Book Description
TensorFlow is an open source software library for Machine Intelligence. The independent recipes in this book will teach you how to use TensorFlow for complex data computations and will let you dig deeper and gain more insights into your data than ever before. You’ll work through recipes on training models, model evaluation, sentiment analysis, regression analysis, clustering analysis, artificial neural networks, and deep learning – each using Google’s machine learning library TensorFlow.
This guide starts with the fundamentals of the TensorFlow library which includes variables, matrices, and various data sources. Moving ahead, you will get hands-on experience with Linear Regression techniques with TensorFlow. The next chapters cover important high-level concepts such as neural networks, CNN, RNN, and NLP.
Once you are familiar and comfortable with the TensorFlow ecosystem, the last chapter will show you how to take it to production.
What You Will Learn
- Become familiar with the basics of the TensorFlow machine learning library
- Get to know Linear Regression techniques with TensorFlow
- Learn SVMs with hands-on recipes
- Implement neural networks and improve predictions
- Apply NLP and sentiment analysis to your data
- Master CNN and RNN through practical recipes
- Take TensorFlow into production
Table of Contents
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
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
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
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
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
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
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
8: Convolutional Neural Networks
- Introduction
- Implementing a Simpler CNN
- Implementing an Advanced CNN
- Retraining Existing CNNs models
- Applying Stylenet/Neural-Style
- Implementing DeepDream
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
10: Taking TensorFlow to Production
- Introduction
- Implementing unit tests
- Using Multiple Executors
- Parallelizing TensorFlow
- Taking TensorFlow to Production
- Productionalizing TensorFlow – An Example
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