Machine Learning with PyTorch and Scikit-Learn
Develop machine learning and deep learning models with Python
1st Edition
Published on 3. June 2024
774 pages
978-1-80181-638-0 (ISBN)
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Sebastian Raschka | Yuxi (Hayden) Liu | Vahid Mirjalili
Machine Learning with PyTorch and Scikit-Learn
Develop machine learning and deep learning models with Python
Book
02/2022
Packt Publishing
€63.90
Shipment within 3-4 weeks
Persons
Raschka Sebastian :
Sebastian Raschka is an Assistant Professor of Statistics at the University of Wisconsin-Madison focusing on machine learning and deep learning research. As Lead AI Educator at Grid AI, Sebastian plans to continue following his passion for helping people get into machine learning and artificial intelligence.Liu Yuxi (Hayden) :
Yuxi (Hayden) Liu was a Machine Learning Software Engineer at Google. With a wealth of experience from his tenure as a machine learning scientist, he has applied his expertise across data-driven domains and applied his ML expertise in computational advertising, cybersecurity, and information retrieval. He is the author of a series of influential machine learning books and an education enthusiast. His debut book, also the first edition of Python Machine Learning by Example, ranked the #1 bestseller in Amazon and has been translated into many different languages.Mirjalili Vahid :
Vahid Mirjalili is a deep learning researcher focusing on CV applications. Vahid received a Ph.D. degree in both Mechanical Engineering and Computer Science from Michigan State University.
Sebastian Raschka is an Assistant Professor of Statistics at the University of Wisconsin-Madison focusing on machine learning and deep learning research. As Lead AI Educator at Grid AI, Sebastian plans to continue following his passion for helping people get into machine learning and artificial intelligence.Liu Yuxi (Hayden) :
Yuxi (Hayden) Liu was a Machine Learning Software Engineer at Google. With a wealth of experience from his tenure as a machine learning scientist, he has applied his expertise across data-driven domains and applied his ML expertise in computational advertising, cybersecurity, and information retrieval. He is the author of a series of influential machine learning books and an education enthusiast. His debut book, also the first edition of Python Machine Learning by Example, ranked the #1 bestseller in Amazon and has been translated into many different languages.Mirjalili Vahid :
Vahid Mirjalili is a deep learning researcher focusing on CV applications. Vahid received a Ph.D. degree in both Mechanical Engineering and Computer Science from Michigan State University.
Content
Table of Contents - Giving Computers the Ability to Learn from Data
- Training Simple Machine Learning Algorithms for Classification
- A Tour of Machine Learning Classifiers Using Scikit-Learn
- Building Good Training Datasets - Data Preprocessing
- Compressing Data via Dimensionality Reduction
- Learning Best Practices for Model Evaluation and Hyperparameter Tuning
- Combining Different Models for Ensemble Learning
- Applying Machine Learning to Sentiment Analysis
- Predicting Continuous Target Variables with Regression Analysis
- Working with Unlabeled Data - Clustering Analysis
- Implementing a Multilayer Artificial Neural Network from Scratch
- Parallelizing Neural Network Training with PyTorch
- Going Deeper - The Mechanics of PyTorch
- Classifying Images with Deep Convolutional Neural Networks
- Modeling Sequential Data Using Recurrent Neural Networks
- Transformers - Improving Natural Language Processing with Attention Mechanisms
- Generative Adversarial Networks for Synthesizing New Data
- Graph Neural Networks for Capturing Dependencies in Graph Structured Data
- Reinforcement Learning for Decision Making in Complex Environments
- Training Simple Machine Learning Algorithms for Classification
- A Tour of Machine Learning Classifiers Using Scikit-Learn
- Building Good Training Datasets - Data Preprocessing
- Compressing Data via Dimensionality Reduction
- Learning Best Practices for Model Evaluation and Hyperparameter Tuning
- Combining Different Models for Ensemble Learning
- Applying Machine Learning to Sentiment Analysis
- Predicting Continuous Target Variables with Regression Analysis
- Working with Unlabeled Data - Clustering Analysis
- Implementing a Multilayer Artificial Neural Network from Scratch
- Parallelizing Neural Network Training with PyTorch
- Going Deeper - The Mechanics of PyTorch
- Classifying Images with Deep Convolutional Neural Networks
- Modeling Sequential Data Using Recurrent Neural Networks
- Transformers - Improving Natural Language Processing with Attention Mechanisms
- Generative Adversarial Networks for Synthesizing New Data
- Graph Neural Networks for Capturing Dependencies in Graph Structured Data
- Reinforcement Learning for Decision Making in Complex Environments
