THE TENSORFLOW WORKSHOP: a hands-on guide to building deep learning models from scratch using real-world datasets.
This Workshop will teach you how to build deep learning models from scratch using real-world datasets with the TensorFlow framework. You will gain the knowledge you need to process a variety of data types, perform tensor computations, and understand the different layers in a deep learning model.
Gespeichert in:
| 1. Verfasser: | |
|---|---|
| Weitere Verfasser: | , |
| Format: | Elektronisch E-Book |
| Sprache: | English |
| Veröffentlicht: |
[S.l.] :
PACKT PUBLISHING LIMITED,
2021.
|
| Schlagworte: | |
| Online-Zugang: | Volltext |
| Zusammenfassung: | This Workshop will teach you how to build deep learning models from scratch using real-world datasets with the TensorFlow framework. You will gain the knowledge you need to process a variety of data types, perform tensor computations, and understand the different layers in a deep learning model. |
| Beschreibung: | 1 online resource |
| ISBN: | 9781800200227 1800200226 |
Internformat
MARC
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| 245 | 1 | 4 | |a THE TENSORFLOW WORKSHOP |h [electronic resource] : |b a hands-on guide to building deep learning models from scratch using real-world datasets. |
| 260 | |a [S.l.] : |b PACKT PUBLISHING LIMITED, |c 2021. | ||
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| 505 | 0 | |a Cover -- FM -- Copyright -- Table of Contents -- Preface -- Chapter 1: Introduction to Machine Learning with TensorFlow -- Introduction -- Implementing Artificial Neural Networks in TensorFlow -- Advantages of TensorFlow -- Disadvantages of TensorFlow -- The TensorFlow Library in Python -- Exercise 1.01: Verifying Your Version of TensorFlow -- Introduction to Tensors -- Scalars, Vectors, Matrices, and Tensors -- Exercise 1.02: Creating Scalars, Vectors, Matrices, and Tensors in TensorFlow -- Tensor Addition -- Exercise 1.03: Performing Tensor Addition in TensorFlow -- Activity 1.01: Performing Tensor Addition in TensorFlow -- Reshaping -- Tensor Transposition -- Exercise 1.04: Performing Tensor Reshaping and Transposition in TensorFlow -- Activity 1.02: Performing Tensor Reshaping and Transposition in TensorFlow -- Tensor Multiplication -- Exercise 1.05: Performing Tensor Multiplication in TensorFlow -- Optimization -- Forward Propagation -- Backpropagation -- Learning Optimal Parameters -- Optimizers in TensorFlow -- Activation functions -- Activity 1.03: Applying Activation Functions -- Summary -- Chapter 2: Loading and Processing Data -- Introduction -- Exploring Data Types -- Data Preprocessing -- Processing Tabular Data -- Exercise 2.01: Loading Tabular Data and Rescaling Numerical Fields -- Activity 2.01: Loading Tabular Data and Rescaling Numerical Fields with a MinMax Scaler -- Exercise 2.02: Preprocessing Non-Numerical Data -- Processing Image Data -- Exercise 2.03: Loading Image Data for Batch Processing -- Image Augmentation -- Activity 2.02: Loading Image Data for Batch Processing -- Text Processing -- Exercise 2.04: Loading Text Data for TensorFlow Models -- Audio Processing -- Exercise 2.05: Loading Audio Data for TensorFlow Models -- Activity 2.03: Loading Audio Data for Batch Processing -- Summary. | |
| 505 | 8 | |a Chapter 3: TensorFlow Development -- Introduction -- TensorBoard -- Exercise 3.01: Using TensorBoard to Visualize Matrix Multiplication -- Activity 3.01: Using TensorBoard to Visualize Tensor Transformations -- Exercise 3.02: Using TensorBoard to Visualize Image Batches -- TensorFlow Hub -- Exercise 3.03: Downloading a Model from TensorFlow Hub -- Google Colab -- Advantages of Google Colab -- Disadvantages of Google Colab -- Development on Google Colab -- Exercise 3.04: Using Google Colab to Visualize Data -- Activity 3.02: Performing Word Embedding from a Pre-Trained Model from TensorFlow Hub -- Summary -- Chapter 4: Regression and Classification Models -- Introduction -- Sequential Models -- Keras Layers -- Exercise 4.01: Creating an ANN with TensorFlow -- Model Fitting -- The Loss Function -- Model Evaluation -- Exercise 4.02: Creating a Linear Regression Model as an ANN with TensorFlow -- Exercise 4.03: Creating a Multi-Layer ANN with TensorFlow -- Activity 4.01: Creating a Multi-Layer ANN with TensorFlow -- Classification Models -- Exercise 4.04: Creating a Logistic Regression Model as an ANN with TensorFlow -- Activity 4.02: Creating a Multi-Layer Classification ANN with TensorFlow -- Summary -- Chapter 5: Classification Models -- Introduction -- Binary Classification -- Logistic Regression -- Binary Cross-Entropy -- Binary Classification Architecture -- Exercise 5.01: Building a Logistic Regression Model -- Metrics for Classifiers -- Accuracy and Null Accuracy -- Precision, Recall, and the F1 Score -- Confusion Matrices -- Exercise 5.02: Classification Evaluation Metrics -- Multi-Class Classification -- The Softmax Function -- Categorical Cross-Entropy -- Multi-Class Classification Architecture -- Exercise 5.03: Building a Multi-Class Model -- Activity 5.01: Building a Character Recognition Model with TensorFlow -- Multi-Label Classification. | |
