Hands-On Artificial Intelligence for Beginners :: an Introduction to AI Concepts, Algorithms, and Their Implementation.
This book will empower you to apply Artificial Intelligence techniques to design applications for natural language processing, robotics, and other real-world use-cases. You will be able to develop, debug, deploy and optimize intelligent AI systems for self-driving cars, game playing, and much more.
Gespeichert in:
| 1. Verfasser: | |
|---|---|
| Format: | Elektronisch E-Book |
| Sprache: | English |
| Veröffentlicht: |
Birmingham :
Packt Publishing Ltd,
2018.
|
| Schlagworte: | |
| Online-Zugang: | Volltext |
| Zusammenfassung: | This book will empower you to apply Artificial Intelligence techniques to design applications for natural language processing, robotics, and other real-world use-cases. You will be able to develop, debug, deploy and optimize intelligent AI systems for self-driving cars, game playing, and much more. |
| Beschreibung: | Neural machine translation |
| Beschreibung: | 1 online resource (349 pages) |
| Bibliographie: | Includes bibliographical references. |
| ISBN: | 1788992261 9781788992268 |
Internformat
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| 100 | 1 | |a Smith, Patrick D., |e author. |1 https://id.oclc.org/worldcat/entity/E39PCjCcvVgw7Bj9p8XDdMjWtC |0 http://id.loc.gov/authorities/names/no00052368 | |
| 245 | 1 | 0 | |a Hands-On Artificial Intelligence for Beginners : |b an Introduction to AI Concepts, Algorithms, and Their Implementation. |
| 260 | |a Birmingham : |b Packt Publishing Ltd, |c 2018. | ||
| 300 | |a 1 online resource (349 pages) | ||
| 336 | |a text |b txt |2 rdacontent | ||
| 337 | |a computer |b c |2 rdamedia | ||
| 338 | |a online resource |b cr |2 rdacarrier | ||
| 588 | 0 | |a Print version record. | |
| 505 | 0 | |a Cover; Title Page; Copyright and Credits; Packt Upsell; Contributors; Table of Contents; Preface; Chapter 1: The History of AI; The beginnings of AI -1950-1974; Rebirth -1980-1987; The modern era takes hold -- 1997-2005; Deep learning and the future -- 2012-Present; Summary; Chapter 2: Machine Learning Basics; Technical requirements; Applied math basics; The building blocks -- scalars, vectors, matrices, and tensors; Scalars; Vectors; Matrices; Tensors; Matrix math; Scalar operations; Element-wise operations; Basic statistics and probability theory; The probability space and general theory | |
| 505 | 8 | |a Probability distributionsProbability mass functions ; Probability density functions ; Conditional and joint probability; Chain rule for joint probability; Bayes' rule for conditional probability; Constructing basic machine learning algorithms; Supervised learning algorithms; Random forests; Unsupervised learning algorithms; Basic tuning; Overfitting and underfitting; K-fold cross-validation; Hyperparameter optimization; Summary; Chapter 3: Platforms and Other Essentials; Technical requirements; TensorFlow, PyTorch, and Keras; TensorFlow; Basic building blocks; The TensorFlow graph; PyTorch | |
| 505 | 8 | |a Basic building blocksThe PyTorch graph; Keras; Basic building blocks; Wrapping up; Cloud computing essentials; AWS basics; EC2 and virtual machines; S3 Storage ; AWS Sagemaker; Google Cloud Platform basics; GCP cloud storage; GCP Cloud ML Engine; CPUs, GPUs, and other compute frameworks; Installing GPU libraries and drivers; With Linux (Ubuntu); With Windows; Basic GPU operations; The future -- TPUs and more; Summary; Chapter 4: Your First Artificial Neural Networks; Technical requirements; Network building blocks; Network layers; Naming and sizing neural networks | |
| 505 | 8 | |a Setting up network parameters in our MNIST exampleActivation functions; Historically popular activation functions; Modern approaches to activation functions; Weights and bias factors; Utilizing weights and biases in our MNIST example; Loss functions; Using a loss function for simple regression; Using cross-entropy for binary classification problems; Defining a loss function in our MNIST example; Stochastic gradient descent; Learning rates; Utilizing the Adam optimizer in our MNIST example; Regularization; The training process; Putting it all together; Forward propagation; Backpropagation | |
