Modern Computer Vision with Pytorch: Explore Deep Learning Concepts and Implement over 50 Real-World Image Applications.
Starting from the basics of neural networks, this book covers over 50 applications of computer vision and helps you to gain a solid understanding of the theory of various architectures before implementing them. Each use case is accompanied by a notebook in GitHub with ready-to-execute code and self-...
Gespeichert in:
| 1. Verfasser: | |
|---|---|
| Weitere Verfasser: | |
| Format: | Elektronisch E-Book |
| Sprache: | English |
| Veröffentlicht: |
Birmingham :
Packt Publishing, Limited,
2020.
|
| Schlagworte: | |
| Online-Zugang: | Volltext |
| Zusammenfassung: | Starting from the basics of neural networks, this book covers over 50 applications of computer vision and helps you to gain a solid understanding of the theory of various architectures before implementing them. Each use case is accompanied by a notebook in GitHub with ready-to-execute code and self-assessment questions. |
| Beschreibung: | Description based upon print version of record. |
| Beschreibung: | 1 online resource (805 p.) |
| ISBN: | 9781839216534 1839216530 |
Internformat
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| 260 | |a Birmingham : |b Packt Publishing, Limited, |c 2020. | ||
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| 500 | |a Description based upon print version of record. | ||
| 520 | |a Starting from the basics of neural networks, this book covers over 50 applications of computer vision and helps you to gain a solid understanding of the theory of various architectures before implementing them. Each use case is accompanied by a notebook in GitHub with ready-to-execute code and self-assessment questions. | ||
| 505 | 0 | |a Cover -- Title Page -- Copyright and Credits -- Dedication -- About Packt -- Contributors -- Table of Contents -- Preface -- Section 1 -- Fundamentals of Deep Learning for Computer Vision -- Chapter 1: Artificial Neural Network Fundamentals -- Comparing AI and traditional machine learning -- Learning about the artificial neural network building blocks -- Implementing feedforward propagation -- Calculating the hidden layer unit values -- Applying the activation function -- Calculating the output layer values -- Calculating loss values -- Calculating loss during continuous variable prediction | |
| 505 | 8 | |a Calculating loss during categorical variable prediction -- Feedforward propagation in code -- Activation functions in code -- Loss functions in code -- Implementing backpropagation -- Gradient descent in code -- Implementing backpropagation using the chain rule -- Putting feedforward propagation and backpropagation together -- Understanding the impact of the learning rate -- Summarizing the training process of a neural network -- Summary -- Questions -- Chapter 2: PyTorch Fundamentals -- Installing PyTorch -- PyTorch tensors -- Initializing a tensor -- Operations on tensors | |
| 505 | 8 | |a Auto gradients of tensor objects -- Advantages of PyTorch's tensors over NumPy's ndarrays -- Building a neural network using PyTorch -- Dataset, DataLoader, and batch size -- Predicting on new data points -- Implementing a custom loss function -- Fetching the values of intermediate layers -- Using a sequential method to build a neural network -- Saving and loading a PyTorch model -- state dict -- Saving -- Loading -- Summary -- Questions -- Chapter 3: Building a Deep Neural Network with PyTorch -- Representing an image -- Converting images into structured arrays and scalars | |
| 505 | 8 | |a Why leverage neural networks for image analysis? -- Preparing our data for image classification -- Training a neural network -- Scaling a dataset to improve model accuracy -- Understanding the impact of varying the batch size -- Batch size of 32 -- Batch size of 10,000 -- Understanding the impact of varying the loss optimizer -- Understanding the impact of varying the learning rate -- Impact of the learning rate on a scaled dataset -- High learning rate -- Medium learning rate -- Low learning rate -- Parameter distribution across layers for different learning rates | |
