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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...
| Clasificación: | Libro Electrónico |
|---|---|
| Autores principales: | , , |
| Formato: | Electrónico eBook |
| Idioma: | Inglés |
| Publicado: |
Birmingham, UK :
Packt Publishing,
2018.
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| Temas: | |
| Acceso en línea: | Texto completo (Requiere registro previo con correo institucional) |
Tabla de Contenidos:
- 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