| 505 | 8 | |a Activity 5.02: Building a Movie Genre Tagging a Model with TensorFlow -- Summary -- Chapter 6: Regularization and Hyperparameter Tuning -- Introduction -- Regularization Techniques -- L1 Regularization -- L2 Regularization -- Exercise 6.01: Predicting a Connect-4 Game Outcome Using the L2 Regularizer -- Dropout Regularization -- Exercise 6.02: Predicting a Connect-4 Game Outcome Using Dropout -- Early Stopping -- Activity 6.01: Predicting Income with L1 and L2 Regularizers -- Hyperparameter Tuning -- Keras Tuner -- Random Search -- Exercise 6.03: Predicting a Connect-4 Game Outcome Using Random Search from Keras Tuner -- Hyperband -- Exercise 6.04: Predicting a Connect-4 Game Outcome Using Hyperband from Keras Tuner -- Bayesian Optimization -- Activity 6.02: Predicting Income with Bayesian Optimization from Keras Tuner -- Summary -- Chapter 7: Convolutional Neural Networks -- Introduction -- CNNs -- Image Representation -- The Convolutional Layer -- Creating the Model -- Exercise 7.01: Creating the First Layer to Build a CNN -- Pooling Layer -- Max Pooling -- Average Pooling -- Exercise 7.02: Creating a Pooling Layer for a CNN -- Flattening Layer -- Exercise 7.03: Building a CNN -- Image Augmentation -- Batch Normalization -- Exercise 7.04: Building a CNN with Additional Convolutional Layers -- Binary Image Classification -- Object Classification -- Exercise 7.05: Building a CNN -- Activity 7.01: Building a CNN with More ANN Layers -- Summary -- Chapter 8: Pre-Trained Networks -- Introduction -- ImageNet -- Transfer Learning -- Exercise 8.01: Classifying Cats and Dogs with Transfer Learning -- Fine-Tuning -- Activity 8.01: Fruit Classification with Fine-Tuning -- TensorFlow Hub -- Feature Extraction -- Activity 8.02: Transfer Learning with TensorFlow Hub -- Summary -- Chapter 9: Recurrent Neural Networks -- Introduction -- Sequential Data. | |
| 505 | 8 | |a Examples of Sequential Data -- Exercise 9.01: Training an ANN for Sequential Data -- Nvidia Stock Prediction -- Recurrent Neural Networks -- RNN Architecture -- Vanishing Gradient Problem -- Long Short-Term Memory Network -- Exercise 9.02: Building an RNN with an LSTM Layer -- Nvidia Stock Prediction -- Activity 9.01: Building an RNN with Multiple LSTM Layers to Predict Power Consumption -- Natural Language Processing -- Data Preprocessing -- Dataset Cleaning -- Generating a Sequence and Tokenization -- Padding Sequences -- Back Propagation Through Time (BPTT) -- Exercise 9.03: Building an RNN with an LSTM Layer for Natural Language Processing -- Activity 9.02: Building an RNN for Predicting Tweets' Sentiment -- Summary -- Chapter 10: Custom TensorFlow Components -- Introduction -- TensorFlow APIs -- Implementing Custom Loss Functions -- Building a Custom Loss Function with the Functional API -- Building a Custom Loss Function with the Subclassing API -- Exercise 10.01: Building a Custom Loss Function -- Implementing Custom Layers -- Introduction to ResNet Blocks -- Building Custom Layers with the Functional API -- Building Custom Layers with Subclassing -- Exercise 10.02: Building a Custom Layer -- Activity 10.01: Building a Model with Custom Layers and a Custom Loss Function -- Summary -- Chapter 11: Generative Models -- Introduction -- Text Generation -- Extending NLP Sequence Models to Generate Text -- Dataset Cleaning -- Generating a Sequence and Tokenization -- Generating a Sequence of n-gram Tokens -- Padding Sequences -- Exercise 11.01: Generating Text -- Generative Adversarial Networks -- The Generator Network -- The Discriminator Network -- The Adversarial Network -- Combining the Generative and Discriminative Models -- Generating Real Samples with Class Labels -- Creating Latent Points for the Generator. | |
| 505 | 8 | |a Using the Generator to Generate Fake Samples and Class Labels -- Evaluating the Discriminator Model -- Training the Generator and Discriminator -- Creating the Latent Space, Generator, Discriminator, GAN, and Training Data -- Exercise 11.02: Generating Sequences with GANs -- Deep Convolutional Generative Adversarial Networks (DCGANs) -- Training a DCGAN -- Exercise 11.03: Generating Images with DCGAN -- Activity 11.01: Generating Images Using GANs -- Summary -- Appendix -- Index. | |
| 520 | |a This Workshop will teach you how to build deep learning models from scratch using real-world datasets with the TensorFlow framework. You will gain the knowledge you need to process a variety of data types, perform tensor computations, and understand the different layers in a deep learning model. | ||
| 630 | 0 | 0 | |a TensorFlow. |0 http://id.loc.gov/authorities/names/n2019020612 |
| 650 | 0 | |a Machine learning. |0 http://id.loc.gov/authorities/subjects/sh85079324 | |
| 650 | 0 | |a Artificial intelligence. |0 http://id.loc.gov/authorities/subjects/sh85008180 | |
| 650 | 0 | |a Open source software. |0 http://id.loc.gov/authorities/subjects/sh99003437 | |
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| 650 | 2 | |a Machine Learning |0 https://id.nlm.nih.gov/mesh/D000069550 | |
| 650 | 6 | |a Apprentissage automatique. | |
| 650 | 6 | |a Intelligence artificielle. | |
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| 700 | 1 | |a Maddalone, Anthony. | |
| 700 | 1 | |a So, Anthony. | |
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| contents | Cover -- FM -- Copyright -- Table of Contents -- Preface -- Chapter 1: Introduction to Machine Learning with TensorFlow -- Introduction -- Implementing Artificial Neural Networks in TensorFlow -- Advantages of TensorFlow -- Disadvantages of TensorFlow -- The TensorFlow Library in Python -- Exercise 1.01: Verifying Your Version of TensorFlow -- Introduction to Tensors -- Scalars, Vectors, Matrices, and Tensors -- Exercise 1.02: Creating Scalars, Vectors, Matrices, and Tensors in TensorFlow -- Tensor Addition -- Exercise 1.03: Performing Tensor Addition in TensorFlow -- Activity 1.01: Performing Tensor Addition in TensorFlow -- Reshaping -- Tensor Transposition -- Exercise 1.04: Performing Tensor Reshaping and Transposition in TensorFlow -- Activity 1.02: Performing Tensor Reshaping and Transposition in TensorFlow -- Tensor Multiplication -- Exercise 1.05: Performing Tensor Multiplication in TensorFlow -- Optimization -- Forward Propagation -- Backpropagation -- Learning Optimal Parameters -- Optimizers in TensorFlow -- Activation functions -- Activity 1.03: Applying Activation Functions -- Summary -- Chapter 2: Loading and Processing Data -- Introduction -- Exploring Data Types -- Data Preprocessing -- Processing Tabular Data -- Exercise 2.01: Loading Tabular Data and Rescaling Numerical Fields -- Activity 2.01: Loading Tabular Data and Rescaling Numerical Fields with a MinMax Scaler -- Exercise 2.02: Preprocessing Non-Numerical Data -- Processing Image Data -- Exercise 2.03: Loading Image Data for Batch Processing -- Image Augmentation -- Activity 2.02: Loading Image Data for Batch Processing -- Text Processing -- Exercise 2.04: Loading Text Data for TensorFlow Models -- Audio Processing -- Exercise 2.05: Loading Audio Data for TensorFlow Models -- Activity 2.03: Loading Audio Data for Batch Processing -- Summary. Chapter 3: TensorFlow Development -- Introduction -- TensorBoard -- Exercise 3.01: Using TensorBoard to Visualize Matrix Multiplication -- Activity 3.01: Using TensorBoard to Visualize Tensor Transformations -- Exercise 3.02: Using TensorBoard to Visualize Image Batches -- TensorFlow Hub -- Exercise 3.03: Downloading a Model from TensorFlow Hub -- Google Colab -- Advantages of Google Colab -- Disadvantages of Google Colab -- Development on Google Colab -- Exercise 3.04: Using Google Colab to Visualize Data -- Activity 3.02: Performing Word Embedding from a Pre-Trained Model from TensorFlow Hub -- Summary -- Chapter 4: Regression and Classification Models -- Introduction -- Sequential Models -- Keras Layers -- Exercise 4.01: Creating an ANN with TensorFlow -- Model Fitting -- The Loss Function -- Model Evaluation -- Exercise 4.02: Creating a Linear Regression Model as an ANN with TensorFlow -- Exercise 4.03: Creating a Multi-Layer ANN with TensorFlow -- Activity 4.01: Creating a Multi-Layer ANN with TensorFlow -- Classification Models -- Exercise 4.04: Creating a Logistic Regression Model as an ANN with TensorFlow -- Activity 4.02: Creating a Multi-Layer Classification ANN with TensorFlow -- Summary -- Chapter 5: Classification Models -- Introduction -- Binary Classification -- Logistic Regression -- Binary Cross-Entropy -- Binary Classification Architecture -- Exercise 5.01: Building a Logistic Regression Model -- Metrics for Classifiers -- Accuracy and Null Accuracy -- Precision, Recall, and the F1 Score -- Confusion Matrices -- Exercise 5.02: Classification Evaluation Metrics -- Multi-Class Classification -- The Softmax Function -- Categorical Cross-Entropy -- Multi-Class Classification Architecture -- Exercise 5.03: Building a Multi-Class Model -- Activity 5.01: Building a Character Recognition Model with TensorFlow -- Multi-Label Classification. Activity 5.02: Building a Movie Genre Tagging a Model with TensorFlow -- Summary -- Chapter 6: Regularization and Hyperparameter Tuning -- Introduction -- Regularization Techniques -- L1 Regularization -- L2 Regularization -- Exercise 6.01: Predicting a Connect-4 Game Outcome Using the L2 Regularizer -- Dropout Regularization -- Exercise 6.02: Predicting a Connect-4 Game Outcome Using Dropout -- Early Stopping -- Activity 6.01: Predicting Income with L1 and L2 Regularizers -- Hyperparameter Tuning -- Keras Tuner -- Random Search -- Exercise 6.03: Predicting a Connect-4 Game Outcome Using Random Search from Keras Tuner -- Hyperband -- Exercise 6.04: Predicting a Connect-4 Game Outcome Using Hyperband from Keras Tuner -- Bayesian Optimization -- Activity 6.02: Predicting Income with Bayesian Optimization from Keras Tuner -- Summary -- Chapter 7: Convolutional Neural Networks -- Introduction -- CNNs -- Image Representation -- The Convolutional Layer -- Creating the Model -- Exercise 7.01: Creating the First Layer to Build a CNN -- Pooling Layer -- Max Pooling -- Average Pooling -- Exercise 7.02: Creating a Pooling Layer for a CNN -- Flattening Layer -- Exercise 7.03: Building a CNN -- Image Augmentation -- Batch Normalization -- Exercise 7.04: Building a CNN with Additional Convolutional Layers -- Binary Image Classification -- Object Classification -- Exercise 7.05: Building a CNN -- Activity 7.01: Building a CNN with More ANN Layers -- Summary -- Chapter 8: Pre-Trained Networks -- Introduction -- ImageNet -- Transfer Learning -- Exercise 8.01: Classifying Cats and Dogs with Transfer Learning -- Fine-Tuning -- Activity 8.01: Fruit Classification with Fine-Tuning -- TensorFlow Hub -- Feature Extraction -- Activity 8.02: Transfer Learning with TensorFlow Hub -- Summary -- Chapter 9: Recurrent Neural Networks -- Introduction -- Sequential Data. Examples of Sequential Data -- Exercise 9.01: Training an ANN for Sequential Data -- Nvidia Stock Prediction -- Recurrent Neural Networks -- RNN Architecture -- Vanishing Gradient Problem -- Long Short-Term Memory Network -- Exercise 9.02: Building an RNN with an LSTM Layer -- Nvidia Stock Prediction -- Activity 9.01: Building an RNN with Multiple LSTM Layers to Predict Power Consumption -- Natural Language Processing -- Data Preprocessing -- Dataset Cleaning -- Generating a Sequence and Tokenization -- Padding Sequences -- Back Propagation Through Time (BPTT) -- Exercise 9.03: Building an RNN with an LSTM Layer for Natural Language Processing -- Activity 9.02: Building an RNN for Predicting Tweets' Sentiment -- Summary -- Chapter 10: Custom TensorFlow Components -- Introduction -- TensorFlow APIs -- Implementing Custom Loss Functions -- Building a Custom Loss Function with the Functional API -- Building a Custom Loss Function with the Subclassing API -- Exercise 10.01: Building a Custom Loss Function -- Implementing Custom Layers -- Introduction to ResNet Blocks -- Building Custom Layers with the Functional API -- Building Custom Layers with Subclassing -- Exercise 10.02: Building a Custom Layer -- Activity 10.01: Building a Model with Custom Layers and a Custom Loss Function -- Summary -- Chapter 11: Generative Models -- Introduction -- Text Generation -- Extending NLP Sequence Models to Generate Text -- Dataset Cleaning -- Generating a Sequence and Tokenization -- Generating a Sequence of n-gram Tokens -- Padding Sequences -- Exercise 11.01: Generating Text -- Generative Adversarial Networks -- The Generator Network -- The Discriminator Network -- The Adversarial Network -- Combining the Generative and Discriminative Models -- Generating Real Samples with Class Labels -- Creating Latent Points for the Generator. Using the Generator to Generate Fake Samples and Class Labels -- Evaluating the Discriminator Model -- Training the Generator and Discriminator -- Creating the Latent Space, Generator, Discriminator, GAN, and Training Data -- Exercise 11.02: Generating Sequences with GANs -- Deep Convolutional Generative Adversarial Networks (DCGANs) -- Training a DCGAN -- Exercise 11.03: Generating Images with DCGAN -- Activity 11.01: Generating Images Using GANs -- Summary -- Appendix -- Index. |
