Deep learning: a comprehensive guide
Gespeichert in:
| Hauptverfasser: | , , |
|---|---|
| Format: | Elektronisch E-Book |
| Sprache: | English |
| Veröffentlicht: |
Boca Raton, FL ; Abingdon, Oxon
CRC Press
2022
|
| Ausgabe: | First edition |
| Schlagworte: | |
| Online-Zugang: | DE-863 DE-862 DE-91 Volltext |
| Beschreibung: | 1 Online-Ressource (xv, 290 Seiten) Illustrationen, Diagramme |
| ISBN: | 9781003185635 9781000481884 |
| DOI: | 10.1201/9781003185635 |
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| 100 | 1 | |a Vasudevan, Shriram K. |e Verfasser |0 (DE-588)1067855165 |4 aut | |
| 245 | 1 | 0 | |a Deep learning |b a comprehensive guide |c Shriram K. Vasudevan, Sini Raj Pulari, Subashri Vasudevan |
| 250 | |a First edition | ||
| 264 | 1 | |a Boca Raton, FL ; Abingdon, Oxon |b CRC Press |c 2022 | |
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| 505 | 8 | |a Cover -- Half Title -- Title Page -- Copyright Page -- Table of Contents -- Preface -- The Authors -- Chapter 1 Introduction to Deep Learning -- Learning Objectives -- 1.1 Introduction -- 1.2 The Need: Why Deep Learning? -- 1.3 What Is the Need of a Transition From Machine Learning to Deep Learning? -- 1.4 Deep Learning Applications -- 1.4.1 Self-Driving Cars -- 1.4.2 Emotion Detection -- 1.4.3 Natural Language Processing -- 1.4.4 Entertainment -- 1.4.5 Healthcare -- YouTube Session On Deep Learning Applications -- Key Points to Remember -- Quiz -- Further Reading -- Chapter 2 The Tools and the Prerequisites -- Learning Objectives -- 2.1 Introduction -- 2.2 The Tools -- 2.2.1 Python Libraries - Must Know -- 2.2.2 The Installation Phase -- A. Anaconda Installation -- B. Jupyter Installation -- C. The First Program With the Jupyter -- D. Keras Installation -- 2.3 Datasets - A Quick Glance -- Key Points to Remember -- Quiz -- Chapter 3 Machine Learning: The Fundamentals -- Learning Objectives -- 3.1 Introduction -- 3.2 The Definitions - Yet Another Time -- 3.3 Machine Learning Algorithms -- 3.3.1 Supervised Learning Algorithms -- 3.3.2 The Unsupervised Learning Algorithms -- 3.3.3 Reinforcement Learning -- 3.3.4 Evolutionary Approach -- 3.4 How/Why Do We Need ML? -- 3.5 The ML Framework -- 3.6 Linear Regression - A Complete Understanding -- 3.7 Logistic Regression - A Complete Understanding -- 3.8 Classification - A Must-Know Concept -- 3.8.1 SVM - Support Vector Machines -- 3.8.2 K-NN (K-Nearest Neighbor) -- 3.9 Clustering - An Interesting Concept to Know -- 3.9.1 K-Means Clustering -- Key Points to Remember -- Quiz -- Further Reading -- Chapter 4 The Deep Learning Framework -- Learning Objectives -- 4.1 Introduction -- 4.2 Artificial Neuron -- 4.2.1 Biological Neuron -- 4.2.2 Perceptron -- 4.2.2.1 How a Perceptron Works? | |
| 505 | 8 | |a 4.2.3 Activation Functions -- 4.2.4 Parameters -- 4.2.5 Overfitting -- 4.3 A Few More Terms -- 4.4 Optimizers -- Key Points to Remember -- Quiz -- Further Reading -- Chapter 5 CNN - Convolutional Neural Networks: A Complete Understanding -- Learning Objectives -- 5.1 Introduction -- 5.2 What Is Underfitting, Overfitting and Appropriate Fitting? -- 5.3 Bias/variance - A Quick Learning -- 5.4 Convolutional Neural Networks -- 5.4.1 How Convolution Works -- 5.4.2 How Zero Padding Works -- 5.4.3 How Max Pooling Works -- 5.4.4 The CNN Stack - Architecture -- 5.4.5 What Is the Activation Function? -- 5.4.5.1 Sigmoid Activation Function -- 5.4.5.2 ReLU - Rectified Linear Unit -- 5.4.6 CNN - Model Building - Step By Step -- Key Points to Remember -- Quiz -- Further Reading -- Chapter 