ML Algorithms in Depth: Bayesian Inference and Deep Learning
Chp02: Markov Chain Monte Carlo (MCMC)
- Estimate Pi: Monte Carlo estimate of Pi
- Binomial Tree Model: Monte Carlo simulation of binomial stock price
- Random Walk: self-avoiding random walk
- Gibbs Sampling: Gibbs sampling of multivariate Gaussian distribution
- Metropolis-Hastings Sampling: Metropolis-Hastings sampling of multivariate Gaussian mixture
- Importance Sampling: importance sampling for finding expected value of a function
Chp03: Variational Inference (VI)
- Mean Field VI: image denoising in Ising model
Chp04: Software Implementation
- Subset Generation: a complete search algorithm
- Fractional Knapsack: a greedy algorithm
- Binary Search: a divide and conquer algorithm
- Binomial Coefficients: a dynamic programming algorithm
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
- Page Rank: Google page rank algorithm
- HMM: EM algorithm for Hidden Markov Models
- Imbalanced Learning: Tomek Links, SMOTE
- Active Learning: LR
- Bayesian optimization: BO
- Ensemble Learning: Bagging, Boosting, Stacking
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}
}