| 505 | 8 | |a Forwardprop and backprop with MNISTManaging a TensorFlow model; Saving model checkpoints; Summary; Chapter 5: Convolutional Neural Networks; Overview of CNNs; Convolutional layers; Layer parameters and structure; Pooling layers; Fully connected layers; The training process; CNNs for image tagging; Summary; Chapter 6: Recurrent Neural Networks; Technical requirements; The building blocks of RNNs; Basic structure; Vanilla recurrent neural networks; One-to-many; Many-to-one; Many-to-many; Backpropagation through time; Memory units -- LSTMs and GRUs; LSTM; GRUs; Sequence processing with RNNs | |
| 500 | |a Neural machine translation | ||
| 520 | |a This book will empower you to apply Artificial Intelligence techniques to design applications for natural language processing, robotics, and other real-world use-cases. You will be able to develop, debug, deploy and optimize intelligent AI systems for self-driving cars, game playing, and much more. | ||
| 504 | |a Includes bibliographical references. | ||
| 650 | 0 | |a Machine learning. |0 http://id.loc.gov/authorities/subjects/sh85079324 | |
| 650 | 0 | |a Artificial intelligence. |0 http://id.loc.gov/authorities/subjects/sh85008180 | |
| 650 | 2 | |a Artificial Intelligence |0 https://id.nlm.nih.gov/mesh/D001185 | |
| 650 | 2 | |a Machine Learning |0 https://id.nlm.nih.gov/mesh/D000069550 | |
| 650 | 6 | |a Apprentissage automatique. | |
| 650 | 6 | |a Intelligence artificielle. | |
| 650 | 7 | |a artificial intelligence. |2 aat | |
| 650 | 7 | |a Natural language & machine translation. |2 bicssc | |
| 650 | 7 | |a Neural networks & fuzzy systems. |2 bicssc | |
| 650 | 7 | |a Artificial intelligence. |2 bicssc | |
| 650 | 7 | |a Computers |x Natural Language Processing. |2 bisacsh | |
| 650 | 7 | |a Computers |x Neural Networks. |2 bisacsh | |
| 650 | 7 | |a Computers |x Intelligence (AI) & Semantics. |2 bisacsh | |
| 650 | 7 | |a Artificial intelligence |2 fast | |
| 650 | 7 | |a Machine learning |2 fast | |
| 758 | |i has work: |a Hands-On Artificial Intelligence for Beginners (Text) |1 https://id.oclc.org/worldcat/entity/E39PCGYPdWXCV4wmgDcwG8PTgX |4 https://id.oclc.org/worldcat/ontology/hasWork | ||
| 776 | 0 | 8 | |i Print version: |a D. Smith, Patrick. |t Hands-On Artificial Intelligence for Beginners : An Introduction to AI Concepts, Algorithms, and Their Implementation. |d Birmingham : Packt Publishing Ltd, ©2018 |z 9781788991063 |
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| contents | Cover; Title Page; Copyright and Credits; Packt Upsell; Contributors; Table of Contents; Preface; Chapter 1: The History of AI; The beginnings of AI -1950-1974; Rebirth -1980-1987; The modern era takes hold -- 1997-2005; Deep learning and the future -- 2012-Present; Summary; Chapter 2: Machine Learning Basics; Technical requirements; Applied math basics; The building blocks -- scalars, vectors, matrices, and tensors; Scalars; Vectors; Matrices; Tensors; Matrix math; Scalar operations; Element-wise operations; Basic statistics and probability theory; The probability space and general theory Probability distributionsProbability mass functions ; Probability density functions ; Conditional and joint probability; Chain rule for joint probability; Bayes' rule for conditional probability; Constructing basic machine learning algorithms; Supervised learning algorithms; Random forests; Unsupervised learning algorithms; Basic tuning; Overfitting and underfitting; K-fold cross-validation; Hyperparameter optimization; Summary; Chapter 3: Platforms and Other Essentials; Technical requirements; TensorFlow, PyTorch, and Keras; TensorFlow; Basic building blocks; The TensorFlow graph; PyTorch Basic building blocksThe PyTorch graph; Keras; Basic building blocks; Wrapping up; Cloud computing essentials; AWS basics; EC2 and virtual machines; S3 Storage ; AWS Sagemaker; Google Cloud Platform basics; GCP cloud storage; GCP Cloud ML Engine; CPUs, GPUs, and other compute frameworks; Installing GPU libraries and drivers; With Linux (Ubuntu); With Windows; Basic GPU operations; The future -- TPUs and more; Summary; Chapter 4: Your First Artificial Neural Networks; Technical requirements; Network building