| 505 | 8 | |a Impact of varying the learning rate on a non-scaled dataset -- Understanding the impact of learning rate annealing -- Building a deeper neural network -- Understanding the impact of batch normalization -- Very small input values without batch normalization -- Very small input values with batch normalization -- The concept of overfitting -- Impact of adding dropout -- Impact of regularization -- L1 regularization -- L2 regularization -- Summary -- Questions -- Section 2 -- Object Classification and Detection -- Chapter 4: Introducing Convolutional Neural Networks | |
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| 776 | 0 | 8 | |i Print version: |a Ayyadevara, V. Kishore |t Modern Computer Vision with Pytorch : Explore Deep Learning Concepts and Implement over 50 Real-World Image Applications |d Birmingham : Packt Publishing, Limited,c2020 |z 9781839213472 |
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| contents | Cover -- Title Page -- Copyright and Credits -- Dedication -- About Packt -- Contributors -- Table of Contents -- Preface -- Section 1 -- Fundamentals of Deep Learning for Computer Vision -- Chapter 1: Artificial Neural Network Fundamentals -- Comparing AI and traditional machine learning -- Learning about the artificial neural network building blocks -- Implementing feedforward propagation -- Calculating the hidden layer unit values -- Applying the activation function -- Calculating the output layer values -- Calculating loss values -- Calculating loss during continuous variable prediction Calculating loss during categorical variable prediction -- Feedforward propagation in code -- Activation functions in code -- Loss functions in code -- Implementing backpropagation -- Gradient descent in code -- Implementing backpropagation using the chain rule -- Putting feedforward propagation and backpropagation together -- Understanding the impact of the learning rate -- Summarizing the training process of a neural network -- Summary -- Questions -- Chapter 2: PyTorch Fundamentals -- Installing PyTorch -- PyTorch tensors -- Initializing a tensor -- Operations on tensors Auto gradients of tensor objects -- Advantages of PyTorch's tensors over NumPy's ndarrays -- Building a neural network using PyTorch -- Dataset, DataLoader, and batch size -- Predicting on new data points -- Implementing a custom loss function -- Fetching the values of intermediate layers -- Using a sequential method to build a neural network -- Saving and loading a PyTorch model -- state dict -- Saving -- Loading -- Summary -- Questions -- Chapter 3: Building a Deep Neural Network with PyTorch -- Representing an image -- Converting images into structured arrays and scalars Why leverage neural networks for image analysis? -- Preparing our data for image classification -- Training a neural network -- Scaling a dataset to improve model accuracy -- Understanding the impact of varying the batch size -- Batch size of 32 -- Batch size of 10,000 -- Understanding the impact of varying the loss optimizer -- Understanding the impact of varying the learning rate -- Impact of the learning rate on a scaled dataset -- High learning rate -- Medium learning rate -- Low learning rate -- Parameter distribution across layers for different learning rates Impact of varying the learning rate on a non-scaled dataset -- Understanding the impact of learning rate annealing -- Building a deeper neural network -- Understanding the impact of batch normalization -- Very small input values without batch normalization -- Very small input values with batch normalization -- The concept of overfitting -- Impact of adding dropout -- Impact of regularization -- L1 regularization -- L2 regularization -- Summary -- Questions -- Section 2 -- Object Classification and Detection -- Chapter 4: Introducing Convolutional Neural Networks |
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| dewey-search | 006.3/2 |
| dewey-sort | 16.3 12 |
| dewey-tens | 000 - Computer science, information, general works |
| discipline | Informatik |
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| id | ZDB-4-EBA-on1225548805 |
| illustrated | Not Illustrated |
| indexdate | 2024-11-27T13:30:08Z |
| institution | BVB |
