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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
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 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

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 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.

Generatives Deep Learning by David Foster

Title Generatives Deep Learning
Author David Foster
Publisher
Release 2020
Category
Total Pages 310
ISBN
Language English, Spanish, and French
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Book Summary:

Generative Modelle haben sich zu einem der spannendsten Themenbereiche der Künstlichen Intelligenz entwickelt: Mit generativem Deep Learning ist es inzwischen möglich, einer Maschine das Malen, Schreiben oder auch das Komponieren von Musik beizubringen - kreative Fähigkeiten, die bisher dem Menschen vorbehalten waren. Mit diesem praxisnahen Buch können Data Scientists einige der eindrucksvollsten generativen Deep-Learning-Modelle nachbilden wie z.B. Generative Adversarial Networks (GANs), Variational Autoencoder (VAEs), Encoder-Decoder- sowie World-Modelle. David Foster veranschaulicht die Funktionsweise jeder Methode, beginnend mit den Grundlagen des Deep Learning mit Keras, bevor er zu einigen der modernsten Algorithmen auf diesem Gebiet vorstößt. Die zahlreichen praktischen Beispiele und Tipps helfen dem Leser herauszufinden, wie seine Modelle noch effizienter lernen und noch kreativer werden können.

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 Computerworld
Author
Publisher
Release 1977-01-17
Category
Total Pages 60
ISBN
Language English, Spanish, and French
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Book Summary:

For more than 40 years, Computerworld has been the leading source of technology news and information for IT influencers worldwide. Computerworld's award-winning Web site (Computerworld.com), twice-monthly publication, focused conference series and custom research form the hub of the world's largest global IT media network.

Title The Athenaeum
Author
Publisher
Release 1839
Category
Total Pages
ISBN
Language English, Spanish, and French
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Book Summary:

Title Popular Science
Author
Publisher
Release 1958-01
Category
Total Pages 266
ISBN
Language English, Spanish, and French
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Book Summary:

Popular Science gives our readers the information and tools to improve their technology and their world. The core belief that Popular Science and our readers share: The future is going to be better, and science and technology are the driving forces that will help make it better.

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