Deep Learning with TensorFlow :: Explore neural networks and build intelligent systems with Python, 2nd Edition.
Compliant with TensorFlow 1.7, this book introduces the core concepts of deep learning. Get implementation and research details on cutting-edge architectures and apply advanced concepts to your own projects. Develop your knowledge of deep neural networks through hands-on model building and examples...
Gespeichert in:
| 1. Verfasser: | |
|---|---|
| Weitere Verfasser: | |
| Format: | Elektronisch E-Book |
| Sprache: | English |
| Veröffentlicht: |
Birmingham :
Packt Publishing,
2018.
|
| Ausgabe: | 2nd ed. |
| Schlagworte: | |
| Online-Zugang: | Volltext |
| Zusammenfassung: | Compliant with TensorFlow 1.7, this book introduces the core concepts of deep learning. Get implementation and research details on cutting-edge architectures and apply advanced concepts to your own projects. Develop your knowledge of deep neural networks through hands-on model building and examples of real-world data collection. |
| Beschreibung: | How does an autoencoder work? |
| Beschreibung: | 1 online resource (483 pages) |
| ISBN: | 9781788831833 1788831837 1788831101 9781788831109 |
Internformat
MARC
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| 245 | 1 | 0 | |a Deep Learning with TensorFlow : |b Explore neural networks and build intelligent systems with Python, 2nd Edition. |
| 250 | |a 2nd ed. | ||
| 260 | |a Birmingham : |b Packt Publishing, |c 2018. | ||
| 300 | |a 1 online resource (483 pages) | ||
| 336 | |a text |b txt |2 rdacontent | ||
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| 505 | 0 | |a Cover; Copyright; Packt Upsell; Contributors; Table of Contents; Preface; Chapter 1: Getting Started with Deep Learning; A soft introduction to machine learning; Supervised learning; Unbalanced data; Unsupervised learning; Reinforcement learning; What is deep learning?; Artificial neural networks; The biological neurons; The artificial neuron; How does an ANN learn?; ANNs and the backpropagation algorithm; Weight optimization; Stochastic gradient descent; Neural network architectures; Deep Neural Networks (DNNs); Multilayer perceptron; Deep Belief Networks (DBNs). | |
| 505 | 8 | |a Convolutional Neural Networks (CNNs)AutoEncoders; Recurrent Neural Networks (RNNs); Emergent architectures; Deep learning frameworks; Summary; Chapter 2: A First Look at TensorFlow; A general overview of TensorFlow; What's new in TensorFlow v1.6?; Nvidia GPU support optimized; Introducing TensorFlow Lite; Eager execution; Optimized Accelerated Linear Algebra (XLA); Installing and configuring TensorFlow; TensorFlow computational graph; TensorFlow code structure; Eager execution with TensorFlow; Data model in TensorFlow; Tensor; Rank and shape; Data type; Variables; Fetches. | |
| 505 | 8 | |a Feeds and placeholdersVisualizing computations through TensorBoard; How does TensorBoard work?; Linear regression and beyond; Linear regression revisited for a real dataset; Summary; Chapter 3: Feed-Forward Neural Networks with TensorFlow; Feed-forward neural networks (FFNNs); Feed-forward and backpropagation; Weights and biases; Activation functions; Using sigmoid; Using tanh; Using ReLU; Using softmax; Implementing a feed-forward neural network; Exploring the MNIST dataset; Softmax classifier; Implementing a multilayer perceptron (MLP); Training an MLP; Using MLPs; Dataset description. | |
| 505 | 8 | |a PreprocessingA TensorFlow implementation of MLP for client-subscription assessment; Deep Belief Networks (DBNs); Restricted Boltzmann Machines (RBMs); Construction of a simple DBN; Unsupervised pre-training; Supervised fine-tuning; Implementing a DBN with TensorFlow for client-subscription assessment; Tuning hyperparameters and advanced FFNNs; Tuning FFNN hyperparameters; Number of hidden layers; Number of neurons per hidden layer; Weight and biases initialization; Selecting the most suitable optimizer; GridSearch and randomized search for hyperparameters tuning; Regularization. | |
