TensorFlow 1.x deep learning cookbook over 90 unique recipes to solve artificial-intelligence driven problems with Python
Take the next step in implementing various common and not-so-common neural networks with Tensorflow 1.x About This Book Skill up and implement tricky neural networks using Google's TensorFlow 1.x An easy-to-follow guide that lets you explore reinforcement learning, GANs, autoencoders, multilaye...
| 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/alma991009630078706719 |
Tabla de Contenidos:
- Cover
- Copyright
- Credits
- About the Authors
- About the Reviewers
- www.PacktPub.com
- Customer Feedback
- Dedication
- Table of Contents
- Preface
- Chapter 1: TensorFlow - An Introduction
- Introduction
- Installing TensorFlow
- Getting ready
- How to do it...
- How it works...
- There's more...
- Hello world in TensorFlow
- How to do it...
- How it works...
- Understanding the TensorFlow program structure
- How to do it...
- How it works...
- There's more...
- Working with constants, variables, and placeholders
- How to do it...
- How it works...
- There's more...
- Performing matrix manipulations using TensorFlow
- How to do it...
- How it works...
- There's more...
- Using a data flow graph
- How to do it...
- Migrating from 0.x to 1.x
- How to do it...
- There's more...
- Using XLA to enhance computational performance
- Getting ready
- How to do it...
- Invoking CPU/GPU devices
- How to do it...
- How it works...
- TensorFlow for Deep Learning
- How to do it...
- There's more
- Different Python packages required for DNN-based problems
- How to do it...
- See also
- Chapter 2: Regression
- Introduction
- Choosing loss functions
- Getting ready
- How to do it...
- How it works...
- There's more...
- Optimizers in TensorFlow
- Getting ready
- How to do it...
- There's more...
- See also
- Reading from CSV files and preprocessing data
- Getting ready
- How to do it…
- There's more...
- House price estimation-simple linear regression
- Getting ready
- How to do it...
- How it works...
- There's more...
- House price estimation-multiple linear regression
- How to do it...
- How it works...
- There's more...
- Logistic regression on the MNIST dataset
- How to do it...
- How it works...
- See also
- Chapter 3: Neural Networks - Perceptron
- Introduction.
- Activation functions
- Getting ready
- How to do it...
- How it works...
- There's more...
- See also
- Single layer perceptron
- Getting ready
- How to do it...
- There's more...
- Calculating gradients of backpropagation algorithm
- Getting ready
- How to do it...
- How it works...
- There's more...
- See also
- MNIST classifier using MLP
- Getting ready
- How to do it...
- How it works...
- Function approximation using MLP-predicting Boston house prices
- Getting ready
- How to do it...
- How it works...
- There's more...
- Tuning hyperparameters
- How to do it...
- There's more...
- See also
- Higher-level APIs-Keras
- How to do it...
- There's more...
- See also
- Chapter 4: Convolutional Neural Networks
- Introduction
- Local receptive fields
- Shared weights and bias
- A mathematical example
- ConvNets in TensorFlow
- Pooling layers
- Max pooling
- Average pooling
- ConvNets summary
- Creating a ConvNet to classify handwritten MNIST numbers
- Getting ready
- How to do it...
- How it works...
- Creating a ConvNet to classify CIFAR-10
- Getting ready
- How to do it...
- How it works...
- There's more...
- Transferring style with VGG19 for image repainting
- Getting ready
- How to do it...
- How it works...
- There's more...
- Using a pretrained VGG16 net for transfer learning
- Getting ready
- How to do it...
- How it works...
- There's more...
- Creating a DeepDream network
- Getting ready
- How to do it...
- How it works...
- There's more...
- See also
- Chapter 5: Advanced Convolutional Neural Networks
- Introduction
- Creating a ConvNet for Sentiment Analysis
- Getting ready
- How to do it...
- How it works...
- There is more...
- Inspecting what filters a VGG pre-built network has learned
- Getting ready
- How to do it...
- How it works...
- There is more.
- Classifying images with VGGNet, ResNet, Inception, and Xception
- VGG16 and VGG19
- ResNet
- Inception
- Xception
- Getting ready
- How to do it...
- How it works...
- There is more...
- Recycling pre-built Deep Learning models for extracting features
- Getting ready
- How to do it...
- How it works...
- Very deep InceptionV3 Net used for Transfer Learning
- Getting ready
- How to do it...
- How it works...
- There is more...
- Generating music with dilated ConvNets, WaveNet, and NSynth
- Getting ready
- How to do it...
- How it works...
- There is more...
- Answering questions about images (Visual Q&
- A)
- How to do it...
- How it works...
- There is more...
- Classifying videos with pre-trained nets in six different ways
- How to do it...
- How it works...