6 CNN Architectures: An Evolution -- Learning Objectives -- 6.1 Introduction -- 6.2 LeNET CNN Architecture -- 6.3 VGG16 CNN Architecture -- 6.4 AlexNet CNN Architecture -- 6.5 Other CNN Architectures at a Glance -- Key Points to Remember -- Quiz -- Further Reading -- Chapter 7 Recurrent Neural Networks -- Learning Objectives -- 7.1 Introduction -- 7.2 CNN vs. RNN: A Quick Understanding -- 7.3 RNN vs. Feedforward Neural Networks: A Quick Understanding -- 7.4 Simple RNN -- 7.5 LSTM: Long SHORT-TERM Memory -- 7.6 Gated Recurrent Unit -- Key Points to Remember -- Quiz -- Further Reading -- Chapter 8 Autoencoders -- Learning Objectives -- 8.1 Introduction -- 8.2 What Is an Autoencoder? -- 8.2.1 How Autoencoders Work -- 8.2.2 Properties of Autoencoders -- 8.3 Applications of Autoencoders -- 8.3.1 Data Compression and Dimensionality Reduction -- 8.3.2 Image Denoising -- 8.3.3 Feature Extraction -- 8.3.4 Image Generation -- 8.3.5 Image Colorization -- 8.4 Types of Autoencoders -- 8.4.1 Denoising Autoencoder -- 8.4.2 Vanilla Autoencoder -- 8.4.3 Deep Autoencoder | |
| 505 | 8 | |a 8.4.4 Sparse Autoencoder -- 8.4.5 Undercomplete Autoencoder -- 8.4.6 Stacked Autoencoder -- 8.4.7 Variational Autoencoder (VAEs) -- 8.4.8 Convolutional Autoencoder -- Key Points to Remember -- Quiz -- Further Reading -- Chapter 9 Generative Models -- Learning Objectives -- 9.1 Introduction -- 9.2 What Is a Generative Model? -- 9.3 What Are Generative Adversarial Networks (GAN)? -- 9.4 Types of GAN -- 9.4.1 Deep Convolutional GANs (DCGANs) -- 9.4.2 Stack GAN -- 9.4.3 Cycle GAN -- 9.4.4 Conditional GAN (CGAN) -- 9.4.5 Info GAN -- 9.5 Applications of GAN -- 9.5.1 Fake Image Generation -- 9.5.2 Image Modification -- 9.5.3 Text to Image/Image to Image Generation -- 9.5.4 Speech Modification -- 9.5.5 Assisting Artists -- 9.6 Implementation of GAN -- Key Points to Remember -- Quiz -- Further Reading -- Chapter 10 Transfer Learning -- Learning Objectives -- 10.1 What Is Transfer Learning? -- 10.2 When Can We Use Transfer Learning? -- 10.3 Example - 1: Cat Or Dog Using Transfer Learning With VGG 16 -- 10.4 Example - 2: Identify Your Relatives' Faces Using Transfer Learning -- 10.5 The Difference Between Transfer Learning and Fine Tuning -- 10.6 Transfer Learning Strategies -- 10.6.1 Same Domain, Task -- 10.6.2 Same Domain, Different Task -- 10.6.3 Different Domain, Same Task -- Key Points to Remember -- Quiz -- Further Reading -- Chapter 11 Intel OpenVino: A Must-Know Deep Learning Toolkit -- Learning Objectives -- 11.1 Introduction -- 11.2 OpenVino Installation Guidelines -- Key Points to Remember -- Quiz -- Further Reading -- Chapter 12 Interview Questions and Answers -- Learning Objectives -- YouTube Sessions On Deep Learning Applications -- Index | |
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Datensatz im Suchindex
| _version_ | 1824555895027138562 |
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| author | Vasudevan, Shriram K. Pulari, Sini Raj Vasudevan, Subashri |
| author_GND | (DE-588)1067855165 (DE-588)125070524X (DE-588)1250705525 |
| author_facet | Vasudevan, Shriram K. Pulari, Sini Raj Vasudevan, Subashri |
| author_role | aut aut aut |
| author_sort | Vasudevan, Shriram K. |
| author_variant | s k v sk skv s r p sr srp s v sv |
| building | Verbundindex |
| bvnumber | BV048221476 |
