Machine Learning with TensorFlow

Machine Learning with TensorFlow, Second Edition
brief contents
contents
foreword
preface
acknowledgments
about this book
	How this book is organized: A roadmap
	About the code
	liveBook discussion forum
about the author
about the cover illustration
Part 1: Your machine-learning rig
	Chapter 1: A machine-learning odyssey
		1.1 Machine-learning fundamentals
			1.1.1 Parameters
			1.1.2 Learning and inference
		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
			1.4.4 Meta-learning
		1.5 TensorFlow
		1.6 Overview of future chapters
	Chapter 2: TensorFlow essentials
		2.1 Ensuring that TensorFlow works
		2.2 Representing tensors
		2.3 Creating operators
		2.4 Executing operators within sessions
		2.5 Understanding code as a graph
			2.5.1 Setting session configurations
		2.6 Writing code in Jupyter
		2.7 Using variables
		2.8 Saving and loading variables
		2.9 Visualizing data using TensorBoard
			2.9.1 Implementing a moving average
			2.9.2 Visualizing the moving average
		2.10 Putting it all together: The TensorFlow system architecture and API
Part 2: Core learning algorithms
	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
	Chapter 4: Using regression for call-center volume prediction
		4.1 What is 311?
		4.2 Cleaning the data for regression
		4.3 What’s in a bell curve? Predicting Gaussian distributions
		4.4 Training your call prediction regressor
		4.5 Visualizing the results and plotting the error
		4.6 Regularization and training test splits
	Chapter 5: A gentle introduction to classification
		5.1 Formal notation
		5.2 Measuring performance
			5.2.1 Accuracy
			5.2.2 Precision and recall
			5.2.3 Receiver operating characteristic curve
		5.3 Using linear regression for classification
		5.4 Using logistic regression
			5.4.1 Solving 1D logistic regression
			5.4.2 Solving 2D regression
		5.5 Multiclass classifier
			5.5.1 One-versus-all
			5.5.2 One-versus-one
			5.5.3 Softmax regression
		5.6 Application of classification
	Chapter 6: Sentiment classification: Large movie-review dataset
		6.1 Using the Bag of Words model
			6.1.1 Applying the Bag of Words model to movie reviews
			6.1.2 Cleaning all the movie reviews
			6.1.3 Exploratory data analysis on your Bag of Words
		6.2 Building a sentiment classifier using logistic regression
			6.2.1 Setting up the training for your model
			6.2.2 Performing the training for your model
		6.3 Making predictions using your sentiment classifier
		6.4 Measuring the effectiveness of your classifier
		6.5 Creating the softmax-regression sentiment classifier
		6.6 Submitting your results to Kaggle
	Chapter 7: Automatically clustering data
		7.1 Traversing files in TensorFlow
		7.2 Extracting features from audio
		7.3 Using k-means clustering
		7.4 Segmenting audio
		7.5 Clustering with a self-organizing map
		7.6 Applying clustering
	Chapter 8: Inferring user activity from Android accelerometer data
		8.1 The User Activity from Walking dataset
			8.1.1 Creating the dataset
			8.1.2 Computing jerk and extracting the feature vector
		8.2 Clustering similar participants based on jerk magnitudes
		8.3 Different classes of user activity for a single participant
	Chapter 9: Hidden Markov models
		9.1 Example of a not-so-interpretable model
		9.2 Markov model
		9.3 Hidden Markov model
		9.4 Forward algorithm
		9.5 Viterbi decoding
		9.6 Uses of HMMs
			9.6.1 Modeling a video
			9.6.2 Modeling DNA
			9.6.3 Modeling an image
		9.7 Application of HMMs
	Chapter 10: Part-of-speech tagging and word-sense disambiguation
		10.1 Review of HMM example: Rainy or Sunny
		10.2 PoS tagging
			10.2.1 The big picture: Training and predicting PoS with HMMs
			10.2.2 Generating the ambiguity PoS tagged dataset
		10.3 Algorithms for building the HMM for PoS disambiguation
			10.3.1 Generating the emission probabilities
