Skip to content

depley/ml_algo_in_depth

 
 

Folders and files

NameName
Last commit message
Last commit date

Latest commit

 

History

82 Commits
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 

Repository files navigation

Machine Learning Algorithms in Depth

ML Algorithms in Depth: Bayesian Inference and Deep Learning

Chp02: Markov Chain Monte Carlo (MCMC)

Chp03: Variational Inference (VI)

Chp04: Software Implementation

Chp05: Classification Algorithms

  • Perceptron: perceptron algorithm
  • SVM: support vector machine
  • SGD-LR: stochastic gradient descent logistic regression
  • Naive Bayes: Bernoulli Naive Bayes algorithm
  • CART: decision tree classification algorithm

Chp06: Regression Algorithms

  • KNN: K-Nearest Neighbors regression
  • BLR: Bayesian linear regression
  • HBR: Hierarchical Bayesian regression
  • GPR: Gaussian Process regression

Chp07: Selected Supervised Learning Algorithms

Chp08: Unsupervised Learning Algorithms

  • DP-Means: Dirichlet Process (DP) K-Means
  • EM-GMM: EM algorithm for Gaussian Mixture Models
  • PCA: Principal Component Analysis
  • t-SNE: t-SNE manifold learning

Chp09: Selected Unsupervised Learning Algorithms

  • LDA: Variational Inference for Latent Dirichlet Allocation
  • KDE: Kernel Density Estimator
  • TPO: Tangent Portfolio Optimization
  • ICE: Inverse Covariance Estimation
  • SA: Simulated Annealing
  • GA: Genetic Algorithm

Chp10: Fundamental Deep Learning Algorithms

  • MLP: Multi-Layer Perceptron
  • LeNet: LeNet for MNIST digit classification
  • ResNet: ResNet50 image search on CalTech101 dataset
  • LSTM: LSTM sentiment classification of IMDB movie dataset
  • MINN: Mult-Input Neural Net model for sequence similarity of Quora question pairs dataset
  • OPT: Neural Net Optimizers

Chp11: Advanced Deep Learning Algorithms

  • LSTM-VAE: time-series anomaly detector
  • MDN: mixture density network
  • Transformer: for text classification
  • GNN: graph neural network

Environment

To install required libraries, please run the following commands:

python3 -m venv ml-algo

source ml-algo/bin/activate    //in linux
.\ml-algo\Scripts\activate.bat //in CMD windows
.\ml-algo\Scripts\Activate.ps1 //in Powershell windows

pip install -r requirements.txt

Manning Early Access Preview (MEAP)

This book is now available in Manning Early Access Preview.
Link to book: https://www.manning.com/books/machine-learning-algorithms-in-depth

It will help you develop mathematical intuition for classic and modern ML algorithms, learn the fundamentals of Bayesian inference and deep learning, as well as data structures and algorithmic paradigms in ML!

Citation

You are welcome to cite the book as follows:

@book{MLAlgoInDepth,
  author = {Vadim Smolyakov},
  title = {Machine Learning Algorithms in Depth},
  year = {2023},
  isbn = {9781633439214},
  publisher = {Manning Publications}
}

About

ML algorithms in depth

Resources

Stars

Watchers

Forks

Releases

No releases published

Packages

No packages published

Languages

  • Python 100.0%