This chapter intends to introduce the main objects and concepts in TensorFlow. We also introduce how to access the data for the rest of the book and provide additional resources for learning about TensorFlow.
Here we show how to access all the various required data sources in the book. There are also links describing the data sources and where they come from.
After we have established the basic objects and methods in TensorFlow, we now want to establish the components that make up TensorFlow algorithms. We start by introducing computational graphs, and then move to loss functions and back propagation. We end with creating a simple classifier and then show an example of evaluating regression and classification algorithms.
In order to train a model, we must be able to evaluate how well it is doing. This is given by loss functions. We plot various loss functions and talk about the benefits and limitations of some.
Here we show how to implement various linear regression techniques in TensorFlow. The first two sections show how to do standard matrix linear regression solving in TensorFlow. The remaining six sections depict how to implement various types of regression using computational graphs in TensorFlow.
This chapter shows how to implement various SVM methods with TensorFlow. We first create a linear SVM and also show how it can be used for regression. We then introduce kernels (RBF Gaussian kernel) and show how to use it to split up non-linear data. We finish with a multi-dimensional implementation of non-linear SVMs to work with multiple classes.
Nearest Neighbor methods are a very popular ML algorithm. We show how to implement k-Nearest Neighbors, weighted k-Nearest Neighbors, and k-Nearest Neighbors with mixed distance functions. In this chapter we also show how to use the Levenshtein distance (edit distance) in TensorFlow, and use it to calculate the distance between strings. We end this chapter with showing how to use k-Nearest Neighbors for categorical prediction with the MNIST handwritten digit recognition.
We use a mixed distance function to match addresses. We use numerical distance for zip codes, and string edit distance for street names. The street names are allowed to have typos.
Neural Networks are very important in machine learning and growing in popularity due to the major breakthroughs in prior unsolved problems. We must start with introducing 'shallow' neural networks, which are very powerful and can help us improve our prior ML algorithm results. We start by introducing the very basic NN unit, the operational gate. We gradually add more and more to the neural network and end with training a model to play tic-tac-toe.
This section introduces the convolution layer and the max-pool layer. We show how to chain these together in a 1D and 2D example with fully connected layers as well.
Given a set of tic-tac-toe boards and corresponding optimal moves, we train a neural network classification model to play. At the end of the script, you can attempt to play against the trained model.
Natural Language Processing (NLP) is a way of processing textual information into numerical summaries, features, or models. In this chapter we will motivate and explain how to best deal with text in TensorFlow. We show how to implement the classic 'Bag-of-Words' and show that there may be better ways to embed text based on the problem at hand. There are neural network embeddings called Word2Vec (CBOW and Skip-Gram) and Doc2Vec. We show how to implement all of these in TensorFlow.
Here we use TensorFlow to do a one-hot-encoding of words called bag-of-words. We use this method and logistic regression to predict if a text message is spam or ham.
We implement Text Frequency - Inverse Document Frequency (TFIDF) with a combination of Sci-kit Learn and TensorFlow. We perform logistic regression on TFIDF vectors to improve on our spam/ham text-message predictions.
Next, we implement a form of Word2Vec called, "CBOW" (Continuous Bag of Words) on a movie review database. We also introduce method to saving and loading word embeddings.
Convolutional Neural Networks (CNNs) are ways of getting neural networks to deal with image data. CNN derive their name from the use of a convolutional layer that applies a fixed size filter across a larger image, recognizing a pattern in any part of the image. There are many other tools that they use (max-pooling, dropout, etc...) that we show how to implement with TensorFlow. We also show how to retrain an existing architecture and take CNNs further with Stylenet and Deep Dream.
This script shows a line-by-line explanation of TensorFlow's deepdream tutorial. Taken from Deepdream on TensorFlow. Note that the code here is converted to Python 3.
Recurrent Neural Networks (RNNs) are very similar to regular neural networks except that they allow 'recurrent' connections, or loops that depend on the prior states of the network. This allows RNNs to efficiently deal with sequential data, whereas other types of networks cannot. We then motivate the usage of LSTM (Long Short Term Memory) networks as a way of addressing regular RNN problems. Then we show how easy it is to implement these RNN types in TensorFlow.
We introduce Recurrent Neural Networks and how they are able to feed in a sequence and predict either a fixed target (categorical/numerical) or another sequence (sequence to sequence).
Here, we implement a Siamese RNN to predict the similarity of addresses and use it for record matching. Using RNNs for record matching is very versatile, as we do not have a fixed set of target categories and can use the trained model to predict similarities across new addresses.
Of course there is more to TensorFlow than just creating and fitting machine learning models. Once we have a model that we want to use, we have to move it towards production usage. This chapter will provide tips and examples of implementing unit tests, using multiple processors, using multiple machines (TensorFlow distributed), and finish with a full production example.
We show how to do take the RNN model for predicting ham/spam (from Chapter 9, recipe #2) and put it in two production level files: training and evaluation.
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