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210123 ||| eng |
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|a 9781788839051
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| 050 |
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4 |
|a QA76.73.P98
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| 100 |
1 |
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|a Sarkar, Dipanjan
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| 245 |
0 |
0 |
|a Hands-on transfer learning with Python
|b implement advanced deep learning and neural network models using TensorFlow and Keras
|c Dipanjan Sarkar, Raghav Bali, Tamoghna Ghosh
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| 260 |
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|a Birmingham, UK
|b Packt Publishing
|c 2018
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| 300 |
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|a 1 volume
|b illustrations
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| 505 |
0 |
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|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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| 505 |
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|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
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| 505 |
0 |
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|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
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| 505 |
0 |
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|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
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| 505 |
0 |
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|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
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| 653 |
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|a Réseaux neuronaux (Informatique)
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| 653 |
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|a Neural networks (Computer science) / fast
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| 653 |
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|a COMPUTER SCIENCE / General / bisacsh
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| 653 |
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|a Machine learning / http://id.loc.gov/authorities/subjects/sh85079324
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| 653 |
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|a Python (Computer program language) / fast
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| 653 |
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|a Python (Computer program language) / http://id.loc.gov/authorities/subjects/sh96008834
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| 653 |
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|a Neural Networks, Computer
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| 653 |
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|a Neural networks (Computer science) / http://id.loc.gov/authorities/subjects/sh90001937
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| 653 |
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|a Machine learning / fast
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| 653 |
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|a Apprentissage automatique
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| 653 |
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|a Python (Langage de programmation)
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| 653 |
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|a Machine Learning
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| 700 |
1 |
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|a Bali, Raghav
|e author
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| 700 |
1 |
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|a Ghosh, Tamoghna
|e author
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| 041 |
0 |
7 |
|a eng
|2 ISO 639-2
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| 989 |
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|b OREILLY
|a O'Reilly
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| 776 |
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|z 9781788839051
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| 776 |
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|z 1788839056
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| 776 |
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|z 9781788831307
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| 856 |
4 |
0 |
|u https://learning.oreilly.com/library/view/~/9781788831307/?ar
|x Verlag
|3 Volltext
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| 082 |
0 |
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|a 331
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| 082 |
0 |
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|a 005.133
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| 082 |
0 |
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|a 500
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| 520 |
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|a 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
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