| 505 | 8 | |a Dropout optimizationSummary; Chapter 4: Convolutional Neural Networks; Main concepts of CNNs; CNNs in action; LeNet5; Implementing a LeNet-5 step by step; AlexNet; Transfer learning; Pretrained AlexNet; Dataset preparation; Fine-tuning implementation; VGG; Artistic style learning with VGG-19; Input images; Content extractor and loss; Style extractor and loss; Merger and total loss; Training; Inception-v3; Exploring Inception with TensorFlow; Emotion recognition with CNNs; Testing the model on your own image; Source code; Summary; Chapter 5: Optimizing TensorFlow Autoencoders. | |
| 500 | |a How does an autoencoder work? | ||
| 520 | |a Compliant with TensorFlow 1.7, this book introduces the core concepts of deep learning. Get implementation and research details on cutting-edge architectures and apply advanced concepts to your own projects. Develop your knowledge of deep neural networks through hands-on model building and examples of real-world data collection. | ||
| 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 | 0 | |a Python (Computer program language) |0 http://id.loc.gov/authorities/subjects/sh96008834 | |
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| contents | Cover; Copyright; Packt Upsell; Contributors; Table of Contents; Preface; Chapter 1: Getting Started with Deep Learning; A soft introduction to machine learning; Supervised learning; Unbalanced data; Unsupervised learning; Reinforcement learning; What is deep learning?; Artificial neural networks; The biological neurons; The artificial neuron; How does an ANN learn?; ANNs and the backpropagation algorithm; Weight optimization; Stochastic gradient descent; Neural network architectures; Deep Neural Networks (DNNs); Multilayer perceptron; Deep Belief Networks (DBNs). Convolutional Neural Networks (CNNs)AutoEncoders; Recurrent Neural Networks (RNNs); Emergent architectures; Deep learning frameworks; Summary; Chapter 2: A First Look at TensorFlow; A general overview of TensorFlow; What's new in TensorFlow v1.6?; Nvidia GPU support optimized; Introducing TensorFlow Lite; Eager execution; Optimized Accelerated Linear Algebra (XLA); Installing and configuring TensorFlow; TensorFlow computational graph; TensorFlow code structure; Eager execution with TensorFlow; Data model in TensorFlow; Tensor; Rank and shape; Data type; Variables; Fetches. Feeds and placeholdersVisualizing computations through TensorBoard; How does TensorBoard work?; Linear regression and beyond; Linear regression revisited for a real dataset; Summary; Chapter 3: Feed-Forward Neural Networks with TensorFlow; Feed-forward neural networks (FFNNs); Feed-forward and backpropagation; Weights and biases; Activation functions; Using sigmoid; Using tanh; Using ReLU; Using softmax; Implementing a feed-forward neural network; Exploring the MNIST dataset; Softmax classifier; Implementing a multilayer perceptron (MLP); Training an MLP; Using MLPs; Dataset description. PreprocessingA TensorFlow implementation of MLP for client-subscription assessment; Deep Belief Networks (DBNs); Restricted Boltzmann Machines (RBMs); Construction of a simple DBN; Unsupervised pre-training; Supervised fine-tuning; Implementing a DBN with TensorFlow for client-subscription assessment; Tuning hyperparameters and advanced FFNNs; Tuning FFNN hyperparameters; Number of hidden layers; Number of neurons per hidden layer; Weight and biases initialization; Selecting the most suitable optimizer; GridSearch and randomized search for hyperparameters tuning; Regularization. Dropout optimizationSummary; Chapter 4: Convolutional Neural Networks; Main concepts of CNNs; CNNs in action; LeNet5; Implementing a LeNet-5 step by step; AlexNet; Transfer learning; Pretrained AlexNet; Dataset preparation; Fine-tuning implementation; VGG; Artistic style learning with VGG-19; Input images; Content extractor and loss; Style extractor and loss; Merger and total loss; Training; Inception-v3; Exploring Inception with TensorFlow; Emotion recognition with CNNs; Testing the model on your own image; Source code; Summary; Chapter 5: Optimizing TensorFlow Autoencoders. |
