Practical Convolutional Neural Networks :: Implement advanced deep learning models using Python /
This book helps you master CNN, from the basics to the most advanced concepts in CNN such as GANs, instance classification and attention mechanism for vision models and more. You will implement advanced CNN models using complex image and video datasets. By the end of the book you will learn CNN'...
Gespeichert in:
| 1. Verfasser: | |
|---|---|
| Weitere Verfasser: | , |
| Format: | Elektronisch E-Book |
| Sprache: | English |
| Veröffentlicht: |
Birmingham :
Packt Publishing,
2018.
|
| Schlagworte: | |
| Online-Zugang: | DE-862 DE-863 |
| Zusammenfassung: | This book helps you master CNN, from the basics to the most advanced concepts in CNN such as GANs, instance classification and attention mechanism for vision models and more. You will implement advanced CNN models using complex image and video datasets. By the end of the book you will learn CNN's best practices to implement smart ConvNet ... |
| Beschreibung: | Target dataset is small but different from the original training dataset. |
| Beschreibung: | 1 online resource (211 pages) |
| ISBN: | 9781788394147 1788394143 1788392302 9781788392303 |
Internformat
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| 264 | 1 | |a Birmingham : |b Packt Publishing, |c 2018. | |
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| 505 | 0 | |a Cover; Title Page; Copyright and Credits; Packt Upsell; Contributors; Table of Contents; Preface; Chapter 1: Deep Neural Networks â#x80;#x93; Overview; Building blocks of a neural network; Introduction to TensorFlow; Installing TensorFlow; For macOS X/Linux variants; TensorFlow basics; Basic math with TensorFlow; Softmax in TensorFlow; Introduction to the MNIST dataset ; The simplest artificial neural network; Building a single-layer neural network with TensorFlow; Keras deep learning library overview; Layers in the Keras model; Handwritten number recognition with Keras and MNIST. | |
| 505 | 8 | |a Retrieving training and test dataFlattened data; Visualizing the training data; Building the network; Training the network; Testing; Understanding backpropagation ; Summary; Chapter 2: Introduction to Convolutional Neural Networks; History of CNNs; Convolutional neural networks; How do computers interpret images?; Code for visualizing an image ; Dropout; Input layer; Convolutional layer; Convolutional layers in Keras; Pooling layer; Practical example â#x80;#x93; image classification; Image augmentation; Summary; Chapter 3: Build Your First CNN and Performance Optimization. | |
| 505 | 8 | |a CNN architectures and drawbacks of DNNsConvolutional operations; Pooling, stride, and padding operations; Fully connected layer; Convolution and pooling operations in TensorFlow; Applying pooling operations in TensorFlow; Convolution operations in TensorFlow; Training a CNN; Weight and bias initialization; Regularization; Activation functions; Using sigmoid; Using tanh; Using ReLU; Building, training, and evaluating our first CNN; Dataset description; Step 1 â#x80;#x93; Loading the required packages; Step 2 â#x80;#x93; Loading the training/test images to generate train/test set. | |
| 505 | 8 | |a Step 3- Defining CNN hyperparametersStep 4 â#x80;#x93; Constructing the CNN layers; Step 5 â#x80;#x93; Preparing the TensorFlow graph; Step 6 â#x80;#x93; Creating a CNN model; Step 7 â#x80;#x93; Running the TensorFlow graph to train the CNN model; Step 8 â#x80;#x93; Model evaluation; Model performance optimization; Number of hidden layers; Number of neurons per hidden layer; Batch normalization; Advanced regularization and avoiding overfitting; Applying dropout operations with TensorFlow; Which optimizer to use?; Memory tuning; Appropriate layer placement; Building the second CNN by putting everything together. | |
| 505 | 8 | |a Dataset description and preprocessingCreating the CNN model; Training and evaluating the network; Summary; Chapter 4: Popular CNN Model Architectures; Introduction to ImageNet; LeNet; AlexNet architecture; Traffic sign classifiers using AlexNet; VGGNet architecture; VGG16 image classification code example; GoogLeNet architecture; Architecture insights; Inception module; ResNet architecture; Summary; Chapter 5: Transfer Learning; Feature extraction approach; Target dataset is small and is similar to the original training dataset. | |
