Deep Learning with Python MEAP

Deep Learning with Python, Second Edition MEAP V04
Copyright
Welcome
Brief contents
Chapter 1: What is deep learning?
	1.1 Artificial intelligence, machine learning, and deep learning
		1.1.1 Artificial intelligence
		1.1.2 Machine learning
		1.1.3 Learning rules and representations from data
		1.1.4 The “deep” in deep learning
		1.1.5 Understanding how deep learning works, in three figures
		1.1.6 What deep learning has achieved so far
		1.1.7 Don’t believe the short-term hype
		1.1.8 The promise of AI
	1.2 Before deep learning: a brief history of machine learning
		1.2.1 Probabilistic modeling
		1.2.2 Early neural networks
		1.2.3 Kernel methods
		1.2.4 Decision trees, random forests, and gradient boosting machines
		1.2.5 Back to neural networks
		1.2.6 What makes deep learning different
		1.2.7 The modern machine-learning landscape
	1.3 Why deep learning? Why now?
		1.3.1 Hardware
		1.3.2 Data
		1.3.3 Algorithms
		1.3.4 A new wave of investment
		1.3.5 The democratization of deep learning
		1.3.6 Will it last?
Chapter 2: The mathematical building blocks of neural networks
	2.1 A first look at a neural network
	2.2 Data representations for neural networks
		2.2.1 Scalars (rank-0 tensors)
		2.2.2 Vectors (rank-1 tensors)
		2.2.3 Matrices (rank-2 tensors)
		2.2.4 Rank-3 tensors and higher-rank tensors
		2.2.5 Key attributes
		2.2.6 Manipulating tensors in NumPy
		2.2.7 The notion of data batches
		2.2.8 Real-world examples of data tensors
		2.2.9 Vector data
		2.2.10 Timeseries data or sequence data
		2.2.11 Image data
		2.2.12 Video data
	2.3 The gears of neural networks: tensor operations
		2.3.1 Element-wise operations
		2.3.2 Broadcasting
		2.3.3 Tensor product
		2.3.4 Tensor reshaping
		2.3.5 Geometric interpretation of tensor operations
		2.3.6 A geometric interpretation of deep learning
	2.4 The engine of neural networks: gradient-based optimization
		2.4.1 What’s a derivative?
		2.4.2 Derivative of a tensor operation: the gradient
		2.4.3 Stochastic gradient descent
		2.4.4 Chaining derivatives: the Backpropagation algorithm
	2.5 Looking back at our first example
		2.5.1 Reimplementing our first example from scratch in TensorFlow
		2.5.2 Running one training step
		2.5.3 The full training loop
		2.5.4 Evaluating the model
	2.6 Chapter summary
Chapter 3: Introduction to Keras and TensorFlow
	3.1 What’s TensorFlow?
	3.2 What’s Keras?
	3.3 Keras and TensorFlow: a brief history
	3.4 Setting up a deep-learning workspace
		3.4.1 Jupyter notebooks: the preferred way to run deep-learning experiments
		3.4.2 Using Colaboratory
	3.5 First steps with TensorFlow
		3.5.1 Constant tensors and Variables
		3.5.2 Tensor operations: doing math in TensorFlow
		3.5.3 A second look at the GradientTape API
		3.5.4 An end-to-end example: a linear classifier in pure TensorFlow
	3.6 Anatomy of a neural network: understanding core Keras APIs
		3.6.1 Layers: the building blocks of deep learning
		3.6.2 From layers to models
		3.6.3 The \"compile\" step: configuring the learning process
		3.6.4 Picking a loss function
		3.6.5 Understanding the \"fit\" method
		3.6.6 Monitoring loss & metrics on validation data
		3.6.7 Inference: using a model after training
	3.7 Chapter summary
Chapter 4: Getting started with neural networks: classification and regression
	4.1 Classifying movie reviews: a binary classification example
		4.1.1 The IMDB dataset
		4.1.2 Preparing the data
		4.1.3 Building your model
		4.1.4 Validating your approach
		4.1.5 Using a trained model to generate predictions on new data
		4.1.6 Further experiments
		4.1.7 Wrapping up
	4.2 Classifying newswires: a multiclass classification example
		4.2.1 The Reuters dataset
		4.2.2 Preparing the data
		4.2.3 Building your model
		4.2.4 Validating your approach
		4.2.5 Generating predictions on new data
		4.2.6 A different way to handle the labels and the loss
		4.2.7 The importance of having sufficiently large intermediate layers
		4.2.8 Further experiments
		4.2.9 Wrapping up
	4.3 Predicting house prices: a regression example
		4.3.1 The Boston Housing Price dataset
		4.3.2 Preparing the data
		4.3.3 Building your model
		4.3.4 Validating your approach using K-fold validation
		4.3.5 Generating predictions on new data
		4.3.6 Wrapping up
	4.4 Chapter summary
Chapter 5: Fundamentals of machine learning
	5.1 Generalization: the goal of machine learning
		5.1.1 Underfitting and overfitting
		5.1.2 The nature of generalization in deep learning
	5.2 Evaluating machine-learning models
		5.2.1 Training, validation, and test sets
		5.2.2 Beating a common-sense baseline
		5.2.3 Things to keep in mind about model evaluation
	5.3 Improving model fit
		5.3.1 Tuning key gradient descent parameters
		5.3.2 Leveraging better architecture priors
		5.3.3 Increasing model capacity
	5.4 Improving generalization
		5.4.1 Dataset curation
		5.4.2 Feature engineering
		5.4.3 Using early stopping
		5.4.4 Regularizing your model
	5.5 Chapter summary
Chapter 6: The universal workflow of machine learning
	6.1 Define the task
		6.1.1 Frame the problem
		6.1.2 Collect a dataset
		6.1.3 Understand your data
		6.1.4 Choose a measure of success
	6.2 Develop a model
		6.2.1 Prepare the data
		6.2.2 Choose an evaluation protocol
		6.2.3 Beat a baseline
		6.2.4 Scale up: develop a model that overfits
		6.2.5 Regularize and tune your model
	6.3 Deploy your model
		6.3.1 Explain your work to stakeholders and set expectations
		6.3.2 Ship an inference model
		6.3.3 Monitor your model in the wild
		6.3.4 Maintain your model
	6.4 Chapter summary
Chapter 7: Working with Keras: a deep dive
	7.1 A spectrum of workflows
	7.2 Different ways to build Keras models
		7.2.1 The Sequential model
		7.2.2 The Functional API
		7.2.3 Subclassing the Model class
		7.2.4 Mixing and matching different components
		7.2.5 Remember: use the right tool for the job
	7.3 Using built-in training and evaluation loops
		7.3.1 Writing your own metrics
		7.3.2 Using Callbacks
		7.3.3 Writing your own callbacks
		7.3.4 Monitoring and visualization with TensorBoard
	7.4 Writing your own training and evaluation loops
		7.4.1 Training versus inference
		7.4.2 Low-level usage of metrics
		7.4.3 A complete training and evaluation loop
		7.4.4 Make it fast with tf.function
		7.4.5 Leveraging fit() with a custom training loop
	7.5 Chapter summary
Notes