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| discipline | Informatik |
| edition | 2nd ed. |
| format | Electronic eBook |
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| publisher | Packt Publishing, |
| record_format | marc |
| spelling | Zaccone, Giancarlo. Deep Learning with TensorFlow : Explore neural networks and build intelligent systems with Python, 2nd Edition. 2nd ed. Birmingham : Packt Publishing, 2018. 1 online resource (483 pages) text txt rdacontent computer c rdamedia online resource cr rdacarrier Print version record. Cover; Copyright; Packt Upsell; Contributors; Table of Contents; Preface; Chapter 1: Getting Started with Deep Learning; A soft introduction to machine learning; Supervised learning; Unbalanced data; Unsupervised learning; Reinforcement learning; What is deep learning?; Artificial neural networks; The biological neurons; The artificial neuron; How does an ANN learn?; ANNs and the backpropagation algorithm; Weight optimization; Stochastic gradient descent; Neural network architectures; Deep Neural Networks (DNNs); Multilayer perceptron; Deep Belief Networks (DBNs). Convolutional Neural Networks (CNNs)AutoEncoders; Recurrent Neural Networks (RNNs); Emergent architectures; Deep learning frameworks; Summary; Chapter 2: A First Look at TensorFlow; A general overview of TensorFlow; What's new in TensorFlow v1.6?; Nvidia GPU support optimized; Introducing TensorFlow Lite; Eager execution; Optimized Accelerated Linear Algebra (XLA); Installing and configuring TensorFlow; TensorFlow computational graph; TensorFlow code structure; Eager execution with TensorFlow; Data model in TensorFlow; Tensor; Rank and shape; Data type; Variables; Fetches. Feeds and placeholdersVisualizing computations through TensorBoard; How does TensorBoard work?; Linear regression and beyond; Linear regression revisited for a real dataset; Summary; Chapter 3: Feed-Forward Neural Networks with TensorFlow; Feed-forward neural networks (FFNNs); Feed-forward and backpropagation; Weights and biases; Activation functions; Using sigmoid; Using tanh; Using ReLU; Using softmax; Implementing a feed-forward neural network; Exploring the MNIST dataset; Softmax classifier; Implementing a multilayer perceptron (MLP); Training an MLP; Using MLPs; Dataset description. PreprocessingA TensorFlow implementation of MLP for client-subscription assessment; Deep Belief Networks (DBNs); Restricted Boltzmann Machines (RBMs); Construction of a simple DBN; Unsupervised pre-training; Supervised fine-tuning; Implementing a DBN with TensorFlow for client-subscription assessment; Tuning hyperparameters and advanced FFNNs; Tuning FFNN hyperparameters; Number of hidden layers; Number of neurons per hidden layer; Weight and biases initialization; Selecting the most suitable optimizer; GridSearch and randomized search for hyperparameters tuning; Regularization. Dropout optimizationSummary; Chapter 4: Convolutional Neural Networks; Main concepts of CNNs; CNNs in action; LeNet5; Implementing a LeNet-5 step by step; AlexNet; Transfer learning; Pretrained AlexNet; Dataset preparation; Fine-tuning implementation; VGG; Artistic style learning with VGG-19; Input images; Content extractor and loss; Style extractor and loss; Merger and total loss; Training; Inception-v3; Exploring Inception with TensorFlow; Emotion recognition with CNNs; Testing the model on your own image; Source code; Summary; Chapter 5: Optimizing TensorFlow Autoencoders. How does an autoencoder work? Compliant with TensorFlow 1.7, this book introduces the