| 500 | |a Target dataset is small but different from the original training dataset. | ||
| 520 | |a This book helps you master CNN, from the basics to the most advanced concepts in CNN such as GANs, instance classification and attention mechanism for vision models and more. You will implement advanced CNN models using complex image and video datasets. By the end of the book you will learn CNN's best practices to implement smart ConvNet ... | ||
| 588 | 0 | |a Description based on online resource; title from PDF title page (viewed December 01, 2021). | |
| 650 | 0 | |a Neural networks (Computer science) |0 http://id.loc.gov/authorities/subjects/sh90001937 | |
| 650 | 0 | |a Computer vision. |0 http://id.loc.gov/authorities/subjects/sh85029549 | |
| 650 | 0 | |a Machine learning. |0 http://id.loc.gov/authorities/subjects/sh85079324 | |
| 650 | 0 | |a Python (Computer program language) |0 http://id.loc.gov/authorities/subjects/sh96008834 | |
| 650 | 2 | |a Neural Networks, Computer |0 https://id.nlm.nih.gov/mesh/D016571 | |
| 650 | 2 | |a Machine Learning |0 https://id.nlm.nih.gov/mesh/D000069550 | |
| 650 | 6 | |a Réseaux neuronaux (Informatique) | |
| 650 | 6 | |a Vision par ordinateur. | |
| 650 | 6 | |a Apprentissage automatique. | |
| 650 | 6 | |a Python (Langage de programmation) | |
| 650 | 7 | |a Information technology: general issues. |2 bicssc | |
| 650 | 7 | |a Artificial intelligence. |2 bicssc | |
| 650 | 7 | |a Computers |x Intelligence (AI) & Semantics. |2 bisacsh | |
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| 650 | 7 | |a Computers |x Image Processing. |2 bisacsh | |
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| 700 | 1 | |a Sewak, Mohit. | |
| 700 | 1 | |a Pujari, Pradeep. | |
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| 776 | 0 | 8 | |i Print version: |a Karim, Md. Rezaul. |t Practical Convolutional Neural Networks : Implement advanced deep learning models using Python. |d Birmingham : Packt Publishing, ©2018 |
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| contents | Cover; Title Page; Copyright and Credits; Packt Upsell; Contributors; Table of Contents; Preface; Chapter 1: Deep Neural Networks â#x80;#x93; Overview; Building blocks of a neural network; Introduction to TensorFlow; Installing TensorFlow; For macOS X/Linux variants; TensorFlow basics; Basic math with TensorFlow; Softmax in TensorFlow; Introduction to the MNIST dataset ; The simplest artificial neural network; Building a single-layer neural network with TensorFlow; Keras deep learning library overview; Layers in the Keras model; Handwritten number recognition with Keras and MNIST. Retrieving training and test dataFlattened data; Visualizing the training data; Building the network; Training the network; Testing; Understanding backpropagation ; Summary; Chapter 2: Introduction to Convolutional Neural Networks; History of CNNs; Convolutional neural networks; How do computers interpret images?; Code for visualizing an image ; Dropout; Input layer; Convolutional layer; Convolutional layers in Keras; Pooling layer; Practical example â#x80;#x93; image classification; Image augmentation; Summary; Chapter 3: Build Your First CNN and Performance Optimization. CNN architectures and drawbacks of DNNsConvolutional operations; Pooling, stride, and padding operations; Fully connected layer; Convolution and pooling operations in TensorFlow; Applying pooling operations in TensorFlow; Convolution operations in TensorFlow; Training a CNN; Weight and bias initialization; Regularization; Activation functions; Using sigmoid; Using tanh; Using ReLU; Building, training, and evaluating our first CNN; Dataset description; Step 1 â#x80;#x93; Loading the required packages; Step 2 â#x80;#x93; Loading the training/test images to generate train/test set. Step 3- Defining CNN hyperparametersStep 4 â#x80;#x93; Constructing the CