fucking-machine-learning-tutorials

Machine learning and deep learning tutorials, articles and other resources. With repository stars⭐ and forks🍴
https://github.com/correia-jpv/fucking-machine-learning-tutorials

Last synced: 11 days ago
JSON representation

Introduction
- Statistical Machine Learning Course
Kaggle Competitions WriteUp
Linear Regression
- Assumptions of Linear Regression - is-a-complete-list-of-the-usual-assumptions-for-linear-regression)
- Applying and Interpreting Linear Regression
- Interpreting plot.lm() in R
Logistic Regression
- Geometric Intuition of Logistic Regression
- Guide to an in-depth understanding of logistic regression
Model Validation using Resampling
- Resampling Explained
- Cross Validation
- How to use cross-validation in predictive modeling
- Good Resources
- Preventing Overfitting the Cross Validation Data | Andrew Ng
- Over-fitting in Model Selection and Subsequent Selection Bias in Performance Evaluation
- CV for detecting and preventing Overfitting
- Bootstrapping
- Example of Bootstapping
- Partioning data set in R
- Implementing hold-out Validaion in R - data-frame-into-testing-and.html)
- Training with Full dataset after CV?
- Which CV method is best?
- Variance Estimates in k-fold CV
- Is CV a subsitute for Validation Set?
- Choice of k in k-fold CV
- CV for ensemble learning
- k-fold CV in R
- How does CV overcome the Overfitting Problem
- Why Bootstrapping Works?
- Understanding Bootstapping for Validation and Model Selection
- Cross Validation vs Bootstrap to estimate prediction error - validation vs .632 bootstrapping to evaluate classification performance](http://stats.stackexchange.com/questions/71184/cross-validation-or-bootstrapping-to-evaluate-classification-performance)
Natural Language Processing
- A curated list of speech and natural language processing resources
- tf-idf explained
- Graph Based Semi Supervised Learning for NLP
- Classification text with Bag of Words
- **Probabilistic Topic Models Princeton PDF**
- **Introduction to LDA** - boyd-graber-and-philip-resnik.html)
- Online LDA - online-latent-dirichlet-allocation-with-apache-spark.html)
- LDA in Scala - dirichlet-allocation-in-scala-part-ii-the-code.html)
- Topic Modeling of Twitter Followers
- Multilingual Latent Dirichlet Allocation (LDA) - Latent-Dirichlet-Allocation-LDA/blob/master/Multilingual-LDA-Pipeline-Tutorial.ipynb)))
- Deep Belief Nets for Topic Modeling
- Gaussian LDA for Topic Models with Word Embeddings
- Series of lecture notes for probabilistic topic models written in ipython notebook
- Implementation of various topic models in Python
- A closer look at Skip Gram Modeling
- Skip Gram Model Tutorial - tutorial-part-ii-the-continuous-bag-of-words-model.html)
- Language learning with NLP and reinforcement learning
- word2vec explained on deeplearning4j
- Making sense of word2vec
- The LDA Buffet - Intuitive Explanation
- alpha and beta in LDA
- How string clustering works
- Text clustering with Levenshtein distances
Optimization
Random Forest / Bagging
- Awesome Random Forest (GitHub)**
- Measures of variable importance in random forests
- Compare R-squared from two different Random Forest models
- Why doesn't Random Forest handle missing values in predictors?
- FAQs about Random Forest - forest)
- Obtaining knowledge from a random forest
- How to build random forests in R with missing (NA) values?
- Some Questions for R implementation - preprocessing-is-needed-before-prediction-using-finalmodel-of-randomfore), [3](http://stackoverflow.com/questions/17059432/random-forest-package-in-r-shows-error-during-prediction-if-there-are-new-fact)
Reinforcement Learning
- Awesome Reinforcement Learning (GitHub)
- RL Tutorial Part 1 - Learning-Part-2/)
Semi Supervised Learning
Source
- ujjwalkarn/Machine-Learning-Tutorials
Stacking Models
Statistics
- Learn Statistics Using Python - Learn Statistics using an application-centric programming approach
- What are QQ Plots?
- Stat Trek Website - A dedicated website to teach yourselves Statistics
- Online Statistics Book - An Interactive Multimedia Course for Studying Statistics
- What is a Sampling Distribution?
- AP Statistics Tutorial
- Statistics and Probability Tutorial
- Matrix Algebra Tutorial
Support Vector Machine
- A tutorial on SVMs
- Introductory Overview of SVMs
- Intro to SVM in R
- Classifier Classification with Platt's Scaling
- Help me Understand SVMs!
- How does SVM Work | Comparisons
- Trees > SVMs
- Platt's Probabilistic Outputs for SVM
- Practical Guide to SVC
- Highest Voted Questions about SVMs on Cross Validated
- SVMs > ANNs - are-advantages-of-artificial-neural-networks-over-support-vector-machines), [Another Comparison](http://www.svms.org/anns.html)
- Kernel Logistic Regression vs SVM
- Logistic Regression vs SVM - v-s-logistic-regression), 🌎 [3](www.quora.com/Support-Vector-Machines/What-is-the-difference-between-Linear-SVMs-and-Logistic-Regression)
- Optimization Algorithms in Support Vector Machines
- Variable Importance from SVM
- Why use Platts Scaling
Uncategorized
- Uncategorized
Useful Blogs
- Edwin Chen's Blog - A blog about Math, stats, ML, crowdsourcing, data science
- ML Wave - A blog for Learning Machine Learning
- Andrej Karpathy - A blog about Deep Learning and Data Science in general
- Colah's Blog - Awesome Neural Networks Blog
- Statistically Significant - Andrew Landgraf's Data Science Blog
- fastML - Machine learning made easy
- A Quantitative Journey | outlace - learning quantitative applications
- Variance Explained - David Robinson's Blog
- AI Junkie - a blog about Artificial Intellingence
- Deep Learning Blog by Tim Dettmers - Making deep learning accessible
- J Alammar's Blog - Blog posts about Machine Learning and Neural Nets
- Ethen's Notebook Collection - Continuously updated machine learning documentations (mainly in Python3). Contents include educational implementation of machine learning algorithms from scratch and open-source library usage
- no free hunch | kaggle - The Kaggle Blog about all things Data Science
- The Data School Blog - Data science for beginners!
- Alex Minnaar's Blog - A blog about Machine Learning and Software Engineering
- Simply Statistics - A blog by three biostatistics professors
- Trevor Stephens Blog - Trevor Stephens Personal Page
- r4stats - analyze the world of data science, and to help people learn to use R
Vapnik–Chervonenkis Dimension

