Neural network programming with tensorflow unleash the power of tensorflow to train efficient neural networks
Neural Networks and their implementation decoded with TensorFlow About This Book Develop a strong background in neural network programming from scratch, using the popular Tensorflow library. Use Tensorflow to implement different kinds of neural networks ? from simple feedforward neural networks to m...
| Otros Autores: | , |
|---|---|
| Formato: | Libro electrónico |
| Idioma: | Inglés |
| Publicado: |
Birmingham, England ; Mumbai, [India] :
Packt
2017.
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| Edición: | 1st edition |
| Materias: | |
| Ver en Biblioteca Universitat Ramon Llull: | https://discovery.url.edu/permalink/34CSUC_URL/1im36ta/alma991009630389406719 |
Tabla de Contenidos:
- Cover
- Copyright
- Credits
- About the Authors
- About the Reviewer
- www.PacktPub.com
- Customer Feedback
- Table of Contents
- Preface
- Chapter 1: Maths for Neural Networks
- Understanding linear algebra
- Environment setup
- Setting up the Python environment in Pycharm
- Linear algebra structures
- Scalars, vectors, and matrices
- Tensors
- Operations
- Vectors
- Matrices
- Matrix multiplication
- Trace operator
- Matrix transpose
- Matrix diagonals
- Identity matrix
- Inverse matrix
- Solving linear equations
- Singular value decomposition
- Eigenvalue decomposition
- Principal Component Analysis
- Calculus
- Gradient
- Hessian
- Determinant
- Optimization
- Optimizers
- Summary
- Chapter 2: Deep Feedforward Networks
- Defining feedforward networks
- Understanding backpropagation
- Implementing feedforward networks with TensorFlow
- Analyzing the Iris dataset
- Code execution
- Implementing feedforward networks with images
- Analyzing the effect of activation functions on the feedforward networks accuracy
- Summary
- Chapter 3: Optimization for Neural Networks
- What is optimization?
- Types of optimizers
- Gradient descent
- Different variants of gradient descent
- Algorithms to optimize gradient descent
- Which optimizer to choose
- Optimization with an example
- Summary
- Chapter 4: Convolutional Neural Networks
- An overview and the intuition of CNN
- Single Conv Layer Computation
- CNN in TensorFlow
- Image loading in TensorFlow
- Convolution operations
- Convolution on an image
- Strides
- Pooling
- Max pool
- Example code
- Average pool
- Image classification with convolutional networks
- Defining a tensor for input images and the first convolution layer
- Input tensor
- First convolution layer
- Second convolution layer
- Third convolution layer.
- Flatten the layer
- Fully connected layers
- Defining cost and optimizer
- Optimizer
- First epoch
- Plotting filters and their effects on an image
- Summary
- Chapter 5: Recurrent Neural Networks
- Introduction to RNNs
- RNN implementation
- Computational graph
- RNN implementation with TensorFlow
- Computational graph
- Introduction to long short term memory networks
- Life cycle of LSTM
- LSTM implementation
- Computational graph
- Sentiment analysis
- Word embeddings
- Sentiment analysis with an RNN
- Computational graph
- Summary
- Chapter 6: Generative Models
- Generative models
- Discriminative versus generative models
- Types of generative models
- Autoencoders
- GAN
- Sequence models
- GANs
- GAN with an example
- Types of GANs
- Vanilla GAN
- Conditional GAN
- Info GAN
- Wasserstein GAN
- Coupled GAN
- Summary
- Chapter 7: Deep Belief Networking
- Understanding deep belief networks
- DBN implementation
- Class initialization
- RBM class
- Pretraining the DBN
- Model training
- Predicting the label
- Finding the accuracy of the model
- DBN implementation for the MNIST dataset
- Loading the dataset
- Input parameters for a DBN with 256-Neuron RBM layers
- Output for a DBN with 256-neuron RBN layers
- Effect of the number of neurons in an RBM layer in a DBN
- An RBM layer with 512 neurons
- An&
- #160
- RBM layer with 128 neurons
- Comparing the accuracy metrics
- DBNs with two RBM layers
- Classifying the NotMNIST dataset with a DBN
- Summary
- Chapter 8: Autoencoders
- Autoencoder algorithms
- Under-complete autoencoders
- Dataset
- Basic autoencoders
- Autoencoder initialization
- AutoEncoder class
- Basic autoencoders with MNIST data
- Basic autoencoder plot of weights
- Basic autoencoder recreated images plot
- Basic autoencoder full code listing.
- Basic autoencoder summary
- Additive Gaussian Noise autoencoder
- Autoencoder class
- Additive Gaussian Autoencoder with the MNIST dataset
- Training the model
- Plotting the weights
- Plotting the reconstructed images
- Additive Gaussian autoencoder full code listing
- Comparing basic encoder costs with the Additive Gaussian Noise autoencoder
- Additive Gaussian Noise autoencoder summary
- Sparse autoencoder
- KL divergence
- KL divergence in TensorFlow
- Cost of a sparse autoencoder based on KL Divergence
- Complete code listing of&
- #160
- the sparse autoencoder
- Sparse autoencoder on MNIST data
- Comparing the Sparse encoder with&
- #160
- the Additive Gaussian Noise encoder
- Summary
- Chapter 9: Research in Neural Networks
- Avoiding overfitting in neural networks
- Problem statement
- Solution
- Results
- Large-scale video processing with neural networks
- Resolution improvements
- Feature histogram baselines
- Quantitative results
- Named entity recognition using a twisted neural network
- Example of a named entity recognition
- Defining Twinet
- Results
- Bidirectional RNNs
- BRNN on TIMIT dataset
- Summary
- Appendix: Getting started with TensorFlow
- Environment setup
- TensorFlow comparison with Numpy
- Computational graph
- Graph
- Session objects
- Variables
- Scope
- Data input
- Placeholders and feed dictionaries
- Auto differentiation
- TensorBoard
- Index.