blocks; Network layers; Naming and sizing neural networks Setting up network parameters in our MNIST exampleActivation functions; Historically popular activation functions; Modern approaches to activation functions; Weights and bias factors; Utilizing weights and biases in our MNIST example; Loss functions; Using a loss function for simple regression; Using cross-entropy for binary classification problems; Defining a loss function in our MNIST example; Stochastic gradient descent; Learning rates; Utilizing the Adam optimizer in our MNIST example; Regularization; The training process; Putting it all together; Forward propagation; Backpropagation Forwardprop and backprop with MNISTManaging a TensorFlow model; Saving model checkpoints; Summary; Chapter 5: Convolutional Neural Networks; Overview of CNNs; Convolutional layers; Layer parameters and structure; Pooling layers; Fully connected layers; The training process; CNNs for image tagging; Summary; Chapter 6: Recurrent Neural Networks; Technical requirements; The building blocks of RNNs; Basic structure; Vanilla recurrent neural networks; One-to-many; Many-to-one; Many-to-many; Backpropagation through time; Memory units -- LSTMs and GRUs; LSTM; GRUs; Sequence processing with RNNs |
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| indexdate | 2024-11-27T13:29:16Z |
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| publisher | Packt Publishing Ltd, |
| record_format | marc |
| spelling | Smith, Patrick D., author. https://id.oclc.org/worldcat/entity/E39PCjCcvVgw7Bj9p8XDdMjWtC http://id.loc.gov/authorities/names/no00052368 Hands-On Artificial Intelligence for Beginners : an Introduction to AI Concepts, Algorithms, and Their Implementation. Birmingham : Packt Publishing Ltd, 2018. 1 online resource (349 pages) text txt rdacontent computer c rdamedia online resource cr rdacarrier Print version record. Cover; Title Page; Copyright and Credits; Packt Upsell; Contributors; Table of Contents; Preface; Chapter 1: The History of AI; The beginnings of AI -1950-1974; Rebirth -1980-1987; The modern era takes hold -- 1997-2005; Deep learning and the future -- 2012-Present; Summary; Chapter 2: Machine Learning Basics; Technical requirements; Applied math basics; The building blocks -- scalars, vectors, matrices, and tensors; Scalars; Vectors; Matrices; Tensors; Matrix math; Scalar operations; Element-wise operations; Basic statistics and probability theory; The probability space and general theory Probability distributionsProbability mass functions ; Probability density functions ; Conditional and joint probability; Chain rule for joint probability; Bayes' rule for conditional probability; Constructing basic machine learning algorithms; Supervised learning algorithms; Random forests; Unsupervised learning algorithms; Basic tuning; Overfitting and underfitting; K-fold cross-validation; Hyperparameter optimization; Summary; Chapter 3: Platforms and Other Essentials; Technical requirements; TensorFlow, PyTorch, and Keras; TensorFlow; Basic building blocks; The TensorFlow graph; PyTorch Basic building blocksThe PyTorch graph; Keras; Basic building blocks; Wrapping up; Cloud computing essentials; AWS basics; EC2 and virtual machines; S3 Storage ; AWS Sagemaker; Google Cloud Platform basics; GCP cloud storage; GCP Cloud ML Engine; CPUs, GPUs, and other compute frameworks; Installing GPU libraries and drivers; With Linux (Ubuntu); With Windows; Basic GPU operations; The future -- TPUs and more; Summary; Chapter 4: Your First Artificial Neural Networks; Technical requirements; Network building blocks; Network layers; Naming and sizing neural networks Setting up network parameters in our MNIST exampleActivation functions; Historically popular activation functions; Modern approaches to activation functions; Weights and bias factors; Utilizing weights and biases in our MNIST example; Loss functions; Using a loss function for simple regression; Using cross-entropy for binary classification problems; Defining a loss function in our MNIST example; Stochastic gradient descent; Learning rates; Utilizing the Adam optimizer in our MNIST example; Regularization; The training process; Putting it all together; Forward propagation; Backpropagation