Hands-on transfer learning with Python :: implement advanced deep learning and neural network models using TensorFlow and Keras /
The purpose of this book is two-fold, we focus on detailed coverage of deep learning and transfer learning, comparing and contrasting the two with easy-to-follow concepts and examples. The second area of focus is on real-world examples and research problems using TensorFlow, Keras and Python ecosyst...
Gespeichert in:
| Hauptverfasser: | , , |
|---|---|
| Format: | Elektronisch E-Book |
| Sprache: | English |
| Veröffentlicht: |
Birmingham, UK :
Packt Publishing,
2018.
|
| Schlagworte: | |
| Online-Zugang: | DE-862 DE-863 |
| Zusammenfassung: | The purpose of this book is two-fold, we focus on detailed coverage of deep learning and transfer learning, comparing and contrasting the two with easy-to-follow concepts and examples. The second area of focus is on real-world examples and research problems using TensorFlow, Keras and Python ecosystem with hands-on examples. |
| Beschreibung: | 1 online resource (1 volume) : illustrations |
| ISBN: | 9781788839051 1788839056 |
Internformat
MARC
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| 505 | 0 | |a Cover; Title Page; Copyright and Credits; Dedication; Packt Upsell; Foreword; Contributors; Table of Contents; Preface; Chapter 1: Machine Learning Fundamentals; Why ML?; Formal definition; Shallow and deep learning; ML techniques; Supervised learning; Classification; Regression; Unsupervised learning; Clustering; Dimensionality reduction; Association rule mining; Anomaly detection; CRISP-DM; Business understanding; Data understanding; Data preparation; Modeling; Evaluation; Deployment; Standard ML workflow; Data retrieval; Data preparation; Exploratory data analysis | |
| 505 | 8 | |a Data processing and wranglingFeature engineering and extraction; Feature scaling and selection; Modeling; Model evaluation and tuning; Model evaluation; Bias variance trade-off; Bias; Variance; Trade-off; Underfitting; Overfitting; Generalization; Model tuning; Deployment and monitoring; Exploratory data analysis; Feature extraction and engineering; Feature engineering strategies; Working with numerical data; Working with categorical data; Working with image data; Deep learning based automated feature extraction; Working with text data; Text preprocessing; Feature engineering | |
| 505 | 8 | |a Feature selectionSummary; Chapter 2: Deep Learning Essentials; What is deep learning?; Deep learning frameworks; Setting up a cloud-based deep learning environment with GPU support; Choosing a cloud provider; Setting up your virtual server; Configuring your virtual server; Installing and updating deep learning dependencies ; Accessing your deep learning cloud environment; Validating GPU-enablement on your deep learning environment; Setting up a robust, on-premise deep learning environment with GPU support; Neural network basics; A simple linear neuron; Gradient-based optimization | |
| 505 | 8 | |a The Jacobian and Hessian matricesChain rule of derivatives; Stochastic Gradient Descent; Non-linear neural units; Learning a simple non-linear unit -- logistic unit; Loss functions; Data representations; Tensor examples; Tensor operations; Multilayered neural networks; Backprop -- training deep neural networks; Challenges in neural network learning; Ill-conditioning; Local minima and saddle points ; Cliffs and exploding gradients; Initialization -- bad correspondence between the local and global structure of the objective; Inexact gradients; Initialization of model parameters | |
| 505 | 8 | |a Initialization heuristicsImprovements of SGD; The momentum method; Nesterov momentum; Adaptive learning rate -- separate for each connection; AdaGrad; RMSprop; Adam; Overfitting and underfitting in neural networks; Model capacity; How to avoid overfitting -- regularization; Weight-sharing; Weight-decay ; Early stopping; Dropout; Batch normalization; Do we need more data?; Hyperparameters of the neural network; Automatic hyperparameter tuning; Grid search; Summary; Chapter 3: Understanding Deep Learning Architectures; Neural network architecture; Why different architectures are needed | |
