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Title Page
Dedication
About Packt
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Packt.com
Contributors
About the authors
About the reviewers
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Preface
Who this book is for
What this book covers
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Download the example code files
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Conventions used
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Reviews
Overview of TensorFlow and Machine Learning
What is TensorFlow?
The TensorFlow core
Tensors
Constants
Operations
Placeholders
Tensors from Python objects
Variables
Tensors generated from library functions
Obtaining variables with the tf.get_variable()
Computation graph
The order of execution and lazy loading
Executing graphs across compute devices – CPU and GPGPU
Placing graph nodes on specific compute devices
Simple placement
Dynamic placement
Soft placement
GPU memory handling
Multiple graphs
Machine learning, classification, and logistic regression
Machine learning
Classification
Logistic regression for binary classification
Logistic regression for multiclass classification
Logistic regression with TensorFlow
Logistic regression with Keras
Summary
Questions
Further reading
Using Machine Learning to Detect Exoplanets in Outer Space
What is a decision tree?
Why do we need ensembles?
Decision tree-based ensemble methods
Random forests
Gradient boosting
Decision tree-based ensembles in TensorFlow
TensorForest Estimator
TensorFlow boosted trees estimator
Detecting exoplanets in outer space
Building a TFBT model for exoplanet detection
Summary
Questions
Further reading
Sentiment Analysis in Your Browser Using TensorFlow.js
Understanding TensorFlow.js
Understanding Adam Optimization
Understanding categorical cross entropy loss
Understanding word embeddings
Building the sentiment analysis model
Pre-processing data
Building the model
Running the model on a browser using TensorFlow.js
Summary
Questions
Digit Classification Using TensorFlow Lite
What is TensorFlow Lite?
Classification Model Evaluation Metrics
Classifying digits using TensorFlow Lite
Pre-processing data and defining the model
Converting TensorFlow model to TensorFlow Lite
Summary
Questions
Speech to Text and Topic Extraction Using NLP
Speech-to-text frameworks and toolkits
Google Speech Commands Dataset
Neural network architecture
Feature extraction module
Deep neural network module
Training the model
Summary
Questions
Further reading
Predicting Stock Prices using Gaussian Process Regression
Understanding Bayes rule
Introducing Bayesian inference
Introducing Gaussian processes
Choosing kernels in GPs
Choosing the hyper parameters of a kernel
Applying GPs to stock market prediction
Creating a stock price prediction model
Understanding the results obtained
Summary
Questions
Credit Card Fraud Detection using Autoencoders
Understanding auto-encoders
Building a fraud detection model
Defining and training a fraud detection model
Testing a fraud detection model
Summary
Questions
Generating Uncertainty in Traffic Signs Classifier Using Bayesian Neural Networks
Understanding Bayesian deep learning
Bayes rule in neural networks
Understanding TensorFlow probability, variational inference, and Monte Carlo methods
Building a Bayesian neural network
Defining, training, and testing the model
Summary
Questions
Generating Matching Shoe Bags from Shoe Images Using DiscoGANs
Understanding generative models
Training GANs
Applications
Challenges
Understanding DiscoGANs
Fundamental units of a DiscoGAN
DiscoGAN modeling
Building a DiscoGAN model
Summary
Questions
Classifying Clothing Images using Capsule Networks
Understanding the importance of capsule networks
Understanding capsules
How do capsules work?
The dynamic routing algorithm
CapsNet for classifying Fashion MNIST images
CapsNet implementation
Understanding the encoder
Understanding the decoder
Defining the loss function
Training and testing the model
Reconstructing sample images
Limitations of capsule networks
Summary
Making Quality Product Recommendations Using TensorFlow
Recommendation systems
Content-based filtering
Advantages of content-based filtering algorithms
Disadvantages of content-based filtering algorithms
Collaborative filtering
Hybrid systems
Matrix factorization
Introducing the Retailrocket dataset
Exploring the Retailrocket dataset
Pre-processing the data
The matrix factorization model for Retailrocket recommendations
The neural network model for Retailrocket recommendations
Summary
Questions
Further reading
Object Detection at a Large Scale with TensorFlow
Introducing Apache Spark
Understanding distributed TensorFlow
Deep learning through distributed TensorFlow
Learning about TensorFlowOnSpark
Understanding the architecture of TensorFlowOnSpark
Deep delving inside the TFoS API
Handwritten digits using TFoS
Object detection using TensorFlowOnSpark and Sparkdl
Transfer learning
Understanding the Sparkdl interface
Building an object detection model
Summary
Generating Book Scripts Using LSTMs
Understanding recurrent neural networks
Pre-processing the data
Defining the model
Training the model
Defining and training a text-generating model
Generating book scripts
Summary
Questions
Playing Pacman Using Deep Reinforcement Learning
Reinforcement learning
Reinforcement learning versus supervised and unsupervised learning
Components of Reinforcement Learning
OpenAI Gym
Creating a Pacman game in OpenAI Gym
DQN for deep reinforcement learning
Applying DQN to a game
Summary
Further Reading
What is Next?
Implementing TensorFlow in production
Understanding TensorFlow Hub
TensorFlow Serving
TensorFlow Extended
Recommendations for building AI applications
Limitations of deep learning
AI applications in industries
Ethical considerations in AI
Summary
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