- Häftad (Paperback / softback)
- Antal sidor
- 3 Revised edition
- Packt Publishing Limited
- Mirjalili, Vahid
- Black & white illustrations
- Antal komponenter
- 403:B&W 7.5 x 9.25 in or 235 x 191 mm Perfect Bound on White w/Matte Lam
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Python Machine Learning
Machine Learning and Deep Learning with Python, scikit-learn, and TensorFlow 2, 3rd Edition
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Bloggat om Python Machine Learning
Sebastian Raschka is an Assistant Professor of Statistics at the University of Wisconsin-Madison focusing on machine learning and deep learning research. Some of his recent research methods have been applied to solving problems in the field of biometrics for imparting privacy to face images. Other research focus areas include the development of methods related to model evaluation in machine learning, deep learning for ordinal targets, and applications of machine learning to computational biology. Vahid Mirjalili obtained his Ph.D. in mechanical engineering working on novel methods for large-scale, computational simulations of molecular structures. Currently, he is focusing his research efforts on applications of machine learning in various computer vision projects at the Department of Computer Science and Engineering at Michigan State University. He recently joined 3M Company as a research scientist, where he uses his expertise and applies state-of-the-art machine learning and deep learning techniques to solve real-world problems in various applications to make life better.
Table of Contents Giving Computers the Ability to Learn from Data Training Simple ML Algorithms for Classification ML Classifiers Using scikit-learn Building Good Training Datasets - Data Preprocessing Compressing Data via Dimensionality Reduction Best Practices for Model Evaluation and Hyperparameter Tuning Combining Different Models for Ensemble Learning Applying ML to Sentiment Analysis Embedding a ML Model into a Web Application Predicting Continuous Target Variables with Regression Analysis Working with Unlabeled Data - Clustering Analysis Implementing Multilayer Artificial Neural Networks Parallelizing Neural Network Training with TensorFlow TensorFlow Mechanics Classifying Images with Deep Convolutional Neural Networks Modeling Sequential Data Using Recurrent Neural Networks GANs for Synthesizing New Data RL for Decision Making in Complex Environments