		10.4 Running the HMM and evaluating its output
		10.5 Getting more training data from the Brown Corpus
		10.6 Defining error bars and metrics for PoS tagging
Part 3: The neural network paradigm
	Chapter 11: A peek into autoencoders
		11.1 Neural networks
		11.2 Autoencoders
		11.3 Batch training
		11.4 Working with images
		11.5 Application of autoencoders
	Chapter 12: Applying autoencoders: The CIFAR-10 image dataset
		12.1 What is CIFAR-10?
			12.1.1 Evaluating your CIFAR-10 autoencoder
		12.2 Autoencoders as classifiers
			12.2.1 Using the autoencoder as a classifier via loss
		12.3 Denoising autoencoders
		12.4 Stacked deep autoencoders
	Chapter 13: Reinforcement learning
		13.1 Formal notions
			13.1.1 Policy
			13.1.2 Utility
		13.2 Applying reinforcement learning
		13.3 Implementing reinforcement learning
		13.4 Exploring other applications of reinforcement learning
	Chapter 14: Convolutional neural networks
		14.1 Drawback of neural networks
		14.2 Convolutional neural networks
		14.3 Preparing the image
			14.3.1 Generating filters
			14.3.2 Convolving using filters
			14.3.3 Max pooling
		14.4 Implementing a CNN in TensorFlow
			14.4.1 Measuring performance
			14.4.2 Training the classifier
		14.5 Tips and tricks to improve performance
		14.6 Application of CNNs
	Chapter 15: Building a real-world CNN: VGG -Face and VGG -Face Lite
		15.1 Making a real-world CNN architecture for CIFAR-10
			15.1.1 Loading and preparing the CIFAR-10 image data
			15.1.2 Performing data augmentation
		15.2 Building a deeper CNN architecture for CIFAR-10
			15.2.1 CNN optimizations for increasing learned parameter resilience
		15.3 Training and applying a better CIFAR-10 CNN
		15.4 Testing and evaluating your CNN for CIFAR-10
			15.4.1 CIFAR-10 accuracy results and ROC curves
			15.4.2 Evaluating the softmax predictions per class
		15.5 Building VGG -Face for facial recognition
			15.5.1 Picking a subset of VGG -Face for training VGG -Face Lite
			15.5.2 TensorFlow’s Dataset API and data augmentation
			15.5.3 Creating a TensorFlow dataset
			15.5.4 Training using TensorFlow datasets
			15.5.5 VGG -Face Lite model and training
			15.5.6 Training and evaluating VGG -Face Lite
			15.5.7 Evaluating and predicting with VGG -Face Lite
	Chapter 16: Recurrent neural networks
		16.1 Introduction to RNNs
		16.2 Implementing a recurrent neural network
		16.3 Using a predictive model for time-series data
		16.4 Applying RNNs
	Chapter 17: LSTMs and automatic speech recognition
		17.1 Preparing the LibriSpeech corpus
			17.1.1 Downloading, cleaning, and preparing LibriSpeech OpenSLR data
			17.1.2 Converting the audio
			17.1.3 Generating per-audio transcripts
			17.1.4 Aggregating audio and transcripts
		17.2 Using the deep-speech model
			17.2.1 Preparing the input audio data for deep speech
			17.2.2 Preparing the text transcripts as character-level numerical data
			17.2.3 The deep-speech model in TensorFlow
			17.2.4 Connectionist temporal classification in TensorFlow
		17.3 Training and evaluating deep speech
	Chapter 18: Sequence-to-sequence models for chatbots
		18.1 Building on classification and RNNs
		18.2 Understanding seq2seq architecture
		18.3 Vector representation of symbols
		18.4 Putting it all together
		18.5 Gathering dialogue data
	Chapter 19: Utility landscape
		19.1 Preference model
		19.2 Image embedding
		19.3 Ranking images
What’s next
appendix: Installation instructions
	A.1 Installing the book’s code with Docker
		A.1.1 Installing Docker in Windows
		A.1.2 Installing Docker in Linux
		A.1.3 Installing Docker in macOS
		A.1.4 Using Docker
	A.2 Getting the data and storing models
	A.3 Necessary libraries
	A.4 Converting the call-center example to TensorFlow2
		A.4.1 The call-center example with TF2
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