Forwardprop and backprop with MNISTManaging a TensorFlow model; Saving model checkpoints; Summary; Chapter 5: Convolutional Neural Networks; Overview of CNNs; Convolutional layers; Layer parameters and structure; Pooling layers; Fully connected layers; The training process; CNNs for image tagging; Summary; Chapter 6: Recurrent Neural Networks; Technical requirements; The building blocks of RNNs; Basic structure; Vanilla recurrent neural networks; One-to-many; Many-to-one; Many-to-many; Backpropagation through time; Memory units -- LSTMs and GRUs; LSTM; GRUs; Sequence processing with RNNs Neural machine translation This book will empower you to apply Artificial Intelligence techniques to design applications for natural language processing, robotics, and other real-world use-cases. You will be able to develop, debug, deploy and optimize intelligent AI systems for self-driving cars, game playing, and much more. Includes bibliographical references. Machine learning. http://id.loc.gov/authorities/subjects/sh85079324 Artificial intelligence. http://id.loc.gov/authorities/subjects/sh85008180 Artificial Intelligence https://id.nlm.nih.gov/mesh/D001185 Machine Learning https://id.nlm.nih.gov/mesh/D000069550 Apprentissage automatique. Intelligence artificielle. artificial intelligence. aat Natural language & machine translation. bicssc Neural networks & fuzzy systems. bicssc Artificial intelligence. bicssc Computers Natural Language Processing. bisacsh Computers Neural Networks. bisacsh Computers Intelligence (AI) & Semantics. bisacsh Artificial intelligence fast Machine learning fast has work: Hands-On Artificial Intelligence for Beginners (Text) https://id.oclc.org/worldcat/entity/E39PCGYPdWXCV4wmgDcwG8PTgX https://id.oclc.org/worldcat/ontology/hasWork Print version: D. Smith, Patrick. Hands-On Artificial Intelligence for Beginners : An Introduction to AI Concepts, Algorithms, and Their Implementation. Birmingham : Packt Publishing Ltd, ©2018 9781788991063 FWS01 ZDB-4-EBA FWS_PDA_EBA https://search.ebscohost.com/login.aspx?direct=true&scope=site&db=nlebk&AN=1947196 Volltext |
| spellingShingle | Smith, Patrick D. Hands-On Artificial Intelligence for Beginners : an Introduction to AI Concepts, Algorithms, and Their Implementation. Cover; Title Page; Copyright and Credits; Packt Upsell; Contributors; Table of Contents; Preface; Chapter 1: The History of AI; The beginnings of AI -1950-1974; Rebirth -1980-1987; The modern era takes hold -- 1997-2005; Deep learning and the future -- 2012-Present; Summary; Chapter 2: Machine Learning Basics; Technical requirements; Applied math basics; The building blocks -- scalars, vectors, matrices, and tensors; Scalars; Vectors; Matrices; Tensors; Matrix math; Scalar operations; Element-wise operations; Basic statistics and probability theory; The probability space and general theory Probability distributionsProbability mass functions ; Probability density functions ; Conditional and joint probability; Chain rule for joint probability; Bayes' rule for conditional probability; Constructing basic machine learning algorithms; Supervised learning algorithms; Random forests; Unsupervised learning algorithms; Basic tuning; Overfitting and underfitting; K-fold cross-validation; Hyperparameter optimization; Summary; Chapter 3: Platforms and Other Essentials; Technical requirements; TensorFlow, PyTorch, and Keras; TensorFlow; Basic building blocks; The TensorFlow graph; PyTorch Basic building blocksThe PyTorch graph; Keras; Basic building blocks; Wrapping up; Cloud computing essentials; AWS basics; EC2 and virtual machines; S3 Storage ; AWS Sagemaker; Google Cloud Platform basics; GCP cloud storage; GCP Cloud ML Engine; CPUs, GPUs, and other compute frameworks; Installing GPU libraries and drivers; With Linux (Ubuntu); With Windows; Basic GPU operations; The future -- TPUs and more; Summary; Chapter 4: Your First Artificial Neural Networks; Technical requirements; Network building blocks; Network layers; Naming and sizing neural networks Setting up network parameters in our MNIST exampleActivation functions; Historically popular activation functions; Modern approaches to activation functions; Weights and bias factors; Utilizing weights