TensorFlow Machine Learning Cookbook.
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| Author / Creator: | McClure, Nick. |
|---|---|
| Imprint: | Birmingham : Packt Publishing, 2017. |
| Description: | 1 online resource (370 pages) |
| Language: | English |
| Subject: | |
| Format: | E-Resource Book |
| URL for this record: | http://pi.lib.uchicago.edu/1001/cat/bib/11910785 |
Table of Contents:
- 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 TrainingCombining 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 SVMImplementing 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 ToeChapter 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 LayersCreating 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.