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Exercise 1.05: Performing Tensor Multiplication in TensorFlow -- Optimization -- Forward Propagation -- Backpropagation -- Learning Optimal Parameters -- Optimizers in TensorFlow -- Activation functions -- Activity 1.03: Applying Activation Functions -- Summary -- Chapter 2: Loading and Processing Data -- Introduction -- Exploring Data Types -- Data Preprocessing -- Processing Tabular Data -- Exercise 2.01: Loading Tabular Data and Rescaling Numerical Fields -- Activity 2.01: Loading Tabular Data and Rescaling Numerical Fields with a MinMax Scaler -- Exercise 2.02: Preprocessing Non-Numerical Data -- Processing Image Data -- Exercise 2.03: Loading Image Data for Batch Processing -- Image Augmentation -- Activity 2.02: Loading Image Data for Batch Processing -- Text Processing -- Exercise 2.04: Loading Text Data for TensorFlow Models -- Audio Processing -- Exercise 2.05: Loading Audio Data for TensorFlow Models -- Activity 2.03: Loading Audio Data for Batch Processing -- Summary.</subfield></datafield><datafield tag="505" ind1="8" ind2=" "><subfield code="a">Chapter 3: TensorFlow Development -- Introduction -- TensorBoard -- Exercise 3.01: Using TensorBoard to Visualize Matrix Multiplication -- Activity 3.01: Using TensorBoard to Visualize Tensor Transformations -- Exercise 3.02: Using TensorBoard to Visualize Image Batches -- TensorFlow Hub -- Exercise 3.03: Downloading a Model from TensorFlow Hub -- Google Colab -- Advantages of Google Colab -- Disadvantages of Google Colab -- Development on Google Colab -- Exercise 3.04: Using Google Colab to Visualize Data -- Activity 3.02: Performing Word Embedding from a Pre-Trained Model from TensorFlow Hub -- Summary -- Chapter 4: Regression and Classification Models -- Introduction -- Sequential Models -- Keras Layers -- Exercise 4.01: Creating an ANN with TensorFlow -- Model Fitting -- The Loss Function -- Model Evaluation -- Exercise 4.02: Creating a Linear Regression Model as an ANN with TensorFlow -- Exercise 4.03: Creating a Multi-Layer ANN with TensorFlow -- Activity 4.01: Creating a Multi-Layer ANN with TensorFlow -- Classification Models -- Exercise 4.04: Creating a Logistic Regression Model as an ANN with TensorFlow -- Activity 4.02: Creating a Multi-Layer Classification ANN with TensorFlow -- Summary -- Chapter 5: Classification Models -- Introduction -- Binary Classification -- Logistic Regression -- Binary Cross-Entropy -- Binary Classification Architecture -- Exercise 5.01: Building a Logistic Regression Model -- Metrics for Classifiers -- Accuracy and Null Accuracy -- Precision, Recall, and the F1 Score -- Confusion Matrices -- Exercise 5.02: Classification Evaluation Metrics -- Multi-Class Classification -- The Softmax Function -- Categorical Cross-Entropy -- Multi-Class Classification Architecture -- Exercise 5.03: Building a Multi-Class Model -- Activity 5.01: Building a Character Recognition Model with TensorFlow -- Multi-Label Classification.</subfield></datafield><datafield tag="505" ind1="8" ind2=" "><subfield code="a">Activity 5.02: Building a Movie Genre Tagging a Model with TensorFlow -- Summary -- Chapter 6: Regularization and Hyperparameter Tuning -- Introduction -- Regularization Techniques -- L1 Regularization -- L2 Regularization -- Exercise 6.01: Predicting a Connect-4 Game Outcome Using the L2 Regularizer -- Dropout Regularization -- Exercise 6.02: Predicting a Connect-4 Game Outcome Using Dropout -- Early Stopping -- Activity 6.01: Predicting Income with L1 and L2 Regularizers -- Hyperparameter Tuning -- Keras Tuner -- Random Search -- Exercise 6.03: Predicting a Connect-4 Game Outcome Using Random Search from Keras Tuner -- Hyperband -- Exercise 6.04: Predicting a Connect-4 Game Outcome Using Hyperband from Keras Tuner -- Bayesian Optimization -- Activity 6.02: Predicting Income with Bayesian Optimization from Keras Tuner -- Summary -- Chapter 7: Convolutional Neural Networks -- Introduction -- CNNs -- Image Representation -- The Convolutional Layer -- Creating the Model -- Exercise 7.01: Creating the First Layer to Build a CNN -- Pooling Layer -- Max Pooling -- Average Pooling -- Exercise 7.02: Creating a Pooling Layer for a CNN -- Flattening Layer -- Exercise 7.03: Building a CNN -- Image Augmentation -- Batch Normalization -- Exercise 7.04: Building a CNN with Additional Convolutional Layers -- Binary Image Classification -- Object Classification -- Exercise 7.05: Building a CNN -- Activity 7.01: Building a CNN with More ANN Layers -- Summary -- Chapter 8: Pre-Trained Networks -- Introduction -- ImageNet -- Transfer Learning -- Exercise 8.01: Classifying Cats and Dogs with Transfer Learning -- Fine-Tuning -- Activity 8.01: Fruit Classification with Fine-Tuning -- TensorFlow Hub -- Feature Extraction -- Activity 8.02: Transfer Learning with TensorFlow Hub -- Summary -- Chapter 9: Recurrent Neural Networks -- Introduction -- Sequential Data.