- Machine Learning with TensorFlow, 2e
- Copyright
- dedication
- Praise for the First Edition
- front matter
- contents
- Part 1 Your machine-learning rig
- 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
- Summary
- 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
- Summary
- Part 2 Core learning algorithms
- 3 Linear regression and beyond
- 4 Using regression for call-center volume prediction
- 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
- Summary
- 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
- Summary
- 7 Automatically clustering data
- 8 Inferring user activity from Android accelerometer data
- 9 Hidden Markov models
- 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
- Summary
- Part 3 The neural network paradigm
- 11 A peek into autoencoders
- 12 Applying autoencoders: The CIFAR-10 image dataset
- 13 Reinforcement learning
- 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
- Summary
- 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
- Summary
- 16 Recurrent neural networks
- 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
- Summary
- 18 Sequence-to-sequence models for chatbots
- 19 Utility landscape
- 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
- index
Praise for the First Edition
An excellent book about TensorFlow, with many examples to get hands-on experience.
—Mikaël Dautrey, founder and infrastructure consultant, Isitix
A good way to quickly learn TensorFlow!
—David Krief, data scientist, Altansia
A great guide to machine learning. It helped launch my third career!
—William Wheeler, Java, Scala, Akka and Python developer, TekSystems
Great introduction to machine learning with TensorFlow.
—Hamish Dickson, backend developer, DriveTribe
The book helped me jumpstart TensorFlow and work effectively with it.
—Ursin Stauss, software developer, Swiss Post
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