TensorFlow machine learning cookbook
Explore machine learning concepts using the latest numerical computing library - TensorFlow - with the help of this comprehensive cookbook About This Book Your quick guide to implementing TensorFlow in your day-to-day machine learning activities Learn advanced techniques that bring more accuracy...
| Otros Autores: | |
|---|---|
| Formato: | Libro electrónico |
| Idioma: | Inglés |
| Publicado: |
Birmingham, England :
Packt Publishing
2017.
|
| Edición: | 1st edition |
| Materias: | |
| Ver en Biblioteca Universitat Ramon Llull: | https://discovery.url.edu/permalink/34CSUC_URL/1im36ta/alma991009630329006719 |
Tabla de Contenidos:
- Cover
- Copyright
- Credits
- About the Author
- About the Reviewer
- www.PacktPub.com
- Customer Feedback
- Table of Contents
- Preface
- Chapter 1: Getting Started with TensorFlow
- Introduction
- How TensorFlow Works
- Declaring Tensors
- Using Placeholders and Variables
- Working with Matrices
- Declaring Operations
- Implementing Activation Functions
- Working with Data Sources
- Additional Resources
- Chapter 2: The TensorFlow Way
- Introduction
- Operations in a Computational Graph
- Layering Nested Operations
- Working with Multiple Layers
- Implementing Loss Functions
- Implementing Back Propagation
- Working with Batch and Stochastic Training
- Combining Everything Together
- Evaluating Models
- Chapter 3: Linear Regression
- Introduction
- Using the Matrix Inverse Method
- Implementing a Decomposition Method
- Learning The TensorFlow Way of Linear Regression
- Understanding Loss Functions in Linear Regression
- Implementing Deming regression
- Implementing Lasso and Ridge Regression
- Implementing Elastic Net Regression
- Implementing Logistic Regression
- Chapter 4: Support Vector Machines
- Introduction
- Working with a Linear SVM
- Reduction to Linear Regression
- Working with Kernels in TensorFlow
- Implementing a Non-Linear SVM
- Implementing a Multi-Class SVM
- Chapter 5: Nearest Neighbor Methods
- Introduction
- Working with Nearest Neighbors
- Working with Text-Based Distances
- Computing with Mixed Distance Functions
- Using an Address Matching Example
- Using Nearest Neighbors for Image Recognition
- Chapter 6: Neural Networks
- Introduction
- Implementing Operational Gates
- Working with Gates and Activation Functions
- Implementing a One-Layer Neural Network
- Implementing Different Layers
- Using a Multilayer Neural Network.
- Improving the Predictions of Linear Models
- Learning to Play Tic Tac Toe
- Chapter 7: Natural Language Processing
- Introduction
- Working with bag of words
- Implementing TF-IDF
- Working with Skip-gram Embeddings
- Working with CBOW Embeddings
- Making Predictions with Word2vec
- Using Doc2vec for Sentiment Analysis
- Chapter 8: Convolutional Neural Networks
- Introduction
- Implementing a Simpler CNN
- Implementing an Advanced CNN
- Retraining Existing CNNs models
- Applying Stylenet/Neural-Style
- Implementing DeepDream
- Chapter 9: Recurrent Neural Networks
- Introduction
- Implementing RNN for Spam Prediction
- Implementing an LSTM Model
- Stacking multiple LSTM Layers
- Creating Sequence-to-Sequence Models
- Training a Siamese Similarity Measure
- Chapter 10: Taking TensorFlow to Production
- Introduction
- Implementing unit tests
- Using Multiple Executors
- Parallelizing TensorFlow
- Taking TensorFlow to Production
- Productionalizing TensorFlow - An Example
- Chapter 11: More with TensorFlow
- Introduction
- Visualizing graphs in Tensorboard
- There's more…
- Working with a Genetic Algorithm
- Clustering Using K-Means
- Solving a System of ODEs
- Index.
