Deep learning by example :: a hands-on guide to implementing advanced machine learning algorithms and neural networks /
Annotation
Gespeichert in:
| 1. Verfasser: | |
|---|---|
| Format: | Elektronisch E-Book |
| Sprache: | English |
| Veröffentlicht: |
Birmingham, UK :
Packt Publishing,
2018.
|
| Schlagworte: | |
| Online-Zugang: | Volltext |
| Zusammenfassung: | Annotation |
| Beschreibung: | 1 online resource (1 volume) : illustrations |
| ISBN: | 9781788395762 178839576X 1788399900 9781788399906 |
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| 505 | 0 | |a Table of Contents Data science: Bird's-eye view Data Modeling in Action -- The Titanic Example Feature Engineering and Model Complexity -- The Titanic Example Revisited Get Up and Running with TensorFlow Tensorflow in Action -- Some Basic Examples Deep Feed-forward Neural Networks -- Implementing Digit Classification Introduction to Convolutional Neural Networks Object Detection -- CIFAR-10 Example Object Detection -- Transfer Learning with CNNs Recurrent-Type Neural Networks -- Language modeling Representation Learning -- Implementing Word Embeddings Neural sentiment Analysis Autoencoders -- Feature Extraction and Denoising Generative Adversarial Networks in Action -- Generating New Images Face Generation and Handling Missing Labels Appendix -- Implementing Fish Recognition. | |
| 520 | 8 | |a Annotation |b Grasp the fundamental concepts of deep learning using Tensorflow in a hands-on mannerKey Features Get a first-hand experience of the deep learning concepts and techniques with this easy-to-follow guide Train different types of neural networks using Tensorflow for real-world problems in language processing, computer vision, transfer learning, and more Designed for those who believe in the concept of 'learn by doing', this book is a perfect blend of theory and code examplesBook DescriptionDeep learning is a popular subset of machine learning, and it allows you to build complex models that are faster and give more accurate predictions. This book is your companion to take your first steps into the world of deep learning, with hands-on examples to boost your understanding of the topic. This book starts with a quick overview of the essential concepts of data science and machine learning which are required to get started with deep learning. It introduces you to Tensorflow, the most widely used machine learning library for training deep learning models. You will then work on your first deep learning problem by training a deep feed-forward neural network for digit classification, and move on to tackle other real-world problems in computer vision, language processing, sentiment analysis, and more. Advanced deep learning models such as generative adversarial networks and their applications are also covered in this book. By the end of this book, you will have a solid understanding of all the essential concepts in deep learning. With the help of the examples and code provided in this book, you will be equipped to train your own deep learning models with more confidence. What you will learnUnderstand the fundamentals of deep learning and how it is different from machine learningGet familiarized with Tensorflow, one of the most popular libraries for advanced machine learningIncrease the predictive power of your model using feature engineeringUnderstand the basics of deep learning by solving a digit classification problem of MNISTDemonstrate face generation based on the CelebA database, a promising application of generative modelsApply deep learning to other domains like language modeling, sentiment analysis, and machine translationWho this book is forThis book targets data scientists and machine learning developers who wish to get started with deep learning. If you know what deep learning is but are not quite sure of how to use it, this book will help you as well. An understanding of statistics and data science concepts is required. Some familiarity with Python programming will also be beneficial. | |
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| contents | Table of Contents Data science: Bird's-eye view Data Modeling in Action -- The Titanic Example Feature Engineering and Model Complexity -- The Titanic Example Revisited Get Up and Running with TensorFlow Tensorflow in Action -- Some Basic Examples Deep Feed-forward Neural Networks -- Implementing Digit Classification Introduction to Convolutional Neural Networks Object Detection -- CIFAR-10 Example Object Detection -- Transfer Learning with CNNs Recurrent-Type Neural Networks -- Language modeling Representation Learning -- Implementing Word Embeddings Neural sentiment Analysis Autoencoders -- Feature Extraction and Denoising Generative Adversarial Networks in Action -- Generating New Images Face Generation and Handling Missing Labels Appendix -- Implementing Fish Recognition. |
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| spelling | Menshawy, Ahmed, author. Deep learning by example : a hands-on guide to implementing advanced machine learning algorithms and neural networks / Ahmed Menshawy. Birmingham, UK : Packt Publishing, 2018. 1 online resource (1 volume) : illustrations text txt rdacontent computer c rdamedia online resource cr rdacarrier data file Online resource; title from title page (viewed March 12, 2018). Table of Contents Data science: Bird's-eye view Data Modeling in Action -- The Titanic Example Feature Engineering and Model Complexity -- The Titanic Example Revisited Get Up and Running with TensorFlow Tensorflow in Action -- Some Basic Examples Deep Feed-forward Neural Networks -- Implementing Digit Classification Introduction to Convolutional Neural Networks Object Detection -- CIFAR-10 Example Object Detection -- Transfer Learning with CNNs Recurrent-Type Neural Networks -- Language modeling Representation Learning -- Implementing Word Embeddings Neural sentiment Analysis Autoencoders -- Feature Extraction and Denoising Generative Adversarial Networks in Action -- Generating New Images Face Generation and Handling Missing Labels Appendix -- Implementing Fish Recognition. Annotation Grasp the fundamental concepts of deep learning using Tensorflow in a hands-on mannerKey Features Get a first-hand experience of the deep learning concepts and techniques with