GitHub - pranavchandran/Begintocodewithpython.github.io: Begin to Code with Python book website

Machine Learning

Study Materials for -Linear and Regression-Basics https://www.superdatascience.com/blogs/the-ultimate-guide-to-regression-classification

For Practice - UCI Machine learning.All type of Datasets for practice https://archive.ics.uci.edu/ml/index.php https://archive.ics.uci.edu/ml/datasets/Combined+Cycle+Power+Plant

Books Collection More about Neural networks and Deep Learning Michale Nileson

http://neuralnetworksanddeeplearning.com/chap1.html

ANN-Pooling refer: scs.reyserson.ca/

God father of AI:- Jeffery Hinton-videos

https://olympus.greatlearning.in/courses/10902

Machine Learning 10 hour class youtube https://www.youtube.com/watch?v=URLwFGwCBb0

Anaconda Steps

https://www.digitalocean.com/community/tutorials/how-to-install-anaconda-on-ubuntu-18-04-quickstart

Refer CNN for good reading https://www.superdatascience.com/blogs/the-ultimate-guide-to-convolutional-neural-networks-cnn

for image dataprocessing keras help to identify in dog or cat case https://blog.keras.io/building-powerful-image-classification-models-using-very-little-data.html

Sunspring movie - RNN details by Benjamin

deep learning reference http://karpathy.github.io/

http://karpathy.github.io/2015/05/21/rnn-effectiveness/

Study More about self organising maps(SOM) Reference https://sci2s.ugr.es/keel/pdf/algorithm/articulo/1990-Kohonen-PIEEE.pdf

Kohonens self organizing feature maps http://www.ai-junkie.com/ann/som/som1.html

D3.js is a JavaScript library for manipulating documents based on data. D3 helps you bring data to life using HTML, SVG, and CSS. D3’s emphasis on web standards gives you the full capabilities of modern browsers without tying yourself to a proprietary framework, combining powerful visualization components and a data-driven approach to DOM manipulation. https://d3js.org/

Visulaizing and creative coding JS https://bl.ocks.org/

http://localhost/wordpress/wp-admin/

https://www.superdatascience.com/pages/deep-learning

Name	Name	Last commit message	Last commit date
Latest commit History 136 Commits 136 Commits
Artificial_Neural_Network.ipynb	Artificial_Neural_Network.ipynb
BONUS_multiple_linear_regression.ipynb	BONUS_multiple_linear_regression.ipynb
BONUS_natural_language_processing.ipynb	BONUS_natural_language_processing.ipynb
Begin to code Python MTA 98-381 Exam Prep.pdf	Begin to code Python MTA 98-381 Exam Prep.pdf
CNAME	CNAME
Classification_Pros_Cons.pdf	Classification_Pros_Cons.pdf
Clustering-Pros-Cons.pdf	Clustering-Pros-Cons.pdf
Copy_of_convolutional_neural_network.ipynb	Copy_of_convolutional_neural_network.ipynb
Copy_of_data_preprocessing_tools.ipynb	Copy_of_data_preprocessing_tools.ipynb
Copy_of_decision_tree_regression.ipynb	Copy_of_decision_tree_regression.ipynb
Copy_of_multiple_linear_regression.ipynb	Copy_of_multiple_linear_regression.ipynb
Copy_of_polynomial_regression.ipynb	Copy_of_polynomial_regression.ipynb
Copy_of_random_forest_regression.ipynb	Copy_of_random_forest_regression.ipynb
Copy_of_support_vector_regression.ipynb	Copy_of_support_vector_regression.ipynb
DD4_classes_are_callables.ipynb	DD4_classes_are_callables.ipynb
Logistic_Regression.ipynb	Logistic_Regression.ipynb
Machine_Learning_A_Z_Q_A.pdf	Machine_Learning_A_Z_Q_A.pdf
README.md	README.md
Some imp Notes	Some imp Notes
apriori.ipynb	apriori.ipynb
artificial_neural_network.ipynb	artificial_neural_network.ipynb
convolutional_neural_network.ipynb	convolutional_neural_network.ipynb
decision_tree_classification.ipynb	decision_tree_classification.ipynb
decision_tree_classification_last.ipynb	decision_tree_classification_last.ipynb
decision_tree_regression.ipynb	decision_tree_regression.ipynb
eclat.ipynb	eclat.ipynb
grid_search.ipynb	grid_search.ipynb
hierarchical_clustering.ipynb	hierarchical_clustering.ipynb
index.html	index.html
k_fold_cross_validation.ipynb	k_fold_cross_validation.ipynb
k_means_clustering.ipynb	k_means_clustering.ipynb
k_nearest_neighbors.ipynb	k_nearest_neighbors.ipynb
kernel_pca.ipynb	kernel_pca.ipynb
kernel_svm.ipynb	kernel_svm.ipynb
kernel_svm_last.ipynb	kernel_svm_last.ipynb
linear_discriminant_analysis.ipynb	linear_discriminant_analysis.ipynb
logistic_regression.ipynb	logistic_regression.ipynb
logistic_regression_last_stage.ipynb	logistic_regression_last_stage.ipynb
multiple_linear_regression.ipynb	multiple_linear_regression.ipynb
naive_bayes.ipynb	naive_bayes.ipynb
naive_bayes_last.ipynb	naive_bayes_last.ipynb
natural_language_processing.ipynb	natural_language_processing.ipynb
polynomial_regression.ipynb	polynomial_regression.ipynb
principal_component_analysis.ipynb	principal_component_analysis.ipynb
random_forest_classification.ipynb	random_forest_classification.ipynb
random_forest_classification_last.ipynb	random_forest_classification_last.ipynb
random_forest_classification_last_classes.ipynb	random_forest_classification_last_classes.ipynb
random_forest_regression.ipynb	random_forest_regression.ipynb
simple_linear_regression.ipynb	simple_linear_regression.ipynb
styles.css	styles.css
support_vector_machine.ipynb	support_vector_machine.ipynb
support_vector_machine_last.ipynb	support_vector_machine_last.ipynb
support_vector_regression.ipynb	support_vector_regression.ipynb
thompson_sampling.ipynb	thompson_sampling.ipynb
tools.html	tools.html
upper_confidence_bound.ipynb	upper_confidence_bound.ipynb
xg_boost.ipynb	xg_boost.ipynb

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