Free Download Supervised Learning in Biological Applications (Genesis Protocol: Next Generation Technology for Biological and Life Sciences) by Jamie Flux
English | August 24, 2024 | ISBN: N/A | ASIN: B0DF6CVBQD | 204 pages | PDF | 4.00 Mb
Discover the power of supervised learning in biological applications with this comprehensive guide. This book introduces you to a wide range of gradient boosting algorithms, exploring their principles and implementation in Python. Each chapter focuses on a specific algorithm or technique, providing in-depth explanations, practical examples, and fully-coded Python applications.
Key Features:
– Understand the principles behind gradient boosting algorithms
– Explore popular algorithms such as XGBoost, LightGBM, CatBoost, and AdaBoost
– Learn how to apply gradient boosting with decision trees, linear discriminant analysis, and quadratic discriminant analysis
– Dive into advanced topics like softmax function, entropy and information gain, maximum likelihood estimation, and Bayesian inference
– Gain hands-on experience with optimization techniques such as stochastic gradient descent, Adam optimizer, and ridge, lasso, and elastic net regressions
– Master the concepts of kernel methods, radial basis function networks, Fourier and wavelet transforms, and Monte Carlo methods
– Discover the power of genetic algorithms, ant colony optimization, primal-dual methods, latent variable models, and reinforcement learning
Book Description:
Supervised Learning in Biological Applications is a comprehensive guide that brings together various supervised learning techniques with a focus on their applications in the field of biology. Whether you are a biologist, researcher, or data scientist, this book will equip you with the necessary knowledge and skills to effectively apply these algorithms to solve biological problems. Each chapter presents a different algorithm or technique, including detailed explanations, Python code examples, and practical applications.
What You Will Learn:
– Understand the principles and concepts behind gradient boosting algorithms
– Implement popular gradient boosting algorithms like XGBoost, LightGBM, and CatBoost in Python
– Apply gradient boosting with decision trees and explore its equations and model derivation
– Perform linear and quadratic discriminant analysis for classification problems
– Use softmax function for multi-class classification and input to neural networks
– Measure information gain and apply it to improve model decisions
– Implement optimization techniques such as stochastic gradient descent and Adam optimizer
– Apply ridge, lasso, and elastic net regressions for regularization and bias-variance tradeoff in linear regressions
– Explore kernel methods, radial basis function networks, Fourier and wavelet transforms
– Understand Monte Carlo methods, simulated annealing, genetic algorithms, ant colony optimization, and primal-dual methods
– Explore latent variable models, including factor analysis and independent component analysis
– Discover the principles of reinforcement learning and implement Q-learning and policy gradient algorithms
Who This Book Is For:
This book is for biologists, researchers, and data scientists interested in applying supervised learning algorithms in biological applications. You should have basic knowledge of Python programming and a background in biology or related fields. The Python code provided in each chapter will help you implement and experiment with the algorithms discussed in the book.