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| contents | Cover -- Half Title -- Title Page -- Copyright Page -- Table of Contents -- Preface -- The Authors -- Chapter 1 Introduction to Deep Learning -- Learning Objectives -- 1.1 Introduction -- 1.2 The Need: Why Deep Learning? -- 1.3 What Is the Need of a Transition From Machine Learning to Deep Learning? -- 1.4 Deep Learning Applications -- 1.4.1 Self-Driving Cars -- 1.4.2 Emotion Detection -- 1.4.3 Natural Language Processing -- 1.4.4 Entertainment -- 1.4.5 Healthcare -- YouTube Session On Deep Learning Applications -- Key Points to Remember -- Quiz -- Further Reading -- Chapter 2 The Tools and the Prerequisites -- Learning Objectives -- 2.1 Introduction -- 2.2 The Tools -- 2.2.1 Python Libraries - Must Know -- 2.2.2 The Installation Phase -- A. Anaconda Installation -- B. Jupyter Installation -- C. The First Program With the Jupyter -- D. Keras Installation -- 2.3 Datasets - A Quick Glance -- Key Points to Remember -- Quiz -- Chapter 3 Machine Learning: The Fundamentals -- Learning Objectives -- 3.1 Introduction -- 3.2 The Definitions - Yet Another Time -- 3.3 Machine Learning Algorithms -- 3.3.1 Supervised Learning Algorithms -- 3.3.2 The Unsupervised Learning Algorithms -- 3.3.3 Reinforcement Learning -- 3.3.4 Evolutionary Approach -- 3.4 How/Why Do We Need ML? -- 3.5 The ML Framework -- 3.6 Linear Regression - A Complete Understanding -- 3.7 Logistic Regression - A Complete Understanding -- 3.8 Classification - A Must-Know Concept -- 3.8.1 SVM - Support Vector Machines -- 3.8.2 K-NN (K-Nearest Neighbor) -- 3.9 Clustering - An Interesting Concept to Know -- 3.9.1 K-Means Clustering -- Key Points to Remember -- Quiz -- Further Reading -- Chapter 4 The Deep Learning Framework -- Learning Objectives -- 4.1 Introduction -- 4.2 Artificial Neuron -- 4.2.1 Biological Neuron -- 4.2.2 Perceptron -- 4.2.2.1 How a Perceptron Works? 4.2.3 Activation Functions -- 4.2.4 Parameters -- 4.2.5 Overfitting -- 4.3 A Few More Terms -- 4.4 Optimizers -- Key Points to Remember -- Quiz -- Further Reading -- Chapter 5 CNN - Convolutional Neural Networks: A Complete Understanding -- Learning Objectives -- 5.1 Introduction -- 5.2 What Is Underfitting, Overfitting and Appropriate Fitting? -- 5.3 Bias/variance - A Quick Learning -- 5.4 Convolutional Neural Networks -- 5.4.1 How Convolution Works -- 5.4.2 How Zero Padding Works -- 5.4.3 How Max Pooling Works -- 5.4.4 The CNN Stack - Architecture -- 5.4.5 What Is the Activation Function? -- 5.4.5.1 Sigmoid Activation Function -- 5.4.5.2 ReLU - Rectified Linear Unit -- 5.4.6 CNN - Model Building - Step By Step -- Key Points to Remember -- Quiz -- Further Reading -- Chapter 6 CNN Architectures: An Evolution -- Learning Objectives -- 6.1 Introduction -- 6.2 LeNET CNN Architecture -- 6.3 VGG16 CNN Architecture -- 6.4 AlexNet CNN Architecture -- 6.5 Other CNN Architectures at a Glance -- Key Points to Remember -- Quiz -- Further Reading -- Chapter 7 Recurrent Neural Networks -- Learning Objectives -- 7.1 Introduction -- 7.2 CNN vs. RNN: A Quick Understanding -- 7.3 RNN vs. Feedforward Neural Networks: A Quick Understanding -- 7.4 Simple RNN -- 7.5 LSTM: Long SHORT-TERM Memory -- 7.6 Gated Recurrent Unit -- Key Points to Remember -- Quiz -- Further Reading -- Chapter 8 Autoencoders -- Learning Objectives -- 8.1 Introduction -- 8.2 What Is an Autoencoder? -- 8.2.1 How Autoencoders Work -- 8.2.2 Properties of Autoencoders -- 8.3 Applications of Autoencoders -- 8.3.1 Data Compression and Dimensionality Reduction -- 8.3.2 Image Denoising -- 8.3.3 Feature Extraction -- 8.3.4 Image Generation -- 8.3.5 Image Colorization -- 8.4 Types of Autoencoders -- 8.4.1 Denoising Autoencoder -- 8.4.2 Vanilla Autoencoder -- 8.4.3 Deep