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| contents | Cover; Title Page; Copyright and Credits; Dedication; Packt Upsell; Foreword; Contributors; Table of Contents; Preface; Chapter 1: Machine Learning Fundamentals; Why ML?; Formal definition; Shallow and deep learning; ML techniques; Supervised learning; Classification; Regression; Unsupervised learning; Clustering; Dimensionality reduction; Association rule mining; Anomaly detection; CRISP-DM; Business understanding; Data understanding; Data preparation; Modeling; Evaluation; Deployment; Standard ML workflow; Data retrieval; Data preparation; Exploratory data analysis Data processing and wranglingFeature engineering and extraction; Feature scaling and selection; Modeling; Model evaluation and tuning; Model evaluation; Bias variance trade-off; Bias; Variance; Trade-off; Underfitting; Overfitting; Generalization; Model tuning; Deployment and monitoring; Exploratory data analysis; Feature extraction and engineering; Feature engineering strategies; Working with numerical data; Working with categorical data; Working with image data; Deep learning based automated feature extraction; Working with text data; Text preprocessing; Feature engineering Feature selectionSummary; Chapter 2: Deep Learning Essentials; What is deep learning?; Deep learning frameworks; Setting up a cloud-based deep learning environment with GPU support; Choosing a cloud provider; Setting up your virtual server; Configuring your virtual server; Installing and updating deep learning dependencies ; Accessing your deep learning cloud environment; Validating GPU-enablement on your deep learning environment; Setting up a robust, on-premise deep learning environment with GPU support; Neural network basics; A simple linear neuron; Gradient-based optimization The Jacobian and Hessian matricesChain rule of derivatives; Stochastic Gradient Descent; Non-linear neural units; Learning a simple non-linear unit -- logistic unit; Loss functions; Data representations; Tensor examples; Tensor operations; Multilayered neural networks; Backprop -- training deep neural networks; Challenges in neural network learning; Ill-conditioning; Local minima and saddle points ; Cliffs and exploding gradients; Initialization -- bad correspondence between the local and global structure of the objective; Inexact gradients; Initialization of model parameters Initialization heuristicsImprovements of SGD; The momentum method; Nesterov momentum; Adaptive learning rate -- separate for each connection; AdaGrad; RMSprop; Adam; Overfitting and underfitting in neural networks; Model capacity; How to avoid overfitting -- regularization; Weight-sharing; Weight-decay ; Early stopping; Dropout; Batch normalization; Do we need more data?; Hyperparameters of the neural network; Automatic hyperparameter tuning; Grid search; Summary; Chapter 3: Understanding Deep Learning Architectures; Neural network architecture; Why different architectures are needed |
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| dewey-hundreds | 000 - Computer science, information, general works |
| dewey-ones | 005 - Computer programming, programs, data, security |
| dewey-raw | 005.133 |
| dewey-search | 005.133 |
| dewey-sort | 15.133 |
| dewey-tens | 000 - Computer science, information, general works |
| discipline | Informatik |
| format | Electronic eBook |
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| indexdate | 2025-04-11T08:46:33Z |
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| publisher | Packt Publishing, |
| record_format | marc |
| spelling | Sarkar, Dipanjan, author. http://id.loc.gov/authorities/names/no2017023286 Hands-on transfer learning with Python : implement advanced deep learning and neural network models using TensorFlow and Keras / Dipanjan Sarkar, Raghav Bali, Tamoghna Ghosh. Birmingham, UK : Packt Publishing, 2018. 1 online resource (1 volume) : illustrations text txt rdacontent computer c rdamedia online resource cr rdacarrier Online resource; title from title page (Safari, viewed October 1, 2018). Cover; Title Page; Copyright and Credits; Dedication; Packt Upsell; Foreword; Contributors; Table of Contents; Preface; Chapter 1: Machine Learning Fundamentals; Why ML?; Formal definition; Shallow and deep learning; ML techniques; Supervised learning; Classification; Regression; Unsupervised learning; Clustering; Dimensionality reduction; Association rule mining; Anomaly detection; CRISP-DM; Business understanding; Data understanding; Data preparation; Modeling; Evaluation; Deployment; Standard ML workflow; Data retrieval; Data preparation; Exploratory data analysis Data processing and wranglingFeature engineering and extraction; Feature scaling and selection; Modeling; Model evaluation and tuning; Model evaluation; Bias variance trade-off; Bias; Variance; Trade-off; Underfitting; Overfitting; Generalization; Model tuning; Deployment and monitoring; Exploratory data analysis; Feature extraction and engineering; Feature engineering strategies; Working with numerical data; Working with categorical data; Working with image data; Deep learning based automated feature extraction; Working with text data; Text