Machine Learning Using TensorFlow Cookbook: Over 60 recipes on machine learning using deep learning solutions from Kaggle Masters and Google Developer Experts

Cover
Copyright
Contributors
Table of Contents
Preface
Chapter 1: Getting Started with TensorFlow 2.x
	How TensorFlow works
	Declaring variables and tensors
	Using eager execution
	Working with matrices
	Declaring operations
	Implementing activation functions
	Working with data sources
	Additional resources
Chapter 2: The TensorFlow Way
	Operations using eager execution
	Layering nested operations
	Working with multiple layers
	Implementing loss functions
	Implementing backpropagation
	Working with batch and stochastic training
	Combining everything together
Chapter 3: Keras
	Introduction
	Understanding Keras layers
	Using the Keras Sequential API
	Using the Keras Functional API
	Using the Keras Subclassing API
	Using the Keras Preprocessing API
Chapter 4: Linear Regression
	Learning the TensorFlow way of linear regression
	Turning a Keras model into an Estimator
	Understanding loss functions in linear regression
	Implementing Lasso and Ridge regression
	Implementing logistic regression
	Resorting to non-linear solutions
	Using Wide & Deep models
Chapter 5: Boosted Trees
	Introduction
Chapter 6: Neural Networks
	Implementing operational gates
	Working with gates and activation functions
	Implementing a one-layer neural network
	Implementing different layers
	Using a multilayer neural network
	Improving the predictions of linear models
	Learning to play Tic-Tac-Toe
Chapter 7: Predicting with Tabular Data
	Processing numerical data
	Processing dates
	Processing categorical data
	Processing ordinal data
	Processing high-cardinality categorical data
	Wrapping up all the processing
	Setting up a data generator
	Creating custom activations for tabular data
	Running a test on a difficult problem
Chapter 8: Convolutional Neural Networks
	Introduction
	Implementing a simple CNN
	Implementing an advanced CNN
	Retraining existing CNN models
	Applying StyleNet and the neural style project
	Implementing DeepDream
Chapter 9: Recurrent Neural Networks
	Text generation
	Sentiment classification
	Stock price prediction
	Open-domain question answering
	Summary
Chapter 10: Transformers
	Text generation
	Sentiment analysis
	Open-domain question answering
Chapter 11: Reinforcement Learning with TensorFlow and TF-Agents
	GridWorld
	CartPole
	MAB
Chapter 12: Taking TensorFlow to Production
	Visualizing Graphs in TensorBoard
	Managing Hyperparameter tuning with TensorBoard's HParams
	Implementing unit tests
	Using multiple executors
	Parallelizing TensorFlow
	Saving and restoring a TensorFlow model
	Using TensorFlow Serving
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