/2017/12/11/image-classification-with-keras-and-deep-learning/
Feature Selection and Dimensionality Reduction Using Covariance Matrix Plot https://medium.com/towards-artificial-intelligence/feature-selection-and-dimensionality-reduction-using-covariance-matrix-plot-b4c7498abd07
Maximum Likelihood Estimation VS Maximum A Posterior https://towardsdatascience.com/mle-vs-map-a989f423ae5c
How Did Linear Discriminant Analysis Get It’s Name https://towardsdatascience.com/mathematical-insights-into-classification-using-linear-discriminant-analysis-9c822ad2fce2
Introduction to Classification Algorithms https://dzone.com/articles/introduction-to-classification-algorithms
How to Implement Bayesian Optimization from Scratch https://machinelearningmastery.com/what-is-bayesian-optimization/ https://towardsdatascience.com/intro-to-bayesian-statistics-5056b43d248d
Speech recognition using artificial neural networks and artificial bee colony optimization https://m.techxplore.com/news/2019-10-speech-recognition-artificial-neural-networks.html
A Comprehensive Guide to Neural Networks for Beginners https://towardsdatascience.com/a-comprehensive-guide-on-neural-networks-for-beginners-a4ca07cee1b7
Curse of Dimensionality https://towardsdatascience.com/the-curse-of-dimensionality-50dc6e49aa1e https://www.datacamp.com/community/tutorials/principal-component-analysis-in-python
NLP Applications Sarcasm Classification:: https://towardsdatascience.com/sarcasm-classification-using-fasttext-788ffbacb77b
Stock Price modelling
Using the Vector Error Correction Model to predict FANG Stock Prices:: https://towardsdatascience.com/predicting-fang-stock-prices-ecac4ddd27c1