preprocessing; Feature engineering Feature selectionSummary; Chapter 2: Deep Learning Essentials; What is deep learning?; Deep learning frameworks; Setting up a cloud-based deep learning environment with GPU support; Choosing a cloud provider; Setting up your virtual server; Configuring your virtual server; Installing and updating deep learning dependencies ; Accessing your deep learning cloud environment; Validating GPU-enablement on your deep learning environment; Setting up a robust, on-premise deep learning environment with GPU support; Neural network basics; A simple linear neuron; Gradient-based optimization The Jacobian and Hessian matricesChain rule of derivatives; Stochastic Gradient Descent; Non-linear neural units; Learning a simple non-linear unit -- logistic unit; Loss functions; Data representations; Tensor examples; Tensor operations; Multilayered neural networks; Backprop -- training deep neural networks; Challenges in neural network learning; Ill-conditioning; Local minima and saddle points ; Cliffs and exploding gradients; Initialization -- bad correspondence between the local and global structure of the objective; Inexact gradients; Initialization of model parameters Initialization heuristicsImprovements of SGD; The momentum method; Nesterov momentum; Adaptive learning rate -- separate for each connection; AdaGrad; RMSprop; Adam; Overfitting and underfitting in neural networks; Model capacity; How to avoid overfitting -- regularization; Weight-sharing; Weight-decay ; Early stopping; Dropout; Batch normalization; Do we need more data?; Hyperparameters of the neural network; Automatic hyperparameter tuning; Grid search; Summary; Chapter 3: Understanding Deep Learning Architectures; Neural network architecture; Why different architectures are needed The purpose of this book is two-fold, we focus on detailed coverage of deep learning and transfer learning, comparing and contrasting the two with easy-to-follow concepts and examples. The second area of focus is on real-world examples and research problems using TensorFlow, Keras and Python ecosystem with hands-on examples. Python (Computer program language) http://id.loc.gov/authorities/subjects/sh96008834 Machine learning. http://id.loc.gov/authorities/subjects/sh85079324 Neural networks (Computer science) http://id.loc.gov/authorities/subjects/sh90001937 Neural Networks, Computer https://id.nlm.nih.gov/mesh/D016571 Machine Learning https://id.nlm.nih.gov/mesh/D000069550 Python (Langage de programmation) Apprentissage automatique. Réseaux neuronaux (Informatique) COMPUTER SCIENCE General. bisacsh Machine learning fast Neural networks (Computer science) fast Python (Computer program language) fast Bali, Raghav, author. http://id.loc.gov/authorities/names/no2018013221 Ghosh, Tamoghna, author. has work: Hands-on transfer learning with Python (Text) https://id.oclc.org/worldcat/entity/E39PCFJf6mhD4cdHdpcx7dGDYP https://id.oclc.org/worldcat/ontology/hasWork Print version: Sarkar, Dipanjan. Hands-On Transfer Learning with Python : Implement Advanced Deep Learning and Neural Network Models Using TensorFlow and Keras. Birmingham : Packt Publishing Ltd, ©2018 9781788831307 |
| spellingShingle | Sarkar, Dipanjan Bali, Raghav Ghosh, Tamoghna Hands-on transfer learning with Python : implement advanced deep learning and neural network models using TensorFlow and Keras / Cover; Title Page; Copyright and Credits; Dedication; Packt Upsell; Foreword; Contributors; Table of Contents; Preface; Chapter 1: Machine Learning Fundamentals; Why ML?; Formal definition; Shallow and deep learning; ML techniques; Supervised learning; Classification; Regression; Unsupervised learning; Clustering; Dimensionality reduction; Association rule mining; Anomaly detection; CRISP-DM; Business understanding; Data understanding; Data preparation; Modeling; Evaluation; Deployment; Standard ML workflow; Data retrieval; Data preparation; Exploratory data analysis Data processing and wranglingFeature engineering and extraction; Feature scaling and selection; Modeling; Model evaluation and tuning; Model evaluation; Bias variance trade-off; Bias; Variance; Trade-off; Underfitting; Overfitting; Generalization; Model tuning; Deployment and monitoring; Exploratory data analysis; Feature extraction and engineering; Feature engineering strategies; Working with numerical data; Working with categorical data; Working with image data; Deep learning based automated feature extraction; Working with text data; Text preprocessing; Feature engineering Feature selectionSummary; Chapter 2: Deep Learning Essentials; What is deep learning?; Deep learning frameworks; Setting up a cloud-based deep learning environment