If we want to learn about the hierarchical representation of sequential data, a stack of LSTM layers can be used. Each LSTM layer outputs a sequence of vectors rather than a single vector for each item of the sequence, which will be used as an input to a subsequent LSTM layer. This hierarchy of hidden layers enables a more complex representation of our sequential data. Stacked LSTM models can be used for modeling complex multivariate time series data.
Stacked LSTMs
by Tamoghna Ghosh, Raghav Bali, Dipanjan Sarkar
Hands-On Transfer Learning with Python
- Title Page
- Copyright and Credits
- Dedication
- Packt Upsell
- Foreword
- Contributors
- Preface
- Machine Learning Fundamentals
- Deep Learning Essentials
- What is deep learning?
- Deep learning frameworks
- Setting up a cloud-based deep learning environment with GPU support
- 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 matrices
- Chain rule of derivatives
- Stochastic Gradient Descent
- Non-linear neural units
- Learning a simple non-linear unit – logistic unit
- Loss functions
- Data representations
- Multilayered neural networks
- Backprop – training deep neural networks
- Challenges in neural network learning
- Initialization of model parameters
- Improvements of SGD
- Overfitting and underfitting in neural networks
- Hyperparameters of the neural network
- Summary
- Understanding Deep Learning Architectures
- Neural network architecture
- Various architectures
- Summary
- Transfer Learning Fundamentals
- Unleashing the Power of Transfer Learning
- Image Recognition and Classification
- Text Document Categorization
- Text categorization
- Word representations
- CNN document model
- Building a review sentiment classifier
- What has embedding changed most?
- Transfer learning – application to the IMDB dataset
- Training on the full IMDB dataset with Word2vec embeddings
- Creating document summaries with CNN model
- Multiclass classification with the CNN model
- Visualizing document embeddings
- Summary
- Audio Event Identification and Classification
- DeepDream
- Style Transfer
- Automated Image Caption Generator
- Understanding image captioning
- Formulating our objective
- Understanding the data
- Approach to automated image captioning
- Image feature extraction with transfer learning
- Building a vocabulary for our captions
- Building an image caption dataset generator
- Building our image language encoder-decoder deep learning model
- Training our image captioning deep learning model
- Evaluating our image captioning deep learning model
- Automated image captioning in action!
- Summary
- Image Colorization
- Other Books You May Enjoy
Stacked LSTMs
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