Machine Learning Pocket Reference
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Machine Learning Pocket Reference
Author | : Matt Harrison |
Publsiher | : "O'Reilly Media, Inc." |
Total Pages | : 320 |
Release | : 2019-08-27 |
Genre | : Computers |
ISBN | : 9781492047490 |
Download Machine Learning Pocket Reference Book in PDF, Epub and Kindle
With detailed notes, tables, and examples, this handy reference will help you navigate the basics of structured machine learning. Author Matt Harrison delivers a valuable guide that you can use for additional support during training and as a convenient resource when you dive into your next machine learning project. Ideal for programmers, data scientists, and AI engineers, this book includes an overview of the machine learning process and walks you through classification with structured data. You’ll also learn methods for clustering, predicting a continuous value (regression), and reducing dimensionality, among other topics. This pocket reference includes sections that cover: Classification, using the Titanic dataset Cleaning data and dealing with missing data Exploratory data analysis Common preprocessing steps using sample data Selecting features useful to the model Model selection Metrics and classification evaluation Regression examples using k-nearest neighbor, decision trees, boosting, and more Metrics for regression evaluation Clustering Dimensionality reduction Scikit-learn pipelines
Machine Learning Pocket Reference
Author | : Matt Harrison |
Publsiher | : O'Reilly Media |
Total Pages | : 321 |
Release | : 2019-08-27 |
Genre | : Computers |
ISBN | : 9781492047513 |
Download Machine Learning Pocket Reference Book in PDF, Epub and Kindle
With detailed notes, tables, and examples, this handy reference will help you navigate the basics of structured machine learning. Author Matt Harrison delivers a valuable guide that you can use for additional support during training and as a convenient resource when you dive into your next machine learning project. Ideal for programmers, data scientists, and AI engineers, this book includes an overview of the machine learning process and walks you through classification with structured data. You’ll also learn methods for clustering, predicting a continuous value (regression), and reducing dimensionality, among other topics. This pocket reference includes sections that cover: Classification, using the Titanic dataset Cleaning data and dealing with missing data Exploratory data analysis Common preprocessing steps using sample data Selecting features useful to the model Model selection Metrics and classification evaluation Regression examples using k-nearest neighbor, decision trees, boosting, and more Metrics for regression evaluation Clustering Dimensionality reduction Scikit-learn pipelines
Machine Learning Pocket Reference
Author | : Matthew Harrison |
Publsiher | : Unknown |
Total Pages | : 0 |
Release | : 2019 |
Genre | : Machine learning |
ISBN | : 1492047538 |
Download Machine Learning Pocket Reference Book in PDF, Epub and Kindle
With detailed notes, tables, and examples, this handy reference will help you navigate the basics of structured machine learning. Author Matt Harrison delivers a valuable guide that you can use for additional support during training and as a convenient resource when you dive into your next machine learning project. Ideal for programmers, data scientists, and AI engineers, this book includes an overview of the machine learning process and walks you through classification with structured data. You'll also learn methods for clustering, predicting a continuous value (regression), and reducing dimensionality, among other topics. This pocket reference includes sections that cover: Classification, using the Titanic dataset Cleaning data and dealing with missing data Exploratory data analysis Common preprocessing steps using sample data Selecting features useful to the model Model selection Metrics and classification evaluation Regression examples using k-nearest neighbor, decision trees, boosting, and more Metrics for regression evaluation Clustering Dimensionality reduction Scikit-learn pipelines.
Machine Learning Pocket Reference
Author | : Matthew Harrison |
Publsiher | : Unknown |
Total Pages | : 200 |
Release | : 2019 |
Genre | : Machine learning |
ISBN | : 1492047538 |
Download Machine Learning Pocket Reference Book in PDF, Epub and Kindle
With Early Release ebooks, you get books in their earliest form-the author's raw and unedited content as he or she writes-so you can take advantage of these technologies long before the official release of these titles. You'll also receive updates when significant changes are made, new chapters are available, and the final ebook bundle is released. With detailed notes, tables, and examples, this handy reference will help you navigate the basics of structured machine learning. Author Matt Harrison delivers a valuable guide that you can use for additional support during training and as a convenient resource when you dive into your next machine learning project. Ideal for programmers, data scientists, and AI engineers, this book includes an overview of the machine learning process and walks you through classification with structured data. You'll also learn methods for clustering, predicting a continuous value (regression), and reducing dimensionality, among other topics. This pocket reference includes sections that cover: Classification, using the Titanic dataset Cleaning data and dealing with missing data Exploratory data analysis Common preprocessing steps using sample data Selecting features useful to the model Model selection Metrics and classification evaluation Regression examples using k-nearest neighbor, decision trees, boosting, and more Metrics for regression evaluation Clustering Dimensionality reduction Scikit-learn pipelines.
