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Machine Learning with TensorFlow (Manning 20180.pdf
Machine Learning with TensorFlow MEAP v10
Copyright
Welcome
Brief contents
Chapter 1: A machine-learning odyssey
1.1 Machine learning fundamentals
1.2 Data representation and features
1.3 Distance Metrics
1.4 Types of Learning
1.4.1 Supervised Learning
1.4.2 Unsupervised Learning
1.4.3 Reinforcement Learning
1.5 Existing Tools
1.5.1 Theano
1.5.2 Caffe
1.5.3 Torch
1.5.4 Computational Graph Toolkit
1.6 TensorFlow
1.7 Overview of future chapters
1.8 Summary
Chapter 2: TensorFlow essentials
2.1 Ensuring TensorFlow works
2.2 Representing tensors
2.3 Creating operators
2.4 Executing operators with sessions
2.4.1 Understanding code as a graph
2.4.2 Session configurations
2.5 Writing code in Jupyter
2.6 Using variables
2.7 Saving and Loading Variables
2.8 Visualizing data using TensorBoard
2.8.1 Implementing a moving average
2.8.2 Visualizing the moving average
2.9 Summary
Chapter 3: Linear regression and beyond
3.1 Formal notation
3.1.1 How do you know the regression algorithm is working?
3.2 Linear Regression
3.3 Polynomial Model
3.4 Regularization
3.5 Application of linear regression
3.6 Summary
Chapter 4: A gentle introduction to classification
4.1 Formal Notation
4.2 Measuring Performance
4.2.1 Accuracy
4.2.2 Precision and Recall
4.2.3 Receiver operating characteristic curve
4.3 Using linear regression for classification
4.4 Using logistic regression
4.4.1 Solving one-dimensional logistic regression
4.4.2 Solving two-dimensional logistic regression
4.5 Multiclass classifier
4.5.1 One versus all
4.5.2 One versus one
4.5.3 Softmax regression
4.6 Application of classification
4.7 Summary
Chapter 5: Automatically clustering data
5.1 Traversing files in TensorFlow
5.2 Extracting features from audio
5.3 K-means clustering
5.4 Audio segmentation
5.5 Clustering using a self-organizing map
5.6 Application of clustering
5.7 Summary
Chapter 6: Hidden Markov models
6.1 Example of a not-so-interpretable model
6.2 Markov Model
6.3 Hidden Markov Model
6.4 Forward algorithm
6.5 Viterbi decode
6.6 Uses of Hidden Markov Models
6.6.1 Modeling a video
6.6.2 Modeling DNA
6.6.3 Modeling an image
6.7 Application of hidden Markov models
6.8 Summary
Chapter 7: A peek into autoencoders
7.1 Neural Networks
7.2 Autoencoder
7.3 Batch training
7.4 Working with images
7.5 Application of autoencoders
7.6 Summary
Chapter 8: Reinforcement learning
8.1 Formal notions
8.1.1 Policy
8.1.2 Utility
8.2 Applying reinforcement learning
8.3 Implementation
8.4 Applications of reinforcement learning
8.5 Summary
Chapter 9: Convolutional neural networks
9.1 Drawback of neural networks
9.2 Convolutional neural networks
9.3 Preparing the image
9.3.1 Generate filters
9.3.2 Convolve using filters
9.3.3 Max-pooling
9.4 Implementing a convolutional neural network in TensorFlow
9.4.1 Measuring performance
9.4.2 Training the classifier
9.5 Tips and tricks to improve performance
9.6 Application of convolutional neural networks
9.7 Summary
Chapter 10: Recurrent neural networks
10.1 Contextual information
10.2 Introduction to recurrent neural networks
10.3 Implementing a recurrent neural network
10.4 A predictive model for timeseries data
10.5 Application of recurrent neural networks
10.6 Summary
Chapter 11: Sequence-to-sequence models for chatbots
11.1.1 Classification
11.1.2 Recurrent neural networks
11.1.3 Classification and RNNs
11.2 Seq-to-seq architecture
11.3 Vector representation of symbols
11.4 Putting it all together
11.5 Gathering dialogue data
11.6 Summary
Chapter 12: Utility landscape
12.1 Preference model
12.2 Image embedding
12.3 Ranking images
12.4 Summary
12.5 What's next?
Appendix A: Installation
A.1 Installing TensorFlow using Docker
A.1.1 Install Docker on Windows
A.1.2 Install Docker on Linux
A.1.3 Install Docker on OSX
A.1.4 How to user Docker
A.2 Installing Matplotlib
MEAP Edition
Manning Early Access Program
Machine Learning with TensorFlow
Version 10
Copyright 2017 Manning Publications
For more information on this and other Manning titles go to
www.manning.com
Licensed to Shashank Nainwal
©Manning Publications Co. We welcome reader comments about anything in the manuscript - other than typos and other simple mistakes. These will be cleaned up during production of the book by copyeditors and proofreaders. https://forums.manning.com/forums/machine-learning-with-tensorflow
welcome
Dear fellow early adopters, curious readers, and puzzled newcomers,
Thank you all for every bit of communication with me, whether it be through the official
book forums, through email, on GitHub, or even on Reddit. I’ve listened carefully to your
questions, suggestions, and concerns, regardless of whether or not I’ve replied to you (and I
do apologize for not replying to you).
