| Description |
1 online resource (1 volume) : illustrations |
| 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 |
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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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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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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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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 |
| Summary |
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. |
| Subject |
Python (Computer program language)
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Machine learning.
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Neural networks (Computer science)
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Neural Networks, Computer |
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Machine Learning |
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Python (Langage de programmation) |
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Apprentissage automatique. |
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Réseaux neuronaux (Informatique) |
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Machine learning |
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Neural networks (Computer science) |
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Python (Computer program language) |
| Added Author |
Bali, Raghav, author.
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Ghosh, Tamoghna, author.
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| Other Form: |
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 |
| ISBN |
9781788839051 (electronic bk.) |
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1788839056 (electronic bk.) |
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