Autoencoder 8.4.4 Sparse Autoencoder -- 8.4.5 Undercomplete Autoencoder -- 8.4.6 Stacked Autoencoder -- 8.4.7 Variational Autoencoder (VAEs) -- 8.4.8 Convolutional Autoencoder -- Key Points to Remember -- Quiz -- Further Reading -- Chapter 9 Generative Models -- Learning Objectives -- 9.1 Introduction -- 9.2 What Is a Generative Model? -- 9.3 What Are Generative Adversarial Networks (GAN)? -- 9.4 Types of GAN -- 9.4.1 Deep Convolutional GANs (DCGANs) -- 9.4.2 Stack GAN -- 9.4.3 Cycle GAN -- 9.4.4 Conditional GAN (CGAN) -- 9.4.5 Info GAN -- 9.5 Applications of GAN -- 9.5.1 Fake Image Generation -- 9.5.2 Image Modification -- 9.5.3 Text to Image/Image to Image Generation -- 9.5.4 Speech Modification -- 9.5.5 Assisting Artists -- 9.6 Implementation of GAN -- Key Points to Remember -- Quiz -- Further Reading -- Chapter 10 Transfer Learning -- Learning Objectives -- 10.1 What Is Transfer Learning? -- 10.2 When Can We Use Transfer Learning? -- 10.3 Example - 1: Cat Or Dog Using Transfer Learning With VGG 16 -- 10.4 Example - 2: Identify Your Relatives' Faces Using Transfer Learning -- 10.5 The Difference Between Transfer Learning and Fine Tuning -- 10.6 Transfer Learning Strategies -- 10.6.1 Same Domain, Task -- 10.6.2 Same Domain, Different Task -- 10.6.3 Different Domain, Same Task -- Key Points to Remember -- Quiz -- Further Reading -- Chapter 11 Intel OpenVino: A Must-Know Deep Learning Toolkit -- Learning Objectives -- 11.1 Introduction -- 11.2 OpenVino Installation Guidelines -- Key Points to Remember -- Quiz -- Further Reading -- Chapter 12 Interview Questions and Answers -- Learning Objectives -- YouTube Sessions On Deep Learning Applications -- Index |
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| dewey-full | 006.3/1 |
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| discipline | Informatik |
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| doi_str_mv | 10.1201/9781003185635 |
| edition | First edition |
| format | Electronic eBook |
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-- 5.3 Bias/variance - A Quick Learning -- 5.4 Convolutional Neural Networks -- 5.4.1 How Convolution Works -- 5.4.2 How Zero Padding Works -- 5.4.3 How Max Pooling Works -- 5.4.4 The CNN Stack - Architecture -- 5.4.5 What Is the Activation Function? -- 5.4.5.1 Sigmoid Activation Function -- 5.4.5.2 ReLU - Rectified Linear Unit -- 5.4.6 CNN - Model Building - Step By Step -- Key Points to Remember -- Quiz -- Further Reading -- Chapter 6 CNN Architectures: An Evolution -- Learning Objectives -- 6.1 Introduction -- 6.2 LeNET CNN Architecture -- 6.3 VGG16 CNN Architecture -- 6.4 AlexNet CNN Architecture -- 6.5 Other CNN Architectures at a Glance -- Key Points to Remember -- Quiz -- Further Reading -- Chapter 7 Recurrent Neural Networks -- Learning Objectives -- 7.1 Introduction -- 7.2 CNN vs. RNN: A Quick Understanding -- 7.3 RNN vs. Feedforward Neural Networks: A Quick Understanding -- 7.4 Simple RNN -- 7.5 LSTM: Long SHORT-TERM Memory -- 7.6 Gated Recurrent Unit -- Key Points to Remember -- Quiz -- Further Reading -- Chapter 8 Autoencoders -- Learning Objectives -- 8.1 Introduction -- 8.2 What Is an Autoencoder? 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| id | DE-604.BV048221476 |
| illustrated | Not Illustrated |
| index_date | 2024-07-03T19:50:32Z |
| indexdate | 2025-02-20T07:14:55Z |
| institution | BVB |
| isbn | 9781003185635 9781000481884 |
| language | English |
| oai_aleph_id | oai:aleph.bib-bvb.de:BVB01-033602213 |
| oclc_num | 1289367672 |
| open_access_boolean | |
