Agrawal Tanay Hyperparameter Optimization in Machine Learning: Make Your Machine Learning and Deep Learning Models More Efficient 9781484265789

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Автор: Agrawal Tanay
Название: Hyperparameter Optimization in Machine Learning: Make Your Machine Learning and Deep Learning Models More Efficient
ISBN: 9781484265789
Издательство: Springer
Классификация:

Языки программирования и описания сценариев: общие сведения

Машинное обучение

ISBN-10: 1484265785
Обложка/Формат: Paperback
Страницы: 166
Вес: 0.27 кг.
Дата издания: 16.12.2020
Язык: English
Размер: 23.39 x 15.60 x 1.02 cm
Ссылка на Издательство: Link
Поставляется из: Германии
Описание:

Chapter 1: Hyperparameters

Chapter Goal: To introduce what hyperparameters are, how they can affect themodel training. Also gives an intuition of how hyperparameter affects general machinelearning algorithms, and what value should we choose as per the training dataset.Sub - Topics1. Introduction to hyperparameters.2. Why do we need to tune hyperparameters3. Specific algorithms and their hyperparameters4. Cheatsheet for deciding Hyperparameter of some specific Algorithms.
Chapter 2: Brute Force Hyperparameter TuningChapter Goal: To understand the commonly used classical hyperparameter tuningmethods and implement them from scratch, as well as use the Scikit-Learn library to do so.Sub - Topics: 1. Hyperparameter tuning2. Exhaustive hyperparameter tuning methods3. Grid search4. Random search5. Evaluation of models while tuning hyperparameters.
Chapter 3: Distributed Hyperparameter OptimizationChapter Goal: To handle bigger datasets and a large number of hyperparameterwith continuous search spaces using distributed algorithms and distributedhyperparameter optimization methods, using Dask Library.Sub - Topics: 1. Why we need distributed tuning2. Dask dataframes3. IncrementalSearchCV
Chapter 4: Sequential Model-Based Global Optimization and Its HierarchicalMethodsChapter Goal: A detailed theoretical chapter about SMBO Methods, which usesBayesian techniques to optimize hyperparameter. They learn from their previous iterationunlike Grid Search or Random Search.Sub - Topics: 1. Sequential Model-Based Global Optimization2. Gaussian process approach3. Tree-structured Parzen Estimator(TPE)
Chapter 5: Using HyperOptChapter Goal: A Chapter focusing on a library hyperopt that implements thealgorithm TPE discussed in the last chapter. Goal to use the TPE algorithm to optimizehyperparameter and make the reader aware of how it is better than other methods.MongoDB will be used to parallelize the evaluations. Discuss Hyperopt Scikit-Learn and Hyperas with examples.1. Defining an objective function.2. Creating search space.3. Running HyperOpt.4. Using MongoDB Trials to make parallel evaluations.5. HyperOpt SkLearn6. Hyperas
Chapter 6: Hyperparameter Generating Condition Generative Adversarial NeuralNetworks(HG-cGANs) and So Forth.Chapter Goal: It is based on a hypothesis of how, based on certain properties of dataset, one can train neural networks on metadata and generate hyperparameters for new datasets. It also summarizes how these newer methods of Hyperparameter Tuning can help AI to develop further.Sub - Topics: 1. Generating Metadata2. Training HG-cGANs3. AI and hyperparameter tuning

Hyperparameter Optimization in Machine Learning: Make Your Machine Learning and Deep Learning Models More Efficient, Agrawal Tanay