with GPU support; Choosing a cloud provider; Setting up your virtual server; Configuring your virtual server; Installing and updating deep learning dependencies ; Accessing your deep learning cloud environment; Validating GPU-enablement on your deep learning environment; Setting up a robust, on-premise deep learning environment with GPU support; Neural network basics; A simple linear neuron; Gradient-based optimization The Jacobian and Hessian matricesChain rule of derivatives; Stochastic Gradient Descent; Non-linear neural units; Learning a simple non-linear unit -- logistic unit; Loss functions; Data representations; Tensor examples; Tensor operations; Multilayered neural networks; Backprop -- training deep neural networks; Challenges in neural network learning; Ill-conditioning; Local minima and saddle points ; Cliffs and exploding gradients; Initialization -- bad correspondence between the local and global structure of the objective; Inexact gradients; Initialization of model parameters Initialization heuristicsImprovements of SGD; The momentum method; Nesterov momentum; Adaptive learning rate -- separate for each connection; AdaGrad; RMSprop; Adam; Overfitting and underfitting in neural networks; Model capacity; How to avoid overfitting -- regularization; Weight-sharing; Weight-decay ; Early stopping; Dropout; Batch normalization; Do we need more data?; Hyperparameters of the neural network; Automatic hyperparameter tuning; Grid search; Summary; Chapter 3: Understanding Deep Learning Architectures; Neural network architecture; Why different architectures are needed Python (Computer program language) http://id.loc.gov/authorities/subjects/sh96008834 Machine learning. http://id.loc.gov/authorities/subjects/sh85079324 Neural networks (Computer science) http://id.loc.gov/authorities/subjects/sh90001937 Neural Networks, Computer https://id.nlm.nih.gov/mesh/D016571 Machine Learning https://id.nlm.nih.gov/mesh/D000069550 Python (Langage de programmation) Apprentissage automatique. Réseaux neuronaux (Informatique) COMPUTER SCIENCE General. bisacsh Machine learning fast Neural networks (Computer science) fast Python (Computer program language) fast |
| subject_GND | http://id.loc.gov/authorities/subjects/sh96008834 http://id.loc.gov/authorities/subjects/sh85079324 http://id.loc.gov/authorities/subjects/sh90001937 https://id.nlm.nih.gov/mesh/D016571 https://id.nlm.nih.gov/mesh/D000069550 |
| title | Hands-on transfer learning with Python : implement advanced deep learning and neural network models using TensorFlow and Keras / |
| title_auth | Hands-on transfer learning with Python : implement advanced deep learning and neural network models using TensorFlow and Keras / |
| title_exact_search | Hands-on transfer learning with Python : implement advanced deep learning and neural network models using TensorFlow and Keras / |
| title_full | Hands-on transfer learning with Python : implement advanced deep learning and neural network models using TensorFlow and Keras / Dipanjan Sarkar, Raghav Bali, Tamoghna Ghosh. |
| title_fullStr | Hands-on transfer learning with Python : implement advanced deep learning and neural network models using TensorFlow and Keras / Dipanjan Sarkar, Raghav Bali, Tamoghna Ghosh. |
| title_full_unstemmed | Hands-on transfer learning with Python : implement advanced deep learning and neural network models using TensorFlow and Keras / Dipanjan Sarkar, Raghav Bali, Tamoghna Ghosh. |
| title_short | Hands-on transfer learning with Python : |
| title_sort | hands on transfer learning with python implement advanced deep learning and neural network models using tensorflow and keras |
| title_sub | implement advanced deep learning and neural network models using TensorFlow and Keras / |
| topic | Python (Computer program language) http://id.loc.gov/authorities/subjects/sh96008834 Machine learning. http://id.loc.gov/authorities/subjects/sh85079324 Neural networks (Computer science) http://id.loc.gov/authorities/subjects/sh90001937 Neural Networks, Computer https://id.nlm.nih.gov/mesh/D016571 Machine Learning https://id.nlm.nih.gov/mesh/D000069550 Python (Langage de programmation) Apprentissage automatique. Réseaux neuronaux (Informatique) COMPUTER SCIENCE General. bisacsh Machine learning fast Neural networks (Computer science) fast Python (Computer program language) fast |
| topic_facet | Python (Computer program language) Machine learning. Neural networks (Computer science) Neural Networks, Computer Machine Learning Python (Langage de programmation) Apprentissage automatique. Réseaux neuronaux (Informatique) COMPUTER SCIENCE General. Machine learning |
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