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Supervised Learning In Biological Applications(1st Edition)
Authors:
Jamie Flux
Cover Type:Hardcover
Condition:Used
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ISBN: B0DF6Q34V1, 979-8336880137
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Book Summary: 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 LearningBook 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 AlgorithmsWho 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.
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