</subfield></datafield><datafield tag="505" ind1="8" ind2=" "><subfield code="a">Examples of Sequential Data -- Exercise 9.01: Training an ANN for Sequential Data -- Nvidia Stock Prediction -- Recurrent Neural Networks -- RNN Architecture -- Vanishing Gradient Problem -- Long Short-Term Memory Network -- Exercise 9.02: Building an RNN with an LSTM Layer -- Nvidia Stock Prediction -- Activity 9.01: Building an RNN with Multiple LSTM Layers to Predict Power Consumption -- Natural Language Processing -- Data Preprocessing -- Dataset Cleaning -- Generating a Sequence and Tokenization -- Padding Sequences -- Back Propagation Through Time (BPTT) -- Exercise 9.03: Building an RNN with an LSTM Layer for Natural Language Processing -- Activity 9.02: Building an RNN for Predicting Tweets' Sentiment -- Summary -- Chapter 10: Custom TensorFlow Components -- Introduction -- TensorFlow APIs -- Implementing Custom Loss Functions -- Building a Custom Loss Function with the Functional API -- Building a Custom Loss Function with the Subclassing API -- Exercise 10.01: Building a Custom Loss Function -- Implementing Custom Layers -- Introduction to ResNet Blocks -- Building Custom Layers with the Functional API -- Building Custom Layers with Subclassing -- Exercise 10.02: Building a Custom Layer -- Activity 10.01: Building a Model with Custom Layers and a Custom Loss Function -- Summary -- Chapter 11: Generative Models -- Introduction -- Text Generation -- Extending NLP Sequence Models to Generate Text -- Dataset Cleaning -- Generating a Sequence and Tokenization -- Generating a Sequence of n-gram Tokens -- Padding Sequences -- Exercise 11.01: Generating Text -- Generative Adversarial Networks -- The Generator Network -- The Discriminator Network -- The Adversarial Network -- Combining the Generative and Discriminative Models -- Generating Real Samples with Class Labels -- Creating Latent Points for the Generator.</subfield></datafield><datafield tag="505" ind1="8" ind2=" "><subfield code="a">Using the Generator to Generate Fake Samples and Class Labels -- Evaluating the Discriminator Model -- Training the Generator and Discriminator -- Creating the Latent Space, Generator, Discriminator, GAN, and Training Data -- Exercise 11.02: Generating Sequences with GANs -- Deep Convolutional Generative Adversarial Networks (DCGANs) -- Training a DCGAN -- Exercise 11.03: Generating Images with DCGAN -- Activity 11.01: Generating Images Using GANs -- Summary -- Appendix -- Index.</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">This Workshop will teach you how to build deep learning models from scratch using real-world datasets with the TensorFlow framework. 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| id | ZDB-4-EBA-on1289442521 |
| illustrated | Not Illustrated |
| indexdate | 2024-11-27T13:30:27Z |
| institution | BVB |
| isbn | 9781800200227 1800200226 |
| language | English |
| oclc_num | 1289442521 |
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| owner_facet | MAIN DE-863 DE-BY-FWS |
| physical | 1 online resource |
| psigel | ZDB-4-EBA |
| publishDate | 2021 |
| publishDateSearch | 2021 |
| publishDateSort | 2021 |
| publisher | PACKT PUBLISHING LIMITED, |
| record_format | marc |
| spelling | Moocarme, Matthew. THE TENSORFLOW WORKSHOP [electronic resource] : a hands-on guide to building deep learning models from scratch using real-world datasets. [S.l.] : PACKT PUBLISHING LIMITED, 2021. 1 online resource text rdacontent computer rdamedia online resource rdacarrier Cover -- FM -- Copyright -- Table of Contents -- Preface -- Chapter 1: Introduction to Machine Learning with TensorFlow -- Introduction -- Implementing Artificial Neural Networks in TensorFlow -- Advantages of TensorFlow -- Disadvantages of TensorFlow -- The TensorFlow Library in Python -- Exercise 1.01: Verifying Your Version of TensorFlow -- Introduction to Tensors -- Scalars, Vectors, Matrices, and Tensors -- Exercise 1.02: Creating Scalars, Vectors, Matrices, and Tensors in TensorFlow -- Tensor Addition -- Exercise 1.03: Performing Tensor Addition in TensorFlow -- Activity 1.01: Performing Tensor Addition in TensorFlow -- Reshaping -- Tensor Transposition -- Exercise 1.04: Performing Tensor Reshaping and Transposition in TensorFlow -- Activity 1.02: Performing Tensor Reshaping and Transposition in TensorFlow -- Tensor Multiplication -- Exercise 1.05: Performing Tensor Multiplication in TensorFlow -- Optimization -- Forward Propagation -- Backpropagation -- Learning Optimal Parameters -- Optimizers in TensorFlow -- Activation functions -- Activity 1.03: Applying Activation Functions -- Summary -- Chapter 2: Loading and Processing Data -- Introduction -- Exploring Data Types -- Data Preprocessing -- Processing Tabular Data -- Exercise 2.01: Loading Tabular Data and Rescaling Numerical Fields -- Activity 2.01: Loading Tabular Data and Rescaling Numerical Fields with a MinMax Scaler -- Exercise 2.02: Preprocessing Non-Numerical Data -- Processing Image Data -- Exercise 2.03: Loading Image Data for Batch Processing -- Image Augmentation -- Activity 2.02: Loading Image Data for Batch Processing -- Text Processing -- Exercise 2.04: Loading Text Data for TensorFlow Models -- Audio Processing -- Exercise 2.05: Loading Audio Data for TensorFlow Models -- Activity 2.03: Loading Audio Data for Batch Processing -- Summary. Chapter 3: TensorFlow Development -- Introduction -- TensorBoard -- Exercise 3.01: Using TensorBoard to Visualize Matrix Multiplication -- Activity 3.01: Using TensorBoard to Visualize Tensor Transformations -- Exercise 3.02: Using TensorBoard to Visualize Image Batches -- TensorFlow Hub -- Exercise 3.03: Downloading a Model from TensorFlow Hub -- Google Colab -- Advantages of Google Colab -- Disadvantages of Google Colab -- Development on Google Colab -- Exercise 3.04: Using Google Colab to Visualize Data -- Activity 3.02: Performing Word Embedding from a Pre-Trained Model from TensorFlow Hub -- Summary -- Chapter 4: Regression and Classification Models -- Introduction -- Sequential Models -- Keras Layers -- Exercise 4.01: Creating an ANN with TensorFlow -- Model Fitting -- The Loss Function -- Model Evaluation -- Exercise 4.02: Creating a Linear Regression Model as an ANN with TensorFlow -- Exercise 4.03: Creating a Multi-Layer ANN with TensorFlow -- Activity 4.01: Creating a Multi-Layer ANN with TensorFlow -- Classification Models -- Exercise 4.04: Creating a Logistic Regression Model as an ANN with TensorFlow -- Activity 4.02: Creating a Multi-Layer Classification ANN with TensorFlow -- Summary -- Chapter 5: Classification Models -- Introduction -- Binary Classification -- Logistic Regression -- Binary Cross-Entropy -- Binary Classification Architecture -- Exercise 5.01: Building a Logistic Regression Model -- Metrics for Classifiers -- Accuracy and Null Accuracy -- Precision, Recall, and the F1 Score -- Confusion Matrices -- Exercise 5.02: Classification Evaluation Metrics -- Multi-Class Classification -- The Softmax Function -- Categorical Cross-Entropy -- Multi-Class Classification Architecture -- Exercise 5.03: Building a Multi-Class Model -- Activity 5.01: Building a Character Recognition Model with TensorFlow -- Multi-Label Classification. Activity 5.02: Building a Movie Genre Tagging a Model with TensorFlow -- Summary -- Chapter 6: Regularization and Hyperparameter Tuning -- Introduction -- Regularization Techniques -- L1 Regularization -- L2 Regularization -- Exercise 6.01: Predicting a Connect-4 Game Outcome Using the L2 Regularizer -- Dropout Regularization -- Exercise 6.02: Predicting a Connect-4 Game Outcome Using Dropout -- Early Stopping -- Activity 6.01: Predicting Income with L1 and L2 Regularizers -- Hyperparameter Tuning -- Keras Tuner -- Random Search -- Exercise 6.03: Predicting a Connect-4 Game Outcome Using Random Search from Keras Tuner -- Hyperband -- Exercise 6.04: Predicting a Connect-4 Game Outcome Using Hyperband from Keras Tuner -- Bayesian Optimization -- Activity 6.02: Predicting Income with Bayesian Optimization from Keras Tuner -- Summary -- Chapter 7: Convolutional Neural Networks -- Introduction -- CNNs -- Image Representation -- The Convolutional Layer -- Creating the Model -- Exercise 7.01: Creating the First Layer to Build a CNN -- Pooling Layer -- Max Pooling -- Average Pooling -- Exercise 7.02: Creating a Pooling Layer for a CNN -- Flattening Layer -- Exercise 7.03: Building a CNN -- Image Augmentation -- Batch Normalization -- Exercise 7.04: Building a CNN with Additional Convolutional Layers -- Binary Image Classification -- Object Classification -- Exercise 7.05: Building a CNN -- Activity 7.01: Building a CNN with More ANN Layers -- Summary -- Chapter 8: Pre-Trained Networks -- Introduction -- ImageNet -- Transfer Learning -- Exercise 8.01: Classifying Cats and Dogs with Transfer Learning -- Fine-Tuning -- Activity 8.01: Fruit Classification with Fine-Tuning -- TensorFlow Hub -- Feature Extraction -- Activity 8.02: Transfer Learning with TensorFlow Hub -- Summary -- Chapter 9: Recurrent Neural Networks -- Introduction -- Sequential Data. Examples of Sequential Data -- Exercise 9.01: Training an ANN for Sequential Data -- Nvidia Stock Prediction -- Recurrent Neural Networks -- RNN Architecture -- Vanishing Gradient Problem -- Long Short-Term Memory Network -- Exercise 9.02: Building an RNN with an LSTM Layer -- Nvidia Stock Prediction -- Activity 9.01: Building an RNN with Multiple LSTM Layers to Predict Power Consumption -- Natural Language Processing -- Data Preprocessing -- Dataset Cleaning -- Generating a Sequence and Tokenization -- Padding Sequences -- Back Propagation Through Time (BPTT) -- Exercise 9.03: Building an RNN with an LSTM Layer for Natural Language Processing -- Activity 9.02: Building an RNN for Predicting Tweets' Sentiment -- Summary -- Chapter 10: Custom TensorFlow Components -- Introduction -- TensorFlow APIs -- Implementing Custom Loss Functions -- Building a Custom Loss Function with the Functional API -- Building a Custom Loss Function with the Subclassing API -- Exercise 10.01: Building a Custom Loss Function -- Implementing Custom Layers -- Introduction to ResNet Blocks -- Building Custom Layers with the Functional API -- Building Custom Layers with Subclassing -- Exercise 10.02: Building a Custom Layer -- Activity 10.01: Building a Model with Custom Layers and a Custom Loss Function -- Summary -- Chapter 11: Generative Models -- Introduction -- Text Generation -- Extending NLP Sequence Models to Generate Text -- Dataset Cleaning -- Generating a Sequence and Tokenization -- Generating a Sequence of n-gram Tokens -- Padding Sequences -- Exercise 11.01: Generating Text -- Generative Adversarial Networks -- The Generator Network -- The Discriminator Network -- The Adversarial Network -- Combining the Generative and Discriminative Models -- Generating Real Samples with Class Labels -- Creating Latent Points for the Generator. Using the Generator to Generate Fake Samples and Class Labels -- Evaluating the Discriminator Model -- Training the Generator and Discriminator -- Creating the Latent Space, Generator, Discriminator, GAN, and Training Data -- Exercise 11.02: Generating Sequences with GANs -- Deep Convolutional Generative Adversarial Networks (DCGANs) -- Training a DCGAN -- Exercise 11.03: Generating Images with DCGAN -- Activity 11.01: Generating Images Using GANs -- Summary -- Appendix -- Index. This Workshop will teach you how to build deep learning models from scratch using real-world datasets with the TensorFlow framework. You will gain the knowledge you need to process a variety of data types, perform tensor computations, and understand the different layers in a deep learning model. TensorFlow. http://id.loc.gov/authorities/names/n2019020612 Machine learning. http://id.loc.gov/authorities/subjects/sh85079324 Artificial intelligence. http://id.loc.gov/authorities/subjects/sh85008180 Open source software. http://id.loc.gov/authorities/subjects/sh99003437 Artificial Intelligence https://id.nlm.nih.gov/mesh/D001185 Machine Learning https://id.nlm.nih.gov/mesh/D000069550 Apprentissage automatique. Intelligence artificielle. Logiciels libres. artificial intelligence. aat COMPUTERS / Programming Languages / Python. bisacsh Artificial intelligence fast Machine learning fast Open source software fast Maddalone, Anthony. So, Anthony. has work: The TensorFlow Workshop (Text) https://id.oclc.org/worldcat/entity/E39PCYMJ9fpdBBMctp7fCyhwCP https://id.oclc.org/worldcat/ontology/hasWork Print version: 9781800200227 Print version: 1800205252 9781800205253 (OCoLC)1252848484 FWS01 ZDB-4-EBA FWS_PDA_EBA https://search.ebscohost.com/login.aspx?direct=true&scope=site&db=nlebk&AN=3123350 Volltext |