In the latest edition, I am proud to announce a beautiful makeover of every chapter. The
text is greatly improved and slowed down to better cover complex matters, especially the
areas where you requested more explanation. Most figures and mathematical equations have
been updated to look crisp and professional. The code is now updated to TensorFlow v1.0, and
it is also available on GitHub at https://github.com/BinRoot/TensorFlow-Book/. Also, the
chapters are rearranged to better deliver the right skills at the right time, if the book were
read in order.
Thank you for investing in the MEAP edition of Machine Learning with TensorFlow. You’re
one of the first to dive into this introductory book about cutting-edge machine learning
techniques using the hottest technology (spoiler alert: I’m talking about TensorFlow). You’re a
brave one, dear reader. And for that, I reward you generously with the following.
You’re about to learn machine learning from scratch, both the theory and how to easily
implement it. As long as you roughly understand object-oriented programming and know how
to use Python, this book will teach you everything you need to know to start solving your own
big-data problems, whether it be for work or research.
TensorFlow was released just over a year ago by some company that specializes in search
engine technology. Okay, I’m being a little facetious; well-known researchers at Google
engineered this library. But with such prowess comes intimidating documentation and
assumed knowledge. Fortunately for you, this book is down-to-earth and greets you with open
arms.
Each chapter zooms into a prominent example of machine learning, such as classification,
regression, anomaly detection, clustering, and many modern neural networks. Cover them all
to master the basics, or cater it to your needs by skipping around.
Keep me updated on typos, mistakes, and improvements because this book is undergoing
heavy development. It’s like living in a house that’s still actively under construction; at least
you won’t have to pay rent. But on a serious note, your feedback along the way will be
appreciated.
With gratitude,
—Nishant Shukla
Licensed to Shashank Nainwal
©Manning Publications Co. We welcome reader comments about anything in the manuscript - other than typos and other simple mistakes. These will be cleaned up during production of the book by copyeditors and proofreaders. https://forums.manning.com/forums/machine-learning-with-tensorflow
brief contents
PART 1 MY MACHINE LEARNING RIG
1 A machine learning odyssey
2 TensorFlow essentials
PART 2 CORE LEARNING ALGORITHMS
3 Linear regression and beyond
4 A gentle introduction to classification
5 Automatically clustering data
6 Hidden Markov models
PART 3 THE NEURAL NETWORK PARADIGM
7 A peek into autoencoders
8 Reinforcement learning
9 Convolutional neural networks
10 Recurrent neural networks
11 Sequence-to-sequence models for chatbots
12 Utility landscape
APPENDIX
A Installation
Licensed to Shashank Nainwal
©Manning Publications Co. We welcome reader comments about anything in the manuscript - other than typos and other simple mistakes. These will be cleaned up during production of the book by copyeditors and proofreaders. https://forums.manning.com/forums/machine-learning-with-tensorflow
1
A machine-learning odyssey
This chapter covers
• Machine learning fundamentals
• Data representation, features, and vector norms
• Existing machine learning tools
• Why TensorFlow
Have you ever wondered if there are limits to what computer programs can solve?
Nowadays, computers appear to do a lot more than simply unravel mathematical equations. In
the last half-century, programming has become the ultimate tool to automate tasks and save
time, but how much can we automate, and how do we go about doing so?
Licensed to Shashank Nainwal
1
Can a computer observe a photograph and say “ah ha, I see a lovely couple walking over a
bridge under an umbrella in the rain?” Can software make medical decisions as accurately as
that of trained professionals? Can software predictions about the stock market perform better
than human reasoning? The achievements of the past decade hint that the answer to all these
questions is a resounding “yes,” and the implementations appear to share a common strategy.
Recent theoretic advances coupled with newly available technologies have enabled anyone
with access to a computer to attempt their own approach at solving these incredibly hard
problems. Okay, not just anyone, but that’s why you’re reading this book, right?
A programmer no longer needs to know the intricate details of a problem to solve it.