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| spelling | Ayyadevara, V. Kishore. Modern Computer Vision with Pytorch [electronic resource] : Explore Deep Learning Concepts and Implement over 50 Real-World Image Applications. Birmingham : Packt Publishing, Limited, 2020. 1 online resource (805 p.) text txt rdacontent computer c rdamedia online resource cr rdacarrier Description based upon print version of record. Starting from the basics of neural networks, this book covers over 50 applications of computer vision and helps you to gain a solid understanding of the theory of various architectures before implementing them. Each use case is accompanied by a notebook in GitHub with ready-to-execute code and self-assessment questions. Cover -- Title Page -- Copyright and Credits -- Dedication -- About Packt -- Contributors -- Table of Contents -- Preface -- Section 1 -- Fundamentals of Deep Learning for Computer Vision -- Chapter 1: Artificial Neural Network Fundamentals -- Comparing AI and traditional machine learning -- Learning about the artificial neural network building blocks -- Implementing feedforward propagation -- Calculating the hidden layer unit values -- Applying the activation function -- Calculating the output layer values -- Calculating loss values -- Calculating loss during continuous variable prediction Calculating loss during categorical variable prediction -- Feedforward propagation in code -- Activation functions in code -- Loss functions in code -- Implementing backpropagation -- Gradient descent in code -- Implementing backpropagation using the chain rule -- Putting feedforward propagation and backpropagation together -- Understanding the impact of the learning rate -- Summarizing the training process of a neural network -- Summary -- Questions -- Chapter 2: PyTorch Fundamentals -- Installing PyTorch -- PyTorch tensors -- Initializing a tensor -- Operations on tensors Auto gradients of tensor objects -- Advantages of PyTorch's tensors over NumPy's ndarrays -- Building a neural network using PyTorch -- Dataset, DataLoader, and batch size -- Predicting on new data points -- Implementing a custom loss function -- Fetching the values of intermediate layers -- Using a sequential method to build a neural network -- Saving and loading a PyTorch model -- state dict -- Saving -- Loading -- Summary -- Questions -- Chapter 3: Building a Deep Neural Network with PyTorch -- Representing an image -- Converting images into structured arrays and scalars Why leverage neural networks for image analysis? -- Preparing our data for image classification -- Training a neural network -- Scaling a dataset to improve model accuracy -- Understanding the impact of varying the batch size -- Batch size of 32 -- Batch size of 10,000 -- Understanding the impact of varying the loss optimizer -- Understanding the impact of varying the learning rate -- Impact of the learning rate on a scaled dataset -- High learning rate -- Medium learning rate -- Low learning rate -- Parameter distribution across layers for different learning rates Impact of varying the learning rate on a non-scaled dataset -- Understanding the impact of learning rate annealing -- Building a deeper neural network -- Understanding the impact of batch normalization -- Very small input values without batch normalization -- Very small input values with batch normalization -- The concept of overfitting -- Impact of adding dropout -- Impact of regularization -- L1 regularization -- L2 regularization -- Summary -- Questions -- Section 2 -- Object Classification and Detection -- Chapter 4: Introducing Convolutional Neural Networks Neural networks (Computer science) http://id.loc.gov/authorities/subjects/sh90001937 Machine learning. http://id.loc.gov/authorities/subjects/sh85079324 Artificial intelligence. http://id.loc.gov/authorities/subjects/sh85008180 Python (Computer program language) http://id.loc.gov/authorities/subjects/sh96008834 Neural Networks, Computer https://id.nlm.nih.gov/mesh/D016571 Artificial Intelligence https://id.nlm.nih.gov/mesh/D001185 Machine Learning https://id.nlm.nih.gov/mesh/D000069550 Réseaux neuronaux (Informatique) Apprentissage automatique. Intelligence