Deep Learning with Python, Second Edition.

Detaylı Bibliyografya
Yazar: Chollet, Francois
Materyal Türü: e-Kitap
Dil:İngilizce
Baskı/Yayın Bilgisi: New York : Manning Publications Co. LLC, 2021.
Edisyon:2nd ed.
Konular:
Machine learning.
Electronic books.
Online Erişim:Full-text access
İçindekiler:
  • Intro
  • Deep Learning with Python
  • Copyright
  • dedication
  • brief contents
  • contents
  • front matter
  • preface
  • acknowledgments
  • about this book
  • Who should read this book
  • About the code
  • liveBook discussion forum
  • about the author
  • about the cover illustration
  • 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?
  • 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 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
  • Summary
  • 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 and metrics on validation data
  • 3.6.7 Inference: Using a model after training
  • Summary
  • 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
  • Summary
  • 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
  • Summary
  • 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 the 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
  • Summary
  • 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
  • Summary
  • 8 Introduction to deep learning for computer vision
  • 8.1 Introduction to convnets
  • 8.1.1 The convolution operation
  • 8.1.2 The max-pooling operation
  • 8.2 Training a convnet from scratch on a small dataset
  • 8.2.1 The relevance of deep learning for small-data problems
  • 8.2.2 Downloading the data
  • 8.2.3 Building the model
  • 8.2.4 Data preprocessing
  • 8.2.5 Using data augmentation
  • 8.3 Leveraging a pretrained model
  • 8.3.1 Feature extraction with a pretrained model
  • 8.3.2 Fine-tuning a pretrained model
  • Summary
  • 9 Advanced deep learning for computer vision
  • 9.1 Three essential computer vision tasks
  • 9.2 An image segmentation example
  • 9.3 Modern convnet architecture patterns
  • 9.3.1 Modularity, hierarchy, and reuse
  • 9.3.2 Residual connections
  • 9.3.3 Batch normalization
  • 9.3.4 Depthwise separable convolutions.
  • 9.3.5 Putting it together: A mini Xception-like model
  • 9.4 Interpreting what convnets learn
  • 9.4.1 Visualizing intermediate activations
  • 9.4.2 Visualizing convnet filters
  • 9.4.3 Visualizing heatmaps of class activation
  • Summary
  • 10 Deep learning for timeseries
  • 10.1 Different kinds of timeseries tasks
  • 10.2 A temperature-forecasting example
  • 10.2.1 Preparing the data
  • 10.2.2 A common-sense, non-machine learning baseline
  • 10.2.3 Let's try a basic machine learning model
  • 10.2.4 Let's try a 1D convolutional model
  • 10.2.5 A first recurrent baseline
  • 10.3 Understanding recurrent neural networks
  • 10.3.1 A recurrent layer in Keras
  • 10.4 Advanced use of recurrent neural networks
  • 10.4.1 Using recurrent dropout to fight overfitting
  • 10.4.2 Stacking recurrent layers
  • 10.4.3 Using bidirectional RNNs
  • 10.4.4 Going even further
  • Summary
  • 11 Deep learning for text
  • 11.1 Natural language processing: The bird's eye view
  • 11.2 Preparing text data
  • 11.2.1 Text standardization
  • 11.2.2 Text splitting (tokenization)
  • 11.2.3 Vocabulary indexing
  • 11.2.4 Using the TextVectorization layer
  • 11.3 Two approaches for representing groups of words: Sets and sequences
  • 11.3.1 Preparing the IMDB movie reviews data
  • 11.3.2 Processing words as a set: The bag-of-words approach
  • 11.3.3 Processing words as a sequence: The sequence model approach
  • 11.4 The Transformer architecture
  • 11.4.1 Understanding self-attention
  • 11.4.2 Multi-head attention
  • 11.4.3 The Transformer encoder
  • 11.4.4 When to use sequence models over bag-of-words models
  • 11.5 Beyond text classification: Sequence-to-sequence learning
  • 11.5.1 A machine translation example
  • 11.5.2 Sequence-to-sequence learning with RNNs
  • 11.5.3 Sequence-to-sequence learning with Transformer
  • Summary
  • 12 Generative deep learning
  • 12.1 Text generation.
  • 12.1.1 A brief history of generative deep learning for sequence generation.

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