and biases in our MNIST example; Loss functions; Using a loss function for simple regression; Using cross-entropy for binary classification problems; Defining a loss function in our MNIST example; Stochastic gradient descent; Learning rates; Utilizing the Adam optimizer in our MNIST example; Regularization; The training process; Putting it all together; Forward propagation; Backpropagation Forwardprop and backprop with MNISTManaging a TensorFlow model; Saving model checkpoints; Summary; Chapter 5: Convolutional Neural Networks; Overview of CNNs; Convolutional layers; Layer parameters and structure; Pooling layers; Fully connected layers; The training process; CNNs for image tagging; Summary; Chapter 6: Recurrent Neural Networks; Technical requirements; The building blocks of RNNs; Basic structure; Vanilla recurrent neural networks; One-to-many; Many-to-one; Many-to-many; Backpropagation through time; Memory units -- LSTMs and GRUs; LSTM; GRUs; Sequence processing with RNNs Machine learning. http://id.loc.gov/authorities/subjects/sh85079324 Artificial intelligence. http://id.loc.gov/authorities/subjects/sh85008180 Artificial Intelligence https://id.nlm.nih.gov/mesh/D001185 Machine Learning https://id.nlm.nih.gov/mesh/D000069550 Apprentissage automatique. Intelligence artificielle. artificial intelligence. aat Natural language & machine translation. bicssc Neural networks & fuzzy systems. bicssc Artificial intelligence. bicssc Computers Natural Language Processing. bisacsh Computers Neural Networks. bisacsh Computers Intelligence (AI) & Semantics. bisacsh Artificial intelligence fast Machine learning fast |
| subject_GND | http://id.loc.gov/authorities/subjects/sh85079324 http://id.loc.gov/authorities/subjects/sh85008180 https://id.nlm.nih.gov/mesh/D001185 https://id.nlm.nih.gov/mesh/D000069550 |
| title | Hands-On Artificial Intelligence for Beginners : an Introduction to AI Concepts, Algorithms, and Their Implementation. |
| title_auth | Hands-On Artificial Intelligence for Beginners : an Introduction to AI Concepts, Algorithms, and Their Implementation. |
| title_exact_search | Hands-On Artificial Intelligence for Beginners : an Introduction to AI Concepts, Algorithms, and Their Implementation. |
| title_full | Hands-On Artificial Intelligence for Beginners : an Introduction to AI Concepts, Algorithms, and Their Implementation. |
| title_fullStr | Hands-On Artificial Intelligence for Beginners : an Introduction to AI Concepts, Algorithms, and Their Implementation. |
| title_full_unstemmed | Hands-On Artificial Intelligence for Beginners : an Introduction to AI Concepts, Algorithms, and Their Implementation. |
| title_short | Hands-On Artificial Intelligence for Beginners : |
| title_sort | hands on artificial intelligence for beginners an introduction to ai concepts algorithms and their implementation |
| title_sub | an Introduction to AI Concepts, Algorithms, and Their Implementation. |
| topic | Machine learning. http://id.loc.gov/authorities/subjects/sh85079324 Artificial intelligence. http://id.loc.gov/authorities/subjects/sh85008180 Artificial Intelligence https://id.nlm.nih.gov/mesh/D001185 Machine Learning https://id.nlm.nih.gov/mesh/D000069550 Apprentissage automatique. Intelligence artificielle. artificial intelligence. aat Natural language & machine translation. bicssc Neural networks & fuzzy systems. bicssc Artificial intelligence. bicssc Computers Natural Language Processing. bisacsh Computers Neural Networks. bisacsh Computers Intelligence (AI) & Semantics. bisacsh Artificial intelligence fast Machine learning fast |
| topic_facet | Machine learning. Artificial intelligence. Artificial Intelligence Machine Learning Apprentissage automatique. Intelligence artificielle. artificial intelligence. Natural language & machine translation. Neural networks & fuzzy systems. Computers Natural Language Processing. Computers Neural Networks. Computers Intelligence (AI) & Semantics. Artificial intelligence Machine learning |
| url | https://search.ebscohost.com/login.aspx?direct=true&scope=site&db=nlebk&AN=1947196 |
| work_keys_str_mv | AT smithpatrickd handsonartificialintelligenceforbeginnersanintroductiontoaiconceptsalgorithmsandtheirimplementation |