book

Machine Learning with TensorFlow, Second Edition

by Chris Mattmann

January 2021

Intermediate to advanced

456 pages

14h 7m

English

Manning Publications

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forewordprefaceacknowledgmentsabout this bookHow this book is organized: A roadmapAbout the codeliveBook discussion forumabout the authorabout the cover illustration
1.1 Machine-learning fundamentals1.1.1 Parameters1.1.2 Learning and inference1.2 Data representation and features1.3 Distance metrics1.4 Types of learning1.4.1 Supervised learning1.4.2 Unsupervised learning1.4.3 Reinforcement learning1.4.4 Meta-learning1.5 TensorFlow1.6 Overview of future chaptersSummary
2.1 Ensuring that TensorFlow works2.2 Representing tensors2.3 Creating operators2.4 Executing operators within sessions2.5 Understanding code as a graph2.5.1 Setting session configurations2.6 Writing code in Jupyter2.7 Using variables2.8 Saving and loading variables2.9 Visualizing data using TensorBoard2.9.1 Implementing a moving average2.9.2 Visualizing the moving average2.10 Putting it all together: The TensorFlow system architecture and APISummary

3.1 Formal notation3.1.1 How do you know the regression algorithm is working?3.2 Linear regression3.3 Polynomial model3.4 Regularization3.5 Application of linear regressionSummary
4.1 What is 311?4.2 Cleaning the data for regression4.3 What’s in a bell curve? Predicting Gaussian distributions4.4 Training your call prediction regressor4.5 Visualizing the results and plotting the error4.6 Regularization and training test splitsSummary
5.1 Formal notation5.2 Measuring performance5.2.1 Accuracy5.2.2 Precision and recall5.2.3 Receiver operating characteristic curve5.3 Using linear regression for classification5.4 Using logistic regression5.4.1 Solving 1D logistic regression5.4.2 Solving 2D regression5.5 Multiclass classifier5.5.1 One-versus-all5.5.2 One-versus-one5.5.3 Softmax regression5.6 Application of classificationSummary
6.1 Using the Bag of Words model6.1.1 Applying the Bag of Words model to movie reviews6.1.2 Cleaning all the movie reviews6.1.3 Exploratory data analysis on your Bag of Words6.2 Building a sentiment classifier using logistic regression6.2.1 Setting up the training for your model6.2.2 Performing the training for your model6.3 Making predictions using your sentiment classifier6.4 Measuring the effectiveness of your classifier6.5 Creating the softmax-regression sentiment classifier6.6 Submitting your results to KaggleSummary
7.1 Traversing files in TensorFlow7.2 Extracting features from audio7.3 Using k-means clustering7.4 Segmenting audio7.5 Clustering with a self-organizing map7.6 Applying clusteringSummary
8.1 The User Activity from Walking dataset8.1.1 Creating the dataset8.1.2 Computing jerk and extracting the feature vector8.2 Clustering similar participants based on jerk magnitudes8.3 Different classes of user activity for a single participantSummary
9.1 Example of a not-so-interpretable model9.2 Markov model9.3 Hidden Markov model9.4 Forward algorithm9.5 Viterbi decoding9.6 Uses of HMMs9.6.1 Modeling a video9.6.2 Modeling DNA9.6.3 Modeling an image9.7 Application of HMMsSummary
10.1 Review of HMM example: Rainy or Sunny10.2 PoS tagging10.2.1 The big picture: Training and predicting PoS with HMMs10.2.2 Generating the ambiguity PoS tagged dataset10.3 Algorithms for building the HMM for PoS disambiguation10.3.1 Generating the emission probabilities10.4 Running the HMM and evaluating its output10.5 Getting more training data from the Brown Corpus10.6 Defining error bars and metrics for PoS taggingSummary
11.1 Neural networks11.2 Autoencoders11.3 Batch training11.4 Working with images11.5 Application of autoencodersSummary
12.1 What is CIFAR-10?12.1.1 Evaluating your CIFAR-10 autoencoder12.2 Autoencoders as classifiers12.2.1 Using the autoencoder as a classifier via loss12.3 Denoising autoencoders12.4 Stacked deep autoencodersSummary
13.1 Formal notions13.1.1 Policy13.1.2 Utility13.2 Applying reinforcement learning13.3 Implementing reinforcement learning13.4 Exploring other applications of reinforcement learningSummary
14.1 Drawback of neural networks14.2 Convolutional neural networks14.3 Preparing the image14.3.1 Generating filters14.3.2 Convolving using filters14.3.3 Max pooling14.4 Implementing a CNN in TensorFlow14.4.1 Measuring performance14.4.2 Training the classifier14.5 Tips and tricks to improve performance14.6 Application of CNNsSummary
15.1 Making a real-world CNN architecture for CIFAR-1015.1.1 Loading and preparing the CIFAR-10 image data15.1.2 Performing data augmentation15.2 Building a deeper CNN architecture for CIFAR-1015.2.1 CNN optimizations for increasing learned parameter resilience15.3 Training and applying a better CIFAR-10 CNN15.4 Testing and evaluating your CNN for CIFAR-1015.4.1 CIFAR-10 accuracy results and ROC curves15.4.2 Evaluating the softmax predictions per class15.5 Building VGG -Face for facial recognition15.5.1 Picking a subset of VGG -Face for training VGG -Face Lite15.5.2 TensorFlow’s Dataset API and data augmentation15.5.3 Creating a TensorFlow dataset15.5.4 Training using TensorFlow datasets15.5.5 VGG -Face Lite model and training15.5.6 Training and evaluating VGG -Face Lite15.5.7 Evaluating and predicting with VGG -Face LiteSummary
16.1 Introduction to RNNs16.2 Implementing a recurrent neural network16.3 Using a predictive model for time-series data16.4 Applying RNNsSummary
17.1 Preparing the LibriSpeech corpus17.1.1 Downloading, cleaning, and preparing LibriSpeech OpenSLR data17.1.2 Converting the audio17.1.3 Generating per-audio transcripts17.1.4 Aggregating audio and transcripts17.2 Using the deep-speech model17.2.1 Preparing the input audio data for deep speech17.2.2 Preparing the text transcripts as character-level numerical data17.2.3 The deep-speech model in TensorFlow17.2.4 Connectionist temporal classification in TensorFlow17.3 Training and evaluating deep speechSummary
18.1 Building on classification and RNNs18.2 Understanding seq2seq architecture18.3 Vector representation of symbols18.4 Putting it all together18.5 Gathering dialogue dataSummary
19.1 Preference model19.2 Image embedding19.3 Ranking imagesSummaryWhat’s next
A.1 Installing the book’s code with DockerA.1.1 Installing Docker in WindowsA.1.2 Installing Docker in LinuxA.1.3 Installing Docker in macOSA.1.4 Using DockerA.2 Getting the data and storing modelsA.3 Necessary librariesA.4 Converting the call-center example to TensorFlow2A.4.1 The call-center example with TF2