this easy-to-follow guide Train different types of neural networks using Tensorflow for real-world problems in language processing, computer vision, transfer learning, and more Designed for those who believe in the concept of 'learn by doing', this book is a perfect blend of theory and code examplesBook DescriptionDeep learning is a popular subset of machine learning, and it allows you to build complex models that are faster and give more accurate predictions. This book is your companion to take your first steps into the world of deep learning, with hands-on examples to boost your understanding of the topic. This book starts with a quick overview of the essential concepts of data science and machine learning which are required to get started with deep learning. It introduces you to Tensorflow, the most widely used machine learning library for training deep learning models. You will then work on your first deep learning problem by training a deep feed-forward neural network for digit classification, and move on to tackle other real-world problems in computer vision, language processing, sentiment analysis, and more. Advanced deep learning models such as generative adversarial networks and their applications are also covered in this book. By the end of this book, you will have a solid understanding of all the essential concepts in deep learning. With the help of the examples and code provided in this book, you will be equipped to train your own deep learning models with more confidence. What you will learnUnderstand the fundamentals of deep learning and how it is different from machine learningGet familiarized with Tensorflow, one of the most popular libraries for advanced machine learningIncrease the predictive power of your model using feature engineeringUnderstand the basics of deep learning by solving a digit classification problem of MNISTDemonstrate face generation based on the CelebA database, a promising application of generative modelsApply deep learning to other domains like language modeling, sentiment analysis, and machine translationWho this book is forThis book targets data scientists and machine learning developers who wish to get started with deep learning. If you know what deep learning is but are not quite sure of how to use it, this book will help you as well. An understanding of statistics and data science concepts is required. Some familiarity with Python programming will also be beneficial. Machine learning. http://id.loc.gov/authorities/subjects/sh85079324 Apprentissage automatique. Mathematical theory of computation. bicssc Artificial intelligence. bicssc Machine learning. bicssc Neural networks & fuzzy systems. bicssc Computers Intelligence (AI) & Semantics. bisacsh Computers Machine Theory. bisacsh Computers Neural Networks. bisacsh COMPUTERS General. bisacsh Machine learning fast FWS01 ZDB-4-EBA FWS_PDA_EBA https://search.ebscohost.com/login.aspx?direct=true&scope=site&db=nlebk&AN=1728063 Volltext |
| spellingShingle | Menshawy, Ahmed Deep learning by example : a hands-on guide to implementing advanced machine learning algorithms and neural networks / Table of Contents Data science: Bird's-eye view Data Modeling in Action -- The Titanic Example Feature Engineering and Model Complexity -- The Titanic Example Revisited Get Up and Running with TensorFlow Tensorflow in Action -- Some Basic Examples Deep Feed-forward Neural Networks -- Implementing Digit Classification Introduction to Convolutional Neural Networks Object Detection -- CIFAR-10 Example Object Detection -- Transfer Learning with CNNs Recurrent-Type Neural Networks -- Language modeling Representation Learning -- Implementing Word Embeddings Neural sentiment Analysis Autoencoders -- Feature Extraction and Denoising Generative Adversarial Networks in Action -- Generating New Images Face Generation and Handling Missing Labels Appendix -- Implementing Fish Recognition. Machine learning. http://id.loc.gov/authorities/subjects/sh85079324 Apprentissage automatique. Mathematical theory of computation. bicssc Artificial intelligence. bicssc Machine learning. bicssc Neural networks & fuzzy systems. bicssc Computers Intelligence (AI) & Semantics. bisacsh Computers Machine Theory. bisacsh Computers Neural Networks. bisacsh COMPUTERS General. bisacsh Machine learning fast |
| subject_GND | http://id.loc.gov/authorities/subjects/sh85079324 |
| title | Deep learning by example : a hands-on guide to implementing advanced machine learning algorithms and neural networks / |
| title_auth | Deep learning by example : a hands-on guide to implementing advanced machine learning algorithms and neural networks / |
| title_exact_search | Deep learning by example : a hands-on guide to implementing advanced machine learning algorithms and neural networks / |
| title_full | Deep learning by example : a hands-on guide to implementing advanced machine learning algorithms and neural networks / Ahmed Menshawy. |
| title_fullStr | Deep learning by example : a hands-on guide to implementing advanced machine learning algorithms and neural networks / Ahmed Menshawy. |
| title_full_unstemmed | Deep learning by example : a hands-on guide to implementing advanced machine learning algorithms and neural networks / Ahmed Menshawy. |
| title_short | Deep learning by example : |
| title_sort | deep learning by example a hands on guide to implementing advanced machine learning algorithms and neural networks |
| title_sub | a hands-on guide to implementing advanced machine learning algorithms and neural networks / |
| topic | Machine learning. http://id.loc.gov/authorities/subjects/sh85079324 Apprentissage automatique. Mathematical theory of computation. bicssc Artificial intelligence. bicssc Machine learning. bicssc Neural networks & fuzzy systems. bicssc Computers Intelligence (AI) & Semantics. bisacsh Computers Machine Theory. bisacsh Computers Neural Networks. bisacsh COMPUTERS General. bisacsh Machine learning fast |
| topic_facet | Machine learning. Apprentissage automatique. Mathematical theory of computation. Artificial intelligence. Neural networks & fuzzy systems. Computers Intelligence (AI) & Semantics. Computers Machine Theory. Computers Neural Networks. COMPUTERS General. Machine learning |
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