TensorFlow in Action
- Length: 680 pages
- Edition: 1
- Language: English
- Publisher: Manning
- Publication Date: 2022-10-18
- ISBN-10: 1617298344
- ISBN-13: 9781617298349
- Sales Rank: #1291748 (See Top 100 Books)
This practical guide to building deep learning models with the new features features of TensorFlow 2.0 is filled with engaging projects, simple language, and coverage of the latest algorithms.
In TensorFlow 2.0 in Action, you’ll dig into the newest version of Google’s amazing TensorFlow framework as you learn to create incredible deep learning applications. You’ll develop a sentiment analyzer for movie reviews, an NLP spam classifier, and other hands-on projects.
TensorFlow in Action brief contents contents preface acknowledgments about this book Who should read this book? How this book is organized: A roadmap About the code liveBook discussion forum about the author about the cover illustration Part 1—Foundations of TensorFlow 2 and deep learning 1 The amazing world of TensorFlow 1.1 What is TensorFlow? 1.1.1 An overview of popular components of TensorFlow 1.1.2 Building and deploying a machine learning model 1.2 GPU vs. CPU 1.3 When and when not to use TensorFlow 1.3.1 When to use TensorFlow 1.3.2 When not to use TensorFlow 1.4 What will this book teach you? 1.4.1 TensorFlow fundamentals 1.4.2 Deep learning algorithms 1.4.3 Monitoring and optimization 1.5 Who is this book for? 1.6 Should we really care about Python and TensorFlow 2? Summary 2 TensorFlow 2.1 First steps with TensorFlow 2.1.1 How does TensorFlow operate under the hood? 2.2 TensorFlow building blocks 2.2.1 Understanding tf.Variable 2.2.2 Understanding tf.Tensor 2.2.3 Understanding tf.Operation 2.3 Neural network–related computations in TensorFlow 2.3.1 Matrix multiplication 2.3.2 Convolution operation 2.3.3 Pooling operation Summary Answers to exercises 3 Keras and data retrieval in TensorFlow 3.1 Keras model-building APIs 3.1.1 Introducing the data set 3.1.2 The Sequential API 3.1.3 The functional API 3.1.4 The sub-classing API 3.2 Retrieving data for TensorFlow/Keras models 3.2.1 tf.data API 3.2.2 Keras DataGenerators 3.2.3 tensorflow-datasets package Summary Answers to exercises 4 Dipping toes in deep learning 4.1 Fully connected networks 4.1.1 Understanding the data 4.1.2 Autoencoder model 4.2 Convolutional neural networks 4.2.1 Understanding the data 4.2.2 Implementing the network 4.3 One step at a time: Recurrent neural networks (RNNs) 4.3.1 Understanding the data 4.3.2 Implementing the model 4.3.3 Predicting future CO2 values with the trained model Summary Answers to exercises 5 State-of-the-art in deep learning: Transformers 5.1 Representing text as numbers 5.2 Understanding the Transformer model 5.2.1 The encoder-decoder view of the Transformer 5.2.2 Diving deeper 5.2.3 Self-attention layer 5.2.4 Understanding self-attention using scalars 5.2.5 Self-attention as a cooking competition 5.2.6 Masked self-attention layers 5.2.7 Multi-head attention 5.2.8 Fully connected layer 5.2.9 Putting everything together Summary Answers to exercises Part 2—Look ma, no hands! Deep networks in the real world 6 Teaching machines to see: Image classification with CNNs 6.1 Putting the data under the microscope: Exploratory data analysis 6.1.1 The folder/file structure 6.1.2 Understanding the classes in the data set 6.1.3 Computing simple statistics on the data set 6.2 Creating data pipelines using the Keras ImageDataGenerator 6.3 Inception net: Implementing a state-of-the-art image classifier 6.3.1 Recap on CNNs 6.3.2 Inception net v1 6.3.3 Putting everything together 6.3.4 Other Inception models 6.4 Training the model and evaluating performance Summary Answers to exercises 7 Teaching machines to see better: Improving CNNs and making them confess 7.1 Techniques for reducing overfitting 7.1.1 Image data augmentation with Keras 7.1.2 Dropout: Randomly switching off parts of your network to improve generalizability 7.1.3 Early stopping: Halting the training process if the network starts to underperform 7.2 Toward minimalism: Minception instead of Inception 7.2.1 Implementing the stem 7.2.2 Implementing Inception-ResNet type A block 7.2.3 Implementing the Inception-ResNet type B block 7.2.4 Implementing the reduction block 7.2.5 Putting everything together 7.2.6 Training Minception 7.3 If you can't beat them, join ‘em: Using pretrained networks for enhancing performance 7.3.1 Transfer learning: Reusing existing knowledge in deep neural networks 7.4 Grad-CAM: Making CNNs confess Summary Answers to exercises 8 Telling things apart: Image segmentation 8.1 Understanding the data 8.2 Getting serious: Defining a TensorFlow data pipeline 8.2.1 Optimizing tf.data pipelines 8.2.2 The final tf.data pipeline 8.3 DeepLabv3: Using pretrained networks to segment images 8.3.1 A quick overview of the ResNet-50 model 8.3.2 Atrous convolution: Increasing the receptive field of convolution layers with holes 8.3.3 Implementing DeepLab v3 using the Keras functional API 8.3.4 Implementing the atrous spatial pyramid pooling module 8.3.5 Putting everything together 8.4 Compiling the model: Loss