Overview

Updated with new code, new projects, and new chapters, Machine Learning with TensorFlow, Second Edition gives readers a solid foundation in machine-learning concepts and the TensorFlow library. Written by NASA JPL Deputy CTO and Principal Data Scientist Chris Mattmann, all examples are accompanied by downloadable Jupyter Notebooks for a hands-on experience coding TensorFlow with Python. New and revised content expands coverage of core machine learning algorithms, and advancements in neural networks such as VGG-Face facial identification classifiers and deep speech classifiers.

About the Technology
Supercharge your data analysis with machine learning! ML algorithms automatically improve as they process data, so results get better over time. You don’t have to be a mathematician to use ML: Tools like Google’s TensorFlow library help with complex calculations so you can focus on getting the answers you need.

About the Book
Machine Learning with TensorFlow, Second Edition is a fully revised guide to building machine learning models using Python and TensorFlow. You’ll apply core ML concepts to real-world challenges, such as sentiment analysis, text classification, and image recognition. Hands-on examples illustrate neural network techniques for deep speech processing, facial identification, and auto-encoding with CIFAR-10.

What's Inside

Machine Learning with TensorFlow
Choosing the best ML approaches
Visualizing algorithms with TensorBoard
Sharing results with collaborators
Running models in Docker

About the Reader
Requires intermediate Python skills and knowledge of general algebraic concepts like vectors and matrices. Examples use the super-stable 1.15.x branch of TensorFlow and TensorFlow 2.x.

About the Author
Chris Mattmann is the Division Manager of the Artificial Intelligence, Analytics, and Innovation Organization at NASA Jet Propulsion Lab. The first edition of this book was written by Nishant Shukla with Kenneth Fricklas.

Quotes
A practical, no-nonsense, original approach to machine learning.
- Alain Couniot, Sopra Steria Benelux

An excellent book for readers who want to learn TensorFlow and machine learning.
- Bhagvan Kommadi, ValueMomentum

A great way to learn the ins and outs of TensorFlow, from the fundamentals to autoencoders, CNNs, and sequence-to-sequence models.
- Ariel Gamiño, GLG

Full of practical examples illustrating the concepts in a clear, progressive approach. This book is worth your while!
- Alain Lompo, ISO-GRUPPE