artificielle. Python (Langage de programmation) artificial intelligence. aat Mathematical theory of computation. bicssc Machine learning. bicssc Neural networks & fuzzy systems. bicssc Computers Image Processing. bisacsh Computers Machine Theory. bisacsh Computers Neural Networks. bisacsh Artificial intelligence fast Machine learning fast Neural networks (Computer science) fast Python (Computer program language) fast Reddy, Yeshwanth. has work: Modern Computer Vision with Pytorch (Text) https://id.oclc.org/worldcat/entity/E39PCGD9mDkwtXG6b8wY6T6BT3 https://id.oclc.org/worldcat/ontology/hasWork Print version: Ayyadevara, V. Kishore Modern Computer Vision with Pytorch : Explore Deep Learning Concepts and Implement over 50 Real-World Image Applications Birmingham : Packt Publishing, Limited,c2020 9781839213472 FWS01 ZDB-4-EBA FWS_PDA_EBA https://search.ebscohost.com/login.aspx?direct=true&scope=site&db=nlebk&AN=2695335 Volltext |
| spellingShingle | Ayyadevara, V. Kishore Modern Computer Vision with Pytorch Explore Deep Learning Concepts and Implement over 50 Real-World Image Applications. Cover -- Title Page -- Copyright and Credits -- Dedication -- About Packt -- Contributors -- Table of Contents -- Preface -- Section 1 -- Fundamentals of Deep Learning for Computer Vision -- Chapter 1: Artificial Neural Network Fundamentals -- Comparing AI and traditional machine learning -- Learning about the artificial neural network building blocks -- Implementing feedforward propagation -- Calculating the hidden layer unit values -- Applying the activation function -- Calculating the output layer values -- Calculating loss values -- Calculating loss during continuous variable prediction Calculating loss during categorical variable prediction -- Feedforward propagation in code -- Activation functions in code -- Loss functions in code -- Implementing backpropagation -- Gradient descent in code -- Implementing backpropagation using the chain rule -- Putting feedforward propagation and backpropagation together -- Understanding the impact of the learning rate -- Summarizing the training process of a neural network -- Summary -- Questions -- Chapter 2: PyTorch Fundamentals -- Installing PyTorch -- PyTorch tensors -- Initializing a tensor -- Operations on tensors Auto gradients of tensor objects -- Advantages of PyTorch's tensors over NumPy's ndarrays -- Building a neural network using PyTorch -- Dataset, DataLoader, and batch size -- Predicting on new data points -- Implementing a custom loss function -- Fetching the values of intermediate layers -- Using a sequential method to build a neural network -- Saving and loading a PyTorch model -- state dict -- Saving -- Loading -- Summary -- Questions -- Chapter 3: Building a Deep Neural Network with PyTorch -- Representing an image -- Converting images into structured arrays and scalars Why leverage neural networks for image analysis? -- Preparing our data for image classification -- Training a neural network -- Scaling a dataset to improve model accuracy -- Understanding the impact of varying the batch size -- Batch size of 32 -- Batch size of 10,000 -- Understanding the impact of varying the loss optimizer -- Understanding the impact of varying the learning rate -- Impact of the learning rate on a scaled dataset -- High learning rate -- Medium learning rate -- Low learning rate -- Parameter distribution across layers for different learning rates Impact of varying the learning rate on a non-scaled dataset -- Understanding the impact of learning rate annealing -- Building a deeper neural network -- Understanding the impact of batch normalization -- Very small input values without batch normalization -- Very small input values with batch normalization -- The concept of overfitting -- Impact of adding dropout -- Impact of regularization -- L1 regularization -- L2 regularization -- Summary -- Questions -- Section 2 -- Object Classification and Detection -- Chapter 4: Introducing Convolutional Neural Networks Neural networks (Computer science) http://id.loc.gov/authorities/subjects/sh90001937 Machine learning. http://id.loc.gov/authorities/subjects/sh85079324 Artificial intelligence. http://id.loc.gov/authorities/subjects/sh85008180 