#nn #dl #keras #modeling #demystifying #guide
https://www.pyimagesearch.com/2019/10/28/3-ways-to-create-a-keras-model-with-tensorflow-2-0-sequential-functional-and-model-subclassing
https://www.pyimagesearch.com/2019/10/28/3-ways-to-create-a-keras-model-with-tensorflow-2-0-sequential-functional-and-model-subclassing
PyImageSearch
3 ways to create a Keras model with TensorFlow 2.0 (Sequential, Functional, and Model Subclassing) - PyImageSearch
Keras and TensorFlow 2.0 provide you with three methods to implement your own neural network architectures:, Sequential API, Functional API, and Model subclassing. Inside of this tutorial youโll learn how to utilize each of these methods, including how toโฆ
#ml #r #modeling #cross_validation #letter
http://www.win-vector.com/blog/2019/11/when-cross-validation-is-more-powerful-than-regularization/
http://www.win-vector.com/blog/2019/11/when-cross-validation-is-more-powerful-than-regularization/
Win-Vector Blog
When Cross-Validation is More Powerful than Regularization
Regularization is a way of avoiding overfit by restricting the magnitude of model coefficients (or in deep learning, node weights). A simple example of regularization is the use of ridge or lasso rโฆ
#ml #r #modeling #cross_validation #letter #guide
http://www.win-vector.com/blog/2015/09/isyourmodelgoingtowork/
http://www.win-vector.com/blog/2015/09/isyourmodelgoingtowork/
Win-Vector Blog
How do you know if your model is going to work?
Authors: John Mount (more articles) and Nina Zumel (more articles). Our four part article series collected into one piece. Part 1: The problem Part 2: In-training set measures Part 3: Out of sampleโฆ
#nn #dl #modeling
https://towardsdatascience.com/weight-initialization-in-neural-networks-a-journey-from-the-basics-to-kaiming-954fb9b47c79
https://towardsdatascience.com/weight-initialization-in-neural-networks-a-journey-from-the-basics-to-kaiming-954fb9b47c79
Medium
Weight Initialization in Neural Networks: A Journey From the Basics to Kaiming
Exploring the evolution of initializing layer weights in neural networks: from old-school to Xavier, and arriving finally at Kaiming init.
#ml #modeling #feature_selection #guide
https://towardsdatascience.com/best-bulletproof-python-feature-selection-methods-every-data-scientist-should-know-7c1027a833c6
https://towardsdatascience.com/best-bulletproof-python-feature-selection-methods-every-data-scientist-should-know-7c1027a833c6
Medium
Best Bulletproof Python Feature Selection Methods Every Data Scientist Should Know
The 5 Best Feature Selection Methods in few lines of codes
#ml #imbalanced #feature_selection #modeling
https://machinelearningmastery.com/threshold-moving-for-imbalanced-classification/
https://machinelearningmastery.com/threshold-moving-for-imbalanced-classification/
MachineLearningMastery.com
A Gentle Introduction to Threshold-Moving for Imbalanced Classification - MachineLearningMastery.com
Classification predictive modeling typically involves predicting a class label. Nevertheless, many machine learning algorithms are capable of predicting a probability or scoring of class membership, and this must be interpreted before it can be mapped toโฆ
#ml #time_series #random_forest #modeling #guide
https://machinelearningmastery.com/random-forest-for-time-series-forecasting/
https://machinelearningmastery.com/random-forest-for-time-series-forecasting/
MachineLearningMastery.com
Random Forest for Time Series Forecasting - MachineLearningMastery.com
Random Forest is a popular and effective ensemble machine learning algorithm. It is widely used for classification and regression predictive modeling problems with structured (tabular) data sets, e.g. data as it looks in a spreadsheet or database table. Randomโฆ
#ml #tutorial #eda #modeling #evaluation
https://towardsdatascience.com/practical-machine-learning-tutorial-part-1-data-exploratory-analysis-c13d39b8f33b
https://towardsdatascience.com/practical-machine-learning-tutorial-part-2-build-model-validate-c98c2ddad744
https://towardsdatascience.com/practical-machine-learning-tutorial-part-3-model-evaluation-1-5eefae18ec98
https://towardsdatascience.com/practical-machine-learning-tutorial-part-4-model-evaluation-2-764d69f792a5
https://towardsdatascience.com/practical-machine-learning-tutorial-part-1-data-exploratory-analysis-c13d39b8f33b
https://towardsdatascience.com/practical-machine-learning-tutorial-part-2-build-model-validate-c98c2ddad744
https://towardsdatascience.com/practical-machine-learning-tutorial-part-3-model-evaluation-1-5eefae18ec98
https://towardsdatascience.com/practical-machine-learning-tutorial-part-4-model-evaluation-2-764d69f792a5
Medium
Practical Machine Learning Tutorial: Part.1 (Data Exploratory Analysis)
Multi-class Classification Problem: Geoscience example (Facies)
#ml #cross_validation #modeling #methods #demystifying
https://neptune.ai/blog/cross-validation-in-machine-learning-how-to-do-it-right
https://neptune.ai/blog/cross-validation-in-machine-learning-how-to-do-it-right
neptune.ai
Cross-Validation in Machine Learning: How to Do It Right
Explore the nuances of cross-validation: from k-Fold to time-series methods, with best practices for ML and Deep Learning.
#ml #modeling #performance #tool #nannyml #guide
https://towardsdatascience.com/predict-your-models-performance-without-waiting-for-the-control-group-3f5c9363a7da
https://towardsdatascience.com/predict-your-models-performance-without-waiting-for-the-control-group-3f5c9363a7da
Medium
Predict Your Modelโs Performance (Without Waiting for the Control Group)
A novel algorithm by NannyML allows estimating the performance of an ML model before the ground truth is available. Here is how it works.