Consider converting speech to text: a traditional approach may involve understanding the
biological structure of human vocal chords to decipher utterances using many hand-designed,
domain-specific, un-generalizable pieces of code. Nowadays, it’s possible to write code that
simply looks at many examples, and figures out how to solve the problem given enough time
and examples.
The algorithm learns from data, similar to how humans learn from experiences. Humans
learn by reading books, observing situations, studying in school, exchanging conversations,
browsing websites, among other means. How can a machine possibly develop a brain capable
of learning? There’s no definitive answer, but world-class researchers have developed
intelligent programs from different angles. Among the implementations, scholars have noticed
recurring patterns in solving these kinds of problems that has led to a standardized field that
we today label as machine learning (ML).
As the study of ML matures, the tools have become more standardized, robust, performant,
and scalable. This is where TensorFlow comes in. It’s a software library with an intuitive
interface that lets programmers dive into using complex ML ideas. The next chapter will go
through the ins and outs of this library, and every chapter thereafter will explain how to use
TensorFlow for each of the various ML applications.
Trusting machine learning output
Detecting patterns is a trait that’s no longer unique to humans. The explosive growth of computer clock-speed and
memory has led us to an unusual situation: computers now can be used to make predictions, catch anomalies, rank
items, and automatically label images. This new set of tools provides intelligent answers to ill-defined problems, but at
the subtle cost of trust. Would you trust a computer algorithm to dispense vital medical advice such as whether to
perform heart surgery?
There is no place for mediocre machine learning solutions. Human trust is too fragile, and our algorithms must be
robust against doubt. Follow along closely and carefully in this chapter.
1.1 Machine learning fundamentals
Have you ever tried to explain to someone how to swim? Describing the rhythmic joint
movements and fluid patterns is overwhelming in its complexity. Similarly, some software
Licensed to Shashank Nainwal
2
problems are too complicated for us to easily wrap our minds around. For this, machine
learning may be just the tool to use.
Hand-crafting carefully tuned algorithms to get the job done was once the only way of
building software. From a simplistic point of view, traditional programming assumes a
deterministic output for each of its input. Machine learning, on the other hand, can solve a
class of problems where the input-output correspondences are not well understood.
Full speed ahead!
Machine learning is a relatively young technology, so imagine you're a geometer in Euclid's era, paving the way to a
newly discovered field. Or, treat yourself as a physicist during the time of Newton, possibly pondering something
equivalent to general relativity for the field of machine learning.
Machine Learning is about software that learns from previous experiences. Such a computer
program improves performance as more and more examples are available. The hope is that if
you throw enough data at this machinery, it will learn patterns and produce intelligent results
for newly fed input.
Another name for machine learning is inductive learning, because the code is trying to infer
structure from data alone. It’s like going on vacation in a foreign country, and reading a local
fashion magazine to mimic how to dress up. You can develop an idea of the culture from
images of people wearing local articles of clothing. You are learning inductively.
You might have never used such an approach when programming before because inductive
learning is not always necessary. Consider the task of determining whether the sum of two
arbitrary numbers is even or odd. Sure, you can imagine training a machine learning algorithm
with millions of training examples (outlined in Figure 1.1), but you certainly know that's
overkill. A more direct approach can easily do the trick.
Figure 1.1 Each pair of integers, when summed together, results in an even or odd number. The input and output
correspondences listed are called the ground-truth dataset.
For example, the sum of two odd numbers is always an even number. Convince yourself:
take any two odd numbers, add them up, and check whether the sum is an even number.
Here’s how you can prove that fact directly:
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3
For any integer n, the formula 2n+1 produces an odd number. Moreover, any odd number
can be written as 2n+1 for some value n. So the number 3 can be written 2(1) + 1. And
the number 5 can be written 2(2) + 1.
So, let's say we have two different odd numbers 2n+1 and 2m+1, where n and m are
integers. Adding two odd numbers together yields (2n+1) + (2m+1) = 2n + 2m + 2 =
2(n+m+1). This is an even number because 2 times anything is even.
Likewise, we see that the sum of two even numbers is also an even number: 2m + 2n =
2(m+n). And lastly, we also deduce that the sum of an even with an odd is an odd number: 2m
+ (2n+1) = 2(m+n) + 1. Figure 1.2 visualizes this logic more clearly.
Figure 1.2 This table reveals the inner logic behind how the output response corresponds to the input pairs.
That's it! With absolutely no use of machine learning, you can solve this task on any pair of
integers someone throws at you. Simply applying mathematical rules directly can solve this
problem. However, in ML algorithms, we can treat the inner logic as a black box, meaning the
logic happening inside might not be obvious to interpret.
Licensed to Shashank Nainwal
4
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