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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.
| Clasificación: | Libro Electrónico |
|---|---|
| Autor principal: | |
| Formato: | Electrónico eBook |
| Idioma: | Inglés |
| Publicado: |
Birmingham :
Packt Publishing Ltd,
2018.
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| Temas: | |
| Acceso en línea: | Texto completo |
MARC
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| 100 | 1 | |a Smith, Patrick D., |e author. | |
| 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. | ||
| 590 | |a eBooks on EBSCOhost |b EBSCO eBook Subscription Academic Collection - Worldwide | ||
| 590 | |a ProQuest Ebook Central |b Ebook Central Academic Complete | ||
| 650 | 0 | |a Machine learning. | |
| 650 | 0 | |a Artificial intelligence. | |
| 650 | 2 | |a Artificial Intelligence | |
| 650 | 2 | |a Machine Learning | |
| 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 |
| 856 | 4 | 0 | |u https://ebookcentral.uam.elogim.com/lib/uam-ebooks/detail.action?docID=5607070 |z Texto completo |
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