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Title Machine Learning Pocket Reference
Author Matt Harrison
Publisher O'Reilly Media
Release 2019-09-10
Category Computers
Total Pages 303
ISBN 9781492047544
Language English, Spanish, and French
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Book Summary:

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

Title Machine Learning Pocket Reference
Author Matt Harrison
Publisher O'Reilly Media
Release 2019-08-27
Category Computers
Total Pages 320
ISBN 1492047511
Language English, Spanish, and French
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Book Summary:

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

Title PyTorch Pocket Reference
Author Joe Papa
Publisher O'Reilly Media
Release 2021-09-14
Category
Total Pages 265
ISBN 9781492090007
Language English, Spanish, and French
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Book Summary:

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

Title Deep Learning Pocket Reference Guide
Author The Data Guy
Publisher
Release 2021-02-17
Category
Total Pages 118
ISBN
Language English, Spanish, and French
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Book Summary:

Learn, Revise, and Recap several important deep learning concepts, key terms, and algorithms ranging from a feedforward neural network to generative adversarial networks and many more. Why should you get this book? A perfectly designed pocket reference guide to learn and revise deep learning at anytime, anywhere. This guide comes in handy for a quick reference during work and also for preparing deep learning interviews. The book includes cool animated characters and keeps you engaged. Book description Deep learning which is a subset of machine learning has changed the landscape of artificial intelligence and is prominently used for many interesting applications like natural language processing (NLP), computer vision (CV), predictive analytics, and many more. This book, Deep Learning Pocket Reference Guide walks you through a collection of the most important deep learning algorithms, key concepts, terms, and terminologies. This book can be used for quickly learning, revising, and recapping deep learning concepts. This book can also be used as a quick reference guide to prepare for deep learning interviews. You will learn and revise deep learning algorithms like artificial neural networks, recurrent neural networks, convolutional networks, generative adversarial networks, autoencoders, and many others. You will also look into concepts like weight initialization, batch normalization, drop out, learning rate scheduling, and many more. In the ever-changing world of data science and artificial intelligence, this quick reference guide to deep learning will help you to revise the deep learning concepts with cool animated characters. With this deep learning pocket reference guide, you can revise the algorithms anytime and anywhere you are. Is it the right book for you? If you already know deep learning and want to quickly revise and recap deep learning algorithms then this is the perfect book for you. When you are preparing for a deep learning interview or stuck at work, this book comes in handy for quickly going through all the deep learning concepts.

Title Machine Learning Pocket Reference
Author Matt Harrison
Publisher "O'Reilly Media, Inc."
Release 2019-08-27
Category Computers
Total Pages 320
ISBN 149204749X
Language English, Spanish, and French
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Book Summary:

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 by Matthew Harrison

Title Machine Learning Pocket Reference
Author Matthew Harrison
Publisher
Release 2019
Category Machine learning
Total Pages 200
ISBN 9781492047537
Language English, Spanish, and French
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Book Summary:

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.

Title TensorFlow 2 Pocket Reference
Author K. C. Tung
Publisher O'Reilly Media
Release 2021-11-16
Category
Total Pages 300
ISBN 9781492089186
Language English, Spanish, and French
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Book Summary:

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

Title Reinforcement Learning Pocket Reference
Author Matt Kirk
Publisher
Release 2021
Category
Total Pages 42
ISBN
Language English, Spanish, and French
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Book Summary:

When it comes to pattern recognition, machine learning and deep learning are excellent--until the underlying data changes. Training ML models to make decisions in a dynamic, ever-changing environment requires reinforcement learning. In this pocket reference, author Matt Kirk shows data scientists, data engineers, and software developers how to apply reinforcement learning to real-world situations. Despite its long history in academia, reinforcement learning has yet to reach practical business applications. You'll explore how modeling data over time can apply to recommendations, dynamic pricing, medical treatment plans, customer personalization, and traffic flow. This guide includes an easy-to-reference checklist. You'll explore how to: Build recommendation systems for products or content using bandits Personalize content for customers using contextual bandits Dynamically price ecommerce products using Q-learning and Deep Q-Networks Build a chatbot dialogue engine using policy gradients Apply multiple treatments over time with actor-critic algorithms Optimize traffic flow in a network using Monte Carlo tree search Segment customers based on implied rewards and inverse reinforcement learning.

Title PyTorch Pocket Reference
Author Joe Papa
Publisher "O'Reilly Media, Inc."
Release 2021-05-11
Category Computers
Total Pages 310
ISBN 1492089974
Language English, Spanish, and French
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Book Summary:

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 developmentâ??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

TensorFlow 2 Pocket Primer by Oswald Campesato

Title TensorFlow 2 Pocket Primer
Author Oswald Campesato
Publisher Stylus Publishing, LLC
Release 2019-08-27
Category Computers
Total Pages 252
ISBN 1683924592
Language English, Spanish, and French
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Book Summary:

As part of the best-selling Pocket Primer series, this book is designed to introduce beginners to basic machine learning algorithms using TensorFlow 2. It is intended to be a fast-paced introduction to various “core” features of TensorFlow, with code samples that cover machine learning and TensorFlow basics. A comprehensive appendix contains some Keras-based code samples and the underpinnings of MLPs, CNNs, RNNs, and LSTMs. The material in the chapters illustrates how to solve a variety of tasks after which you can do further reading to deepen your knowledge. Companion files with all of the code samples are available for downloading from the publisher by emailing proof of purchase to [email protected] Features: Uses Python for code samples Covers TensorFlow 2 APIs and Datasets Includes a comprehensive appendix that covers Keras and advanced topics such as NLPs, MLPs, RNNs, LSTMs Features the companion files with all of the source code examples and figures (download from the publisher)

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