PyTorch Pocket Reference
Author | : Joe Papa |
Publsiher | : "O'Reilly Media, Inc." |
Total Pages | : 310 |
Release | : 2021-05-11 |
Genre | : Computers |
ISBN | : 9781492089971 |
Download PyTorch Pocket Reference Book in PDF, Epub and Kindle
This concise, easy-to-use reference puts one of the most popular frameworks for deep learning research and development at your fingertips. Author Joe Papa provides instant access to syntax, design patterns, and code examples to accelerate your development and reduce the time you spend searching for answers. Research scientists, machine learning engineers, and software developers will find clear, structured PyTorch code that covers every step of neural network development-from loading data to customizing training loops to model optimization and GPU/TPU acceleration. Quickly learn how to deploy your code to production using AWS, Google Cloud, or Azure and deploy your ML models to mobile and edge devices. Learn basic PyTorch syntax and design patterns Create custom models and data transforms Train and deploy models using a GPU and TPU Train and test a deep learning classifier Accelerate training using optimization and distributed training Access useful PyTorch libraries and the PyTorch ecosystem
Python Pocket Reference
Author | : Mark Lutz |
Publsiher | : Oreilly & Associates Incorporated |
Total Pages | : 254 |
Release | : 2014 |
Genre | : Computers |
ISBN | : 1449357016 |
Download Python Pocket Reference Book in PDF, Epub and Kindle
Updated for both Python 3.4 and 2.7, this guide provides concise information on Python types and statements, special method names, built-in functions and exceptions, commonly used standard library modules, and other prominent Python tools.--From back cover.
PyTorch Pocket Reference
Author | : Joe Papa |
Publsiher | : O'Reilly Media |
Total Pages | : 265 |
Release | : 2021-09-14 |
Genre | : Electronic Book |
ISBN | : 149209000X |
Download PyTorch Pocket Reference Book in PDF, Epub and Kindle
This concise, easy-to-use reference puts one of the most popular frameworks for deep learning research and development at your fingertips. Author Joe Papa provides instant access to syntax, design patterns, and code examples to accelerate your development and reduce the time you spend searching for answers. Research scientists, machine learning engineers, and software developers will find clear, structured PyTorch code that covers every step of neural network development--from loading data to customizing training loops to model optimization and GPU/TPU acceleration. Quickly learn how to deploy your code to production using AWS, GCP, or Azure, and your ML models to mobile and edge devices. Learn basic PyTorch syntax and design patterns Create custom models and data transforms Train and deploy models using a GPU and TPU Train and test a deep learning classifier Accelerate training using optimization and distributed training Access useful PyTorch libraries and the PyTorch ecosystem
TensorFlow 2 Pocket Reference
Author | : KC Tung |
Publsiher | : "O'Reilly Media, Inc." |
Total Pages | : 255 |
Release | : 2021-07-19 |
Genre | : Computers |
ISBN | : 9781492089155 |
Download TensorFlow 2 Pocket Reference Book in PDF, Epub and Kindle
This easy-to-use reference for TensorFlow 2 design patterns in Python will help you make informed decisions for various use cases. Author KC Tung addresses common topics and tasks in enterprise data science and machine learning practices rather than focusing on TensorFlow itself. When and why would you feed training data as using NumPy or a streaming dataset? How would you set up cross-validations in the training process? How do you leverage a pretrained model using transfer learning? How do you perform hyperparameter tuning? Pick up this pocket reference and reduce the time you spend searching through options for your TensorFlow use cases. Understand best practices in TensorFlow model patterns and ML workflows Use code snippets as templates in building TensorFlow models and workflows Save development time by integrating prebuilt models in TensorFlow Hub Make informed design choices about data ingestion, training paradigms, model saving, and inferencing Address common scenarios such as model design style, data ingestion workflow, model training, and tuning