Programming Languages

Python 9 Jupyter Notebook 8 Lua 3 TeX 3 R 2 Java 1 HTML 1

Categories

Deep Learning 113 Decision Trees 26 Natural Language Processing 23 Model Validation using Resampling 22 Introduction 21 Useful Blogs 18 Support Vector Machine 16 Boosting 13 Ensembles 10 Statistics 8 Optimization 8 Random Forest / Bagging 8 Bayesian Machine Learning 7 Classification 5 Uncategorized 5 Stacking Models 4 Semi Supervised Learning 4 Vapnik–Chervonenkis Dimension 3 Linear Regression 3 Kaggle Competitions WriteUp 3 Genetic Algorithms 2 Reinforcement Learning 2 Logistic Regression 2 Cheat Sheets 2 Source 1 Artificial Intelligence 1 Computer Vision 1

Sub Categories

Uncategorized 5

Keywords

machine-learning 12 deep-learning 10 awesome-list 5 data-science 5 python 5 awesome 4 neural-network 4 tensorflow 4 deep-learning-tutorial 3 natural-language-processing 3 artificial-intelligence 2 reinforcement-learning 2 clustering 2 tutorial 2 jupyter-notebook 2 torch 2 tensorflow-tutorials 2 ai 2 deep-neural-networks 2 deeplearning 2 neural-networks 2 caffe 1 knowledge-graph 1 image-classification 1 graph-embeddings 1 paper-list 1 examples 1 knowledge-embedding 1 graph-representation-learning 1 graph-neural-networks 1 graph-convolutional-networks 1 nlp 1 conference-publications 1 computer-science 1 courses 1 list 1 machinelearning 1 deep-networks 1 face-images 1 recurrent-networks 1 intelligent-machines 1 intelligent-systems 1 machine-intelligence 1 statistical-learning 1 unsupervised-learning 1 lists 1 resources 1 unicorns 1 bayesian-methods 1 mathematical-analysis 1