Python (Computer program language) http://id.loc.gov/authorities/subjects/sh96008834 Neural Networks, Computer https://id.nlm.nih.gov/mesh/D016571 Artificial Intelligence https://id.nlm.nih.gov/mesh/D001185 Machine Learning https://id.nlm.nih.gov/mesh/D000069550 Réseaux neuronaux (Informatique) Apprentissage automatique. Intelligence artificielle. Python (Langage de programmation) artificial intelligence. aat Mathematical theory of computation. bicssc Machine learning. bicssc Neural networks & fuzzy systems. bicssc Computers Image Processing. bisacsh Computers Machine Theory. bisacsh Computers Neural Networks. bisacsh Artificial intelligence fast Machine learning fast Neural networks (Computer science) fast Python (Computer program language) fast |
| subject_GND | http://id.loc.gov/authorities/subjects/sh90001937 http://id.loc.gov/authorities/subjects/sh85079324 http://id.loc.gov/authorities/subjects/sh85008180 http://id.loc.gov/authorities/subjects/sh96008834 https://id.nlm.nih.gov/mesh/D016571 https://id.nlm.nih.gov/mesh/D001185 https://id.nlm.nih.gov/mesh/D000069550 |
| title | Modern Computer Vision with Pytorch Explore Deep Learning Concepts and Implement over 50 Real-World Image Applications. |
| title_auth | Modern Computer Vision with Pytorch Explore Deep Learning Concepts and Implement over 50 Real-World Image Applications. |
| title_exact_search | Modern Computer Vision with Pytorch Explore Deep Learning Concepts and Implement over 50 Real-World Image Applications. |
| title_full | Modern Computer Vision with Pytorch [electronic resource] : Explore Deep Learning Concepts and Implement over 50 Real-World Image Applications. |
| title_fullStr | Modern Computer Vision with Pytorch [electronic resource] : Explore Deep Learning Concepts and Implement over 50 Real-World Image Applications. |
| title_full_unstemmed | Modern Computer Vision with Pytorch [electronic resource] : Explore Deep Learning Concepts and Implement over 50 Real-World Image Applications. |
| title_short | Modern Computer Vision with Pytorch |
| title_sort | modern computer vision with pytorch explore deep learning concepts and implement over 50 real world image applications |
| title_sub | Explore Deep Learning Concepts and Implement over 50 Real-World Image Applications. |
| topic | Neural networks (Computer science) http://id.loc.gov/authorities/subjects/sh90001937 Machine learning. http://id.loc.gov/authorities/subjects/sh85079324 Artificial intelligence. http://id.loc.gov/authorities/subjects/sh85008180 Python (Computer program language) http://id.loc.gov/authorities/subjects/sh96008834 Neural Networks, Computer https://id.nlm.nih.gov/mesh/D016571 Artificial Intelligence https://id.nlm.nih.gov/mesh/D001185 Machine Learning https://id.nlm.nih.gov/mesh/D000069550 Réseaux neuronaux (Informatique) Apprentissage automatique. Intelligence artificielle. Python (Langage de programmation) artificial intelligence. aat Mathematical theory of computation. bicssc Machine learning. bicssc Neural networks & fuzzy systems. bicssc Computers Image Processing. bisacsh Computers Machine Theory. bisacsh Computers Neural Networks. bisacsh Artificial intelligence fast Machine learning fast Neural networks (Computer science) fast Python (Computer program language) fast |
| topic_facet | Neural networks (Computer science) Machine learning. Artificial intelligence. Python (Computer program language) Neural Networks, Computer Artificial Intelligence Machine Learning Réseaux neuronaux (Informatique) Apprentissage automatique. Intelligence artificielle. Python (Langage de programmation) artificial intelligence. Mathematical theory of computation. Neural networks & fuzzy systems. Computers Image Processing. Computers Machine Theory. Computers Neural Networks. Artificial intelligence Machine learning |
| url | https://search.ebscohost.com/login.aspx?direct=true&scope=site&db=nlebk&AN=2695335 |
| work_keys_str_mv | AT ayyadevaravkishore moderncomputervisionwithpytorchexploredeeplearningconceptsandimplementover50realworldimageapplications AT reddyyeshwanth moderncomputervisionwithpytorchexploredeeplearningconceptsandimplementover50realworldimageapplications |