core concepts of deep learning. Get implementation and research details on cutting-edge architectures and apply advanced concepts to your own projects. Develop your knowledge of deep neural networks through hands-on model building and examples of real-world data collection. Machine learning. http://id.loc.gov/authorities/subjects/sh85079324 Artificial intelligence. http://id.loc.gov/authorities/subjects/sh85008180 Python (Computer program language) http://id.loc.gov/authorities/subjects/sh96008834 Artificial Intelligence https://id.nlm.nih.gov/mesh/D001185 Machine Learning https://id.nlm.nih.gov/mesh/D000069550 Apprentissage automatique. Intelligence artificielle. Python (Langage de programmation) artificial intelligence. aat Web programming. bicssc Programming & scripting languages: general. bicssc COMPUTERS General. bisacsh Artificial intelligence fast Machine learning fast Python (Computer program language) fast Karim, Md. Rezaul. has work: Deep learning with TensorFlow (Text) https://id.oclc.org/worldcat/entity/E39PCGBwq3xDhmKVPPgpJgH3PP https://id.oclc.org/worldcat/ontology/hasWork Print version: Zaccone, Giancarlo. Deep Learning with TensorFlow : Explore neural networks and build intelligent systems with Python, 2nd Edition. Birmingham : Packt Publishing, ©2018 FWS01 ZDB-4-EBA FWS_PDA_EBA https://search.ebscohost.com/login.aspx?direct=true&scope=site&db=nlebk&AN=1789473 Volltext |
| spellingShingle | Zaccone, Giancarlo Deep Learning with TensorFlow : Explore neural networks and build intelligent systems with Python, 2nd Edition. Cover; Copyright; Packt Upsell; Contributors; Table of Contents; Preface; Chapter 1: Getting Started with Deep Learning; A soft introduction to machine learning; Supervised learning; Unbalanced data; Unsupervised learning; Reinforcement learning; What is deep learning?; Artificial neural networks; The biological neurons; The artificial neuron; How does an ANN learn?; ANNs and the backpropagation algorithm; Weight optimization; Stochastic gradient descent; Neural network architectures; Deep Neural Networks (DNNs); Multilayer perceptron; Deep Belief Networks (DBNs). Convolutional Neural Networks (CNNs)AutoEncoders; Recurrent Neural Networks (RNNs); Emergent architectures; Deep learning frameworks; Summary; Chapter 2: A First Look at TensorFlow; A general overview of TensorFlow; What's new in TensorFlow v1.6?; Nvidia GPU support optimized; Introducing TensorFlow Lite; Eager execution; Optimized Accelerated Linear Algebra (XLA); Installing and configuring TensorFlow; TensorFlow computational graph; TensorFlow code structure; Eager execution with TensorFlow; Data model in TensorFlow; Tensor; Rank and shape; Data type; Variables; Fetches. Feeds and placeholdersVisualizing computations through TensorBoard; How does TensorBoard work?; Linear regression and beyond; Linear regression revisited for a real dataset; Summary; Chapter 3: Feed-Forward Neural Networks with TensorFlow; Feed-forward neural networks (FFNNs); Feed-forward and backpropagation; Weights and biases; Activation functions; Using sigmoid; Using tanh; Using ReLU; Using softmax; Implementing a feed-forward neural network; Exploring the MNIST dataset; Softmax classifier; Implementing a multilayer perceptron (MLP); Training an MLP; Using MLPs; Dataset description. PreprocessingA TensorFlow implementation of MLP for client-subscription assessment; Deep Belief Networks (DBNs); Restricted Boltzmann Machines (RBMs); Construction of a simple DBN; Unsupervised pre-training; Supervised fine-tuning; Implementing a DBN with TensorFlow for client-subscription assessment; Tuning hyperparameters and advanced FFNNs; Tuning FFNN hyperparameters; Number of hidden layers; Number of neurons per hidden layer; Weight and biases initialization; Selecting the most suitable optimizer; GridSearch and randomized search for hyperparameters tuning; Regularization. Dropout