| owner | DE-91 DE-BY-TUM DE-863 DE-BY-FWS DE-862 DE-BY-FWS |
| owner_facet | DE-91 DE-BY-TUM DE-863 DE-BY-FWS DE-862 DE-BY-FWS |
| physical | 1 Online-Ressource (xv, 290 Seiten) Illustrationen, Diagramme |
| psigel | ZDB-7-TFC ZDB-30-PQE ZDB-30-PQE TUM_PDA_PQE_Kauf |
| publishDate | 2022 |
| publishDateSearch | 2022 |
| publishDateSort | 2022 |
| publisher | CRC Press |
| record_format | marc |
| spellingShingle | Vasudevan, Shriram K. Pulari, Sini Raj Vasudevan, Subashri Deep learning a comprehensive guide Cover -- Half Title -- Title Page -- Copyright Page -- Table of Contents -- Preface -- The Authors -- Chapter 1 Introduction to Deep Learning -- Learning Objectives -- 1.1 Introduction -- 1.2 The Need: Why Deep Learning? -- 1.3 What Is the Need of a Transition From Machine Learning to Deep Learning? -- 1.4 Deep Learning Applications -- 1.4.1 Self-Driving Cars -- 1.4.2 Emotion Detection -- 1.4.3 Natural Language Processing -- 1.4.4 Entertainment -- 1.4.5 Healthcare -- YouTube Session On Deep Learning Applications -- Key Points to Remember -- Quiz -- Further Reading -- Chapter 2 The Tools and the Prerequisites -- Learning Objectives -- 2.1 Introduction -- 2.2 The Tools -- 2.2.1 Python Libraries - Must Know -- 2.2.2 The Installation Phase -- A. Anaconda Installation -- B. Jupyter Installation -- C. The First Program With the Jupyter -- D. Keras Installation -- 2.3 Datasets - A Quick Glance -- Key Points to Remember -- Quiz -- Chapter 3 Machine Learning: The Fundamentals -- Learning Objectives -- 3.1 Introduction -- 3.2 The Definitions - Yet Another Time -- 3.3 Machine Learning Algorithms -- 3.3.1 Supervised Learning Algorithms -- 3.3.2 The Unsupervised Learning Algorithms -- 3.3.3 Reinforcement Learning -- 3.3.4 Evolutionary Approach -- 3.4 How/Why Do We Need ML? -- 3.5 The ML Framework -- 3.6 Linear Regression - A Complete Understanding -- 3.7 Logistic Regression - A Complete Understanding -- 3.8 Classification - A Must-Know Concept -- 3.8.1 SVM - Support Vector Machines -- 3.8.2 K-NN (K-Nearest Neighbor) -- 3.9 Clustering - An Interesting Concept to Know -- 3.9.1 K-Means Clustering -- Key Points to Remember -- Quiz -- Further Reading -- Chapter 4 The Deep Learning Framework -- Learning Objectives -- 4.1 Introduction -- 4.2 Artificial Neuron -- 4.2.1 Biological Neuron -- 4.2.2 Perceptron -- 4.2.2.1 How a Perceptron Works? 4.2.3 Activation Functions -- 4.2.4 Parameters -- 4.2.5 Overfitting -- 4.3 A Few More Terms -- 4.4 Optimizers -- Key Points to Remember -- Quiz -- Further Reading -- Chapter 5 CNN - Convolutional Neural Networks: A Complete Understanding -- Learning Objectives -- 5.1 Introduction -- 5.2 What Is Underfitting, Overfitting and Appropriate Fitting? -- 5.3 Bias/variance - A Quick Learning -- 5.4 Convolutional Neural Networks -- 5.4.1 How Convolution Works -- 5.4.2 How Zero Padding Works -- 5.4.3 How Max Pooling Works -- 5.4.4 The CNN Stack - Architecture -- 5.4.5 What Is the Activation Function? -- 5.4.5.1 Sigmoid Activation Function -- 5.4.5.2 ReLU - Rectified Linear Unit -- 5.4.6 CNN - Model Building - Step By Step -- Key Points to Remember -- Quiz -- Further Reading -- Chapter 6 CNN Architectures: An Evolution -- Learning Objectives -- 6.1 Introduction -- 6.2 LeNET CNN Architecture -- 6.3 VGG16 CNN Architecture -- 6.4 AlexNet CNN Architecture -- 6.5 Other CNN Architectures at a Glance -- Key Points to Remember -- Quiz -- Further Reading -- Chapter 7 Recurrent Neural Networks -- Learning Objectives -- 7.1 Introduction -- 7.2 CNN vs. RNN: A Quick Understanding -- 7.3 