functions and evaluation metrics in image segmentation 8.4.1 Loss functions 8.4.2 Evaluation metrics 8.5 Training the model 8.6 Evaluating the model Summary Answers to exercises 9 Natural language processing with TensorFlow: Sentiment analysis 9.1 What the text? Exploring and processing text 9.2 Getting text ready for the model 9.2.1 Splitting training/validation and testing data 9.2.2 Analyze the vocabulary 9.2.3 Analyzing the sequence length 9.2.4 Text to words and then to numbers with Keras 9.3 Defining an end-to-end NLP pipeline with TensorFlow 9.4 Happy reviews mean happy customers: Sentiment analysis 9.4.1 LSTM Networks 9.4.2 Defining the final model 9.5 Training and evaluating the model 9.6 Injecting semantics with word vectors 9.6.1 Word embeddings 9.6.2 Defining the final model with word embeddings 9.6.3 Training and evaluating the model Summary Answers to exercises 10 Natural language processing with TensorFlow: Language modeling 10.1 Processing the data 10.1.1 What is language modeling? 10.1.2 Downloading and playing with data 10.1.3 Too large vocabulary? N-grams to the rescue 10.1.4 Tokenizing text 10.1.5 Defining a tf.data pipeline 10.2 GRUs in Wonderland: Generating text with deep learning 10.3 Measuring the quality of the generated text 10.4 Training and evaluating the language model 10.5 Generating new text from the language model: Greedy decoding 10.6 Beam search: Enhancing the predictive power of sequential models Summary Answers to exercises Part 3—Advanced deep networks for complex problems 11 Sequence-to-sequence learning: Part 11.1 Understanding the machine translation data 11.2 Writing an English-German seq2seq machine translator 11.2.1 The TextVectorization layer 11.2.2 Defining the TextVectorization layers for the seq2seq model 11.2.3 Defining the encoder 11.2.4 Defining the decoder and the final model 11.2.5 Compiling the model 11.3 Training and evaluating the model 11.4 From training to inference: Defining the inference model Summary Answers to exercises 12 Sequence-to-sequence learning: Part 12.1 Eyeballing the past: Improving our model with attention 12.1.1 Implementing Bahdanau attention in TensorFlow 12.1.2 Defining the final model 12.1.3 Training the model 12.2 Visualizing the attention Summary Answers to exercises 13 Transformers 13.1 Transformers in more detail 13.1.1 Revisiting the basic components of the Transformer 13.1.2 Embeddings in the Transformer 13.1.3 Residuals and normalization 13.2 Using pretrained BERT for spam classification 13.2.1 Understanding BERT 13.2.2 Classifying spam with BERT in TensorFlow 13.3 Question answering with Hugging Face’s Transformers 13.3.1 Understanding the data 13.3.2 Processing data 13.3.3 Defining the DistilBERT model 13.3.4 Training the model 13.3.5 Ask BERT a question Summary Answers to exercises 14 TensorBoard: Big brother of TensorFlow 14.1 Visualize data with TensorBoard 14.2 Tracking and monitoring models with TensorBoard 14.3 Using tf.summary to write custom metrics during model training 14.4 Profiling models to detect performance bottlenecks 14.4.1 Optimizing the input pipeline 14.4.2 Mixed precision training 14.5 Visualizing word vectors with the TensorBoard Summary Answers to exercises 15 TFX: MLOps and deploying models with TensorFlow 15.1 Writing a data pipeline with TFX 15.1.1 Loading data from CSV files 15.1.2 Generating basic statistics from the data 15.1.3 Inferring the schema from data 15.1.4 Converting data to features 15.2 Training a simple regression neural network: TFX Trainer API 15.2.1 Defining a Keras model 15.2.2 Defining the model training 15.2.3 SignatureDefs: Defining how models are used outside TensorFlow 15.2.4 Training the Keras model with TFX Trainer 15.3 Setting up Docker to serve a trained model 15.4 Deploying the model and serving it through an API 15.4.1 Validating the infrastructure 15.4.2 Resolving the correct model 15.4.3 Evaluating the model 15.4.4 Pushing the final model 15.4.5 Predicting with the TensorFlow serving API Summary Answers to exercises Appendix A—Setting up the environment A.1 In a Unix-based environment A.1.1 Creating a virtual Python environment with Anaconda distribution (Ubuntu) A.1.2 Prerequisites for GPU support (Ubuntu) A.1.3 Notes on MacOS A.2 In Windows Environments A.2.1 Creating a Virtual Python Environment (Anaconda) A.2.2 Prerequisites for GPU support A.3 Activating and deactivating the conda environment A.4 Running the Jupyter Notebook server and creating notebooks A.5 Miscellaneous notes Appendix B—Computer vision B.1 Grad-CAM: Interpreting computer vision models B.2 Image segmentation: U-Net model B.2.1 Understanding and defining the U-Net model B.2.2 What’s better than an encoder? A pretrained encoder Appendix C—Natural language processing C.1 Touring around the zoo: Meeting other Transformer models C.1.1 Generative pre-training (GPT) model (2018) C.1.2 DistilBERT (2019) C.1.3 RoBERT/ToBERT (2019) C.1.4 BART (2019) C.1.5 XLNet (2020) C.1.6 Albert (2020) C.1.7 Reformer (2020) index Numerics A B C D E F G H I J K L M N O P Q R S T U V W X Y Z
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