Machine-Learning
- I would like to gather some useful material on machine learning which i study and use in my Possible projects here.
Index:
Useful Links
☛ The 7 Steps of Machine Learning
☛ Unsolved Machine Learning Problems That You Can Solve; by James Fletcher
☛ Interpretable Machine Learning. A Guide for Making Black Box Models Explainable. (Christoph Molnar)
☛ Companion webpage to the book "Mathematics for Machine Learning"
☛ Overfitting in Machine Learning: What It Is and How to Prevent It
☛ Fundamental Techniques of Feature Engineering for Machine Learning
☛ End-to-end Machine Learning project on predicting housing prices using Regression, by Gurupratap Singh Matharu
☛ Stanford Machine Learning Group
☛ example code and solutions to the exercises in the second edition of Hands on Machine Learning with Scikit-Learn, Keras and TensorFlow
Papers
- Papers with code: a free and open resource with Machine Learning papers.
- When Does Label Smoothing Help?
Statistical topics
- The Bias-Variance Dilemma; by Ra´ul Rojas
Feature Engineering
We must learn how to engineer features and build more powerful machine learning models.
- Feature Engineering and Feature Selection
- Effective Feature Engineering
- Advanced Feature Engineering Tutorial with Titanic
Main TextBooks
Boltzmann Machines
✵ Boltzmann Machines; By Geoffrey E. Hinton (University of Toronto)
✵ Boltzmann Machines | Transformation of Unsupervised Deep Learning; By Random Nerds
Datasets
- MNIST dataset
- COCO; Common Objects in Context