optimizationSummary; Chapter 4: Convolutional Neural Networks; Main concepts of CNNs; CNNs in action; LeNet5; Implementing a LeNet-5 step by step; AlexNet; Transfer learning; Pretrained AlexNet; Dataset preparation; Fine-tuning implementation; VGG; Artistic style learning with VGG-19; Input images; Content extractor and loss; Style extractor and loss; Merger and total loss; Training; Inception-v3; Exploring Inception with TensorFlow; Emotion recognition with CNNs; Testing the model on your own image; Source code; Summary; Chapter 5: Optimizing TensorFlow Autoencoders. Machine learning. http://id.loc.gov/authorities/subjects/sh85079324 Artificial intelligence. http://id.loc.gov/authorities/subjects/sh85008180 Python (Computer program language) http://id.loc.gov/authorities/subjects/sh96008834 Artificial Intelligence https://id.nlm.nih.gov/mesh/D001185 Machine Learning https://id.nlm.nih.gov/mesh/D000069550 Apprentissage automatique. Intelligence artificielle. Python (Langage de programmation) artificial intelligence. aat Web programming. bicssc Programming & scripting languages: general. bicssc COMPUTERS General. bisacsh Artificial intelligence fast Machine learning fast Python (Computer program language) fast |
| subject_GND | http://id.loc.gov/authorities/subjects/sh85079324 http://id.loc.gov/authorities/subjects/sh85008180 http://id.loc.gov/authorities/subjects/sh96008834 https://id.nlm.nih.gov/mesh/D001185 https://id.nlm.nih.gov/mesh/D000069550 |
| title | Deep Learning with TensorFlow : Explore neural networks and build intelligent systems with Python, 2nd Edition. |
| title_auth | Deep Learning with TensorFlow : Explore neural networks and build intelligent systems with Python, 2nd Edition. |
| title_exact_search | Deep Learning with TensorFlow : Explore neural networks and build intelligent systems with Python, 2nd Edition. |
| title_full | Deep Learning with TensorFlow : Explore neural networks and build intelligent systems with Python, 2nd Edition. |
| title_fullStr | Deep Learning with TensorFlow : Explore neural networks and build intelligent systems with Python, 2nd Edition. |
| title_full_unstemmed | Deep Learning with TensorFlow : Explore neural networks and build intelligent systems with Python, 2nd Edition. |
| title_short | Deep Learning with TensorFlow : |
| title_sort | deep learning with tensorflow explore neural networks and build intelligent systems with python 2nd edition |
| title_sub | Explore neural networks and build intelligent systems with Python, 2nd Edition. |
| topic | Machine learning. http://id.loc.gov/authorities/subjects/sh85079324 Artificial intelligence. http://id.loc.gov/authorities/subjects/sh85008180 Python (Computer program language) http://id.loc.gov/authorities/subjects/sh96008834 Artificial Intelligence https://id.nlm.nih.gov/mesh/D001185 Machine Learning https://id.nlm.nih.gov/mesh/D000069550 Apprentissage automatique. Intelligence artificielle. Python (Langage de programmation) artificial intelligence. aat Web programming. bicssc Programming & scripting languages: general. bicssc COMPUTERS General. bisacsh Artificial intelligence fast Machine learning fast Python (Computer program language) fast |
| topic_facet | Machine learning. Artificial intelligence. Python (Computer program language) Artificial Intelligence Machine Learning Apprentissage automatique. Intelligence artificielle. Python (Langage de programmation) artificial intelligence. Web programming. Programming & scripting languages: general. COMPUTERS General. Artificial intelligence Machine learning |
| url | https://search.ebscohost.com/login.aspx?direct=true&scope=site&db=nlebk&AN=1789473 |
| work_keys_str_mv | AT zacconegiancarlo deeplearningwithtensorflowexploreneuralnetworksandbuildintelligentsystemswithpython2ndedition AT karimmdrezaul deeplearningwithtensorflowexploreneuralnetworksandbuildintelligentsystemswithpython2ndedition |