RNN vs. Feedforward Neural Networks: A Quick Understanding -- 7.4 Simple RNN -- 7.5 LSTM: Long SHORT-TERM Memory -- 7.6 Gated Recurrent Unit -- Key Points to Remember -- Quiz -- Further Reading -- Chapter 8 Autoencoders -- Learning Objectives -- 8.1 Introduction -- 8.2 What Is an Autoencoder? -- 8.2.1 How Autoencoders Work -- 8.2.2 Properties of Autoencoders -- 8.3 Applications of Autoencoders -- 8.3.1 Data Compression and Dimensionality Reduction -- 8.3.2 Image Denoising -- 8.3.3 Feature Extraction -- 8.3.4 Image Generation -- 8.3.5 Image Colorization -- 8.4 Types of Autoencoders -- 8.4.1 Denoising Autoencoder -- 8.4.2 Vanilla Autoencoder -- 8.4.3 Deep Autoencoder 8.4.4 Sparse Autoencoder -- 8.4.5 Undercomplete Autoencoder -- 8.4.6 Stacked Autoencoder -- 8.4.7 Variational Autoencoder (VAEs) -- 8.4.8 Convolutional Autoencoder -- Key Points to Remember -- Quiz -- Further Reading -- Chapter 9 Generative Models -- Learning Objectives -- 9.1 Introduction -- 9.2 What Is a Generative Model? -- 9.3 What Are Generative Adversarial Networks (GAN)? -- 9.4 Types of GAN -- 9.4.1 Deep Convolutional GANs (DCGANs) -- 9.4.2 Stack GAN -- 9.4.3 Cycle GAN -- 9.4.4 Conditional GAN (CGAN) -- 9.4.5 Info GAN -- 9.5 Applications of GAN -- 9.5.1 Fake Image Generation -- 9.5.2 Image Modification -- 9.5.3 Text to Image/Image to Image Generation -- 9.5.4 Speech Modification -- 9.5.5 Assisting Artists -- 9.6 Implementation of GAN -- Key Points to Remember -- Quiz -- Further Reading -- Chapter 10 Transfer Learning -- Learning Objectives -- 10.1 What Is Transfer Learning? -- 10.2 When Can We Use Transfer Learning? -- 10.3 Example - 1: Cat Or Dog Using Transfer Learning With VGG 16 -- 10.4 Example - 2: Identify Your Relatives' Faces Using Transfer Learning -- 10.5 The Difference Between Transfer Learning and Fine Tuning -- 10.6 Transfer Learning Strategies -- 10.6.1 Same Domain, Task -- 10.6.2 Same Domain, Different Task -- 10.6.3 Different Domain, Same Task -- Key Points to Remember -- Quiz -- Further Reading -- Chapter 11 Intel OpenVino: A Must-Know Deep Learning Toolkit -- Learning Objectives -- 11.1 Introduction -- 11.2 OpenVino Installation Guidelines -- Key Points to Remember -- Quiz -- Further Reading -- Chapter 12 Interview Questions and Answers -- Learning Objectives -- YouTube Sessions On Deep Learning Applications -- Index Neuronales Netz (DE-588)4226127-2 gnd Maschinelles Lernen (DE-588)4193754-5 gnd |
| subject_GND | (DE-588)4226127-2 (DE-588)4193754-5 |
| title | Deep learning a comprehensive guide |
| title_auth | Deep learning a comprehensive guide |
| title_exact_search | Deep learning a comprehensive guide |
| title_exact_search_txtP | Deep learning a comprehensive guide |
| title_full | Deep learning a comprehensive guide Shriram K. Vasudevan, Sini Raj Pulari, Subashri Vasudevan |
| title_fullStr | Deep learning a comprehensive guide Shriram K. Vasudevan, Sini Raj Pulari, Subashri Vasudevan |
| title_full_unstemmed | Deep learning a comprehensive guide Shriram K. Vasudevan, Sini Raj Pulari, Subashri Vasudevan |
| title_short | Deep learning |
| title_sort | deep learning a comprehensive guide |
| title_sub | a comprehensive guide |
| topic | Neuronales Netz (DE-588)4226127-2 gnd Maschinelles Lernen (DE-588)4193754-5 gnd |
| topic_facet | Neuronales Netz Maschinelles Lernen |
| url | https://doi.org/10.1201/9781003185635 |
| work_keys_str_mv | AT vasudevanshriramk deeplearningacomprehensiveguide AT pularisiniraj deeplearningacomprehensiveguide AT vasudevansubashri deeplearningacomprehensiveguide |