| spellingShingle | Moocarme, Matthew THE TENSORFLOW WORKSHOP a hands-on guide to building deep learning models from scratch using real-world datasets. Cover -- FM -- Copyright -- Table of Contents -- Preface -- Chapter 1: Introduction to Machine Learning with TensorFlow -- Introduction -- Implementing Artificial Neural Networks in TensorFlow -- Advantages of TensorFlow -- Disadvantages of TensorFlow -- The TensorFlow Library in Python -- Exercise 1.01: Verifying Your Version of TensorFlow -- Introduction to Tensors -- Scalars, Vectors, Matrices, and Tensors -- Exercise 1.02: Creating Scalars, Vectors, Matrices, and Tensors in TensorFlow -- Tensor Addition -- Exercise 1.03: Performing Tensor Addition in TensorFlow -- Activity 1.01: Performing Tensor Addition in TensorFlow -- Reshaping -- Tensor Transposition -- Exercise 1.04: Performing Tensor Reshaping and Transposition in TensorFlow -- Activity 1.02: Performing Tensor Reshaping and Transposition in TensorFlow -- Tensor Multiplication -- Exercise 1.05: Performing Tensor Multiplication in TensorFlow -- Optimization -- Forward Propagation -- Backpropagation -- Learning Optimal Parameters -- Optimizers in TensorFlow -- Activation functions -- Activity 1.03: Applying Activation Functions -- Summary -- Chapter 2: Loading and Processing Data -- Introduction -- Exploring Data Types -- Data Preprocessing -- Processing Tabular Data -- Exercise 2.01: Loading Tabular Data and Rescaling Numerical Fields -- Activity 2.01: Loading Tabular Data and Rescaling Numerical Fields with a MinMax Scaler -- Exercise 2.02: Preprocessing Non-Numerical Data -- Processing Image Data -- Exercise 2.03: Loading Image Data for Batch Processing -- Image Augmentation -- Activity 2.02: Loading Image Data for Batch Processing -- Text Processing -- Exercise 2.04: Loading Text Data for TensorFlow Models -- Audio Processing -- Exercise 2.05: Loading Audio Data for TensorFlow Models -- Activity 2.03: Loading Audio Data for Batch Processing -- Summary. Chapter 3: TensorFlow Development -- Introduction -- TensorBoard -- Exercise 3.01: Using TensorBoard to Visualize Matrix Multiplication -- Activity 3.01: Using TensorBoard to Visualize Tensor Transformations -- Exercise 3.02: Using TensorBoard to Visualize Image Batches -- TensorFlow Hub -- Exercise 3.03: Downloading a Model from TensorFlow Hub -- Google Colab -- Advantages of Google Colab -- Disadvantages of Google Colab -- Development on Google Colab -- Exercise 3.04: Using Google Colab to Visualize Data -- Activity 3.02: Performing Word Embedding from a Pre-Trained Model from TensorFlow Hub -- Summary -- Chapter 4: Regression and Classification Models -- Introduction -- Sequential Models -- Keras Layers -- Exercise 4.01: Creating an ANN with TensorFlow -- Model Fitting -- The Loss Function -- Model Evaluation -- Exercise 4.02: Creating a Linear Regression Model as an ANN with TensorFlow -- Exercise 4.03: Creating a Multi-Layer ANN with TensorFlow -- Activity 4.01: Creating a Multi-Layer ANN with TensorFlow -- Classification Models -- Exercise 4.04: Creating a Logistic Regression Model as an ANN with TensorFlow -- Activity 4.02: Creating a Multi-Layer Classification ANN with TensorFlow -- Summary -- Chapter 5: Classification Models -- Introduction -- Binary Classification -- Logistic Regression -- Binary Cross-Entropy -- Binary Classification Architecture -- Exercise 5.01: Building a Logistic Regression Model -- Metrics for Classifiers -- Accuracy and Null Accuracy -- Precision, Recall, and the F1 Score -- Confusion Matrices -- Exercise 5.02: Classification Evaluation Metrics -- Multi-Class Classification -- The Softmax Function -- Categorical Cross-Entropy -- Multi-Class Classification Architecture -- Exercise 5.03: Building a Multi-Class Model -- Activity 5.01: Building a Character Recognition Model with TensorFlow -- Multi-Label Classification. Activity 5.02: Building a Movie Genre Tagging a Model with TensorFlow -- Summary -- Chapter 6: Regularization and Hyperparameter Tuning -- Introduction -- Regularization Techniques -- L1 Regularization -- L2 Regularization -- Exercise 6.01: Predicting a Connect-4 Game Outcome Using the L2 Regularizer -- Dropout Regularization -- Exercise 6.02: Predicting a Connect-4 Game Outcome Using Dropout -- Early Stopping -- Activity 6.01: Predicting Income with L1 and L2 Regularizers -- Hyperparameter Tuning -- Keras Tuner -- Random Search -- Exercise 6.03: Predicting a Connect-4 Game Outcome Using Random Search from Keras Tuner -- Hyperband -- Exercise 6.04: Predicting a Connect-4 Game Outcome Using Hyperband from Keras Tuner -- Bayesian Optimization -- Activity 6.02: Predicting Income with Bayesian Optimization from Keras Tuner -- Summary -- Chapter 7: Convolutional Neural Networks -- Introduction -- CNNs -- Image Representation -- The Convolutional Layer -- Creating the Model -- Exercise 7.01: Creating the First Layer to Build a CNN -- Pooling Layer -- Max Pooling -- Average Pooling -- Exercise 7.02: Creating a Pooling Layer for a CNN -- Flattening Layer -- Exercise 7.03: Building a CNN -- Image Augmentation -- Batch Normalization -- Exercise 7.04: Building a CNN with Additional Convolutional Layers -- Binary Image Classification -- Object Classification -- Exercise 7.05: Building a CNN -- Activity 7.01: Building a CNN with More ANN Layers -- Summary -- Chapter 8: Pre-Trained Networks -- Introduction -- ImageNet -- Transfer Learning -- Exercise 8.01: Classifying Cats and Dogs with Transfer Learning -- Fine-Tuning -- Activity 8.01: Fruit Classification with Fine-Tuning -- TensorFlow Hub -- Feature Extraction -- Activity 8.02: Transfer Learning with TensorFlow Hub -- Summary -- Chapter 9: Recurrent Neural Networks -- Introduction -- Sequential Data. Examples of Sequential Data -- Exercise 9.01: Training an ANN for Sequential Data -- Nvidia Stock Prediction -- Recurrent Neural Networks -- RNN Architecture -- Vanishing Gradient Problem -- Long Short-Term Memory Network -- Exercise 9.02: Building an RNN with an LSTM Layer -- Nvidia Stock Prediction -- Activity 9.01: Building an RNN with Multiple LSTM Layers to Predict Power Consumption -- Natural Language Processing -- Data Preprocessing -- Dataset Cleaning -- Generating a Sequence and Tokenization -- Padding Sequences -- Back Propagation Through Time (BPTT) -- Exercise 9.03: Building an RNN with an LSTM Layer for Natural Language Processing -- Activity 9.02: Building an RNN for Predicting Tweets' Sentiment -- Summary -- Chapter 10: Custom TensorFlow Components -- Introduction -- TensorFlow APIs -- Implementing Custom Loss Functions -- Building a Custom Loss Function with the Functional API -- Building a Custom Loss Function with the Subclassing API -- Exercise 10.01: Building a Custom Loss Function -- Implementing Custom Layers -- Introduction to ResNet Blocks -- Building Custom Layers with the Functional API -- Building Custom Layers with Subclassing -- Exercise 10.02: Building a Custom Layer -- Activity 10.01: Building a Model with Custom Layers and a Custom Loss Function -- Summary -- Chapter 11: Generative Models -- Introduction -- Text Generation -- Extending NLP Sequence Models to Generate Text -- Dataset Cleaning -- Generating a Sequence and Tokenization -- Generating a Sequence of n-gram Tokens -- Padding Sequences -- Exercise 11.01: Generating Text -- Generative Adversarial Networks -- The Generator Network -- The Discriminator Network -- The Adversarial Network -- Combining the Generative and Discriminative Models -- Generating Real Samples with Class Labels -- Creating Latent Points for the Generator. Using the Generator to Generate Fake Samples and Class Labels -- Evaluating the Discriminator Model -- Training the Generator and Discriminator -- Creating the Latent Space, Generator, Discriminator, GAN, and Training Data -- Exercise 11.02: Generating Sequences with GANs -- Deep Convolutional Generative Adversarial Networks (DCGANs) -- Training a DCGAN -- Exercise 11.03: Generating Images with DCGAN -- Activity 11.01: Generating Images Using GANs -- Summary -- Appendix -- Index. TensorFlow. http://id.loc.gov/authorities/names/n2019020612 Machine learning. http://id.loc.gov/authorities/subjects/sh85079324 Artificial intelligence. http://id.loc.gov/authorities/subjects/sh85008180 Open source software. http://id.loc.gov/authorities/subjects/sh99003437 Artificial Intelligence https://id.nlm.nih.gov/mesh/D001185 Machine Learning https://id.nlm.nih.gov/mesh/D000069550 Apprentissage automatique. Intelligence artificielle. Logiciels libres. artificial intelligence. aat COMPUTERS / Programming Languages / Python. bisacsh Artificial intelligence fast Machine learning fast Open source software fast |
| subject_GND | http://id.loc.gov/authorities/names/n2019020612 http://id.loc.gov/authorities/subjects/sh85079324 http://id.loc.gov/authorities/subjects/sh85008180 http://id.loc.gov/authorities/subjects/sh99003437 https://id.nlm.nih.gov/mesh/D001185 https://id.nlm.nih.gov/mesh/D000069550 |
| title | THE TENSORFLOW WORKSHOP a hands-on guide to building deep learning models from scratch using real-world datasets. |
| title_auth | THE TENSORFLOW WORKSHOP a hands-on guide to building deep learning models from scratch using real-world datasets. |
| title_exact_search | THE TENSORFLOW WORKSHOP a hands-on guide to building deep learning models from scratch using real-world datasets. |
| title_full | THE TENSORFLOW WORKSHOP [electronic resource] : a hands-on guide to building deep learning models from scratch using real-world datasets. |
| title_fullStr | THE TENSORFLOW WORKSHOP [electronic resource] : a hands-on guide to building deep learning models from scratch using real-world datasets. |
| title_full_unstemmed | THE TENSORFLOW WORKSHOP [electronic resource] : a hands-on guide to building deep learning models from scratch using real-world datasets. |
| title_short | THE TENSORFLOW WORKSHOP |
| title_sort | tensorflow workshop a hands on guide to building deep learning models from scratch using real world datasets |
| title_sub | a hands-on guide to building deep learning models from scratch using real-world datasets. |
| topic | TensorFlow. http://id.loc.gov/authorities/names/n2019020612 Machine learning. http://id.loc.gov/authorities/subjects/sh85079324 Artificial intelligence. http://id.loc.gov/authorities/subjects/sh85008180 Open source software. http://id.loc.gov/authorities/subjects/sh99003437 Artificial Intelligence https://id.nlm.nih.gov/mesh/D001185 Machine Learning https://id.nlm.nih.gov/mesh/D000069550 Apprentissage automatique. Intelligence artificielle. Logiciels libres. artificial intelligence. aat COMPUTERS / Programming Languages / Python. bisacsh Artificial intelligence fast Machine learning fast Open source software fast |
| topic_facet | TensorFlow. Machine learning. Artificial intelligence. Open source software. Artificial Intelligence Machine Learning Apprentissage automatique. Intelligence artificielle. Logiciels libres. artificial intelligence. COMPUTERS / Programming Languages / Python. Artificial intelligence Machine learning Open source software |
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