- There is more...
- Chapter 6: Recurrent Neural Networks
- Introduction
- Vanishing and exploding gradients
- Long Short Term Memory (LSTM)
- Gated Recurrent Units (GRUs) and Peephole LSTM
- Operating on sequences of vectors
- Neural machine translation - training a seq2seq RNN
- Getting ready
- How to do it...
- How it works...
- Neural machine translation - inference on a seq2seq RNN
- How to do it...
- How it works...
- All you need is attention - another example of a seq2seq RNN
- How to do it...
- How it works...
- There's more...
- Learning to write as Shakespeare with RNNs
- How to do it...
- How it works...
- First iteration
- After a few iterations
- There's more...
- Learning to predict future Bitcoin value with RNNs
- How to do it...
- How it works...
- There's more...
- Many-to-one and many-to-many RNN examples
- How to do it...
- How it works...
- Chapter 7: Unsupervised Learning
- Introduction
- Principal component analysis
- Getting ready
- How to do it...
- How it works...
- There's more...
- See also.
- k-means clustering
- Getting ready
- How to do it...
- How it works...
- There's more...
- See also
- Self-organizing maps
- Getting ready
- How to do it...
- How it works...
- See also
- Restricted Boltzmann Machine
- Getting ready
- How to do it...
- How it works...
- See also
- Recommender system using RBM
- Getting ready
- How to do it...
- There's more...
- DBN for Emotion Detection
- Getting ready
- How to do it...
- How it works...
- There's more...
- Chapter 8: Autoencoders
- Introduction
- See Also
- Vanilla autoencoders
- Getting ready
- How to do it...
- How it works...
- There's more...
- Sparse autoencoder
- Getting Ready...
- How to do it...
- How it works...
- There's More...
- See Also
- Denoising autoencoder
- Getting Ready
- How to do it...
- See Also
- Convolutional autoencoders
- Getting Ready...
- How to do it...
- How it Works...
- There's More...
- See Also
- Stacked autoencoder
- Getting Ready
- How to do it...
- How it works...
- There's More...
- See Also
- Chapter 9: Reinforcement Learning
- Introduction
- Learning OpenAI Gym
- Getting ready
- How to do it...
- How it works...
- There's more...
- See also
- Implementing neural network agent to play Pac-Man
- Getting ready
- How to do it...
- Q learning to balance Cart-Pole
- Getting ready
- How to do it...
- There's more...
- See also
- Game of Atari using Deep Q Networks
- Getting ready
- How to do it...
- There's more...
- See also
- Policy gradients to play the game of Pong
- Getting ready
- How to do it...
- How it works...
- There's more...
- AlphaGo Zero
- See also
- Chapter 10: Mobile Computation
- Introduction
- TensorFlow, mobile, and the cloud
- Installing TensorFlow mobile for macOS and Android
- Getting ready
- How to do it...
- How it works...
- There's more.
- Playing with TensorFlow and Android examples
- Getting ready
- How to do it...
- How it works...
- Installing TensorFlow mobile for macOS and iPhone
- Getting ready
- How to do it...
- How it works...
- There's more...
- Optimizing a TensorFlow graph for mobile devices
- Getting ready
- How to do it...
- How it works...
- Profiling a TensorFlow graph for mobile devices
- Getting ready
- How to do it...
- How it works...
- Transforming a TensorFlow graph for mobile devices
- Getting ready
- How to do it...
- How it works...
- Chapter 11: Generative Models and CapsNet
- Introduction
- So what is a GAN?
- Some cool GAN applications
- Learning to forge MNIST images with simple GANs
- Getting ready
- How to do it...
- How it works...
- Learning to forge MNIST images with DCGANs
- Getting ready
- How to do it...
- How it works...
- Learning to forge Celebrity Faces and other datasets with DCGAN
- Getting ready
- How to do it...
- How it works...
- There's more...
- Implementing Variational Autoencoders
- Getting ready...
- How to do it...
- How it works...
- There's More...
- See also...
- Learning to beat the previous MNIST state-of-the-art results with Capsule Networks
- Getting ready
- How to do it...
- How it works...
- There's more...
- Chapter 12: Distributed TensorFlow and Cloud Deep Learning
- Introduction
- Working with TensorFlow and GPUs
- Getting ready
- How to do it...
- How it works...
- Playing with Distributed TensorFlow: multiple GPUs and one CPU
- Getting ready
- How to do it...
- How it works...
- Playing with Distributed TensorFlow: multiple servers
- Getting ready
- How to do it...
- How it works...
- There is more...
- Training a Distributed TensorFlow MNIST classifier
- Getting ready
- How to do it...
- How it works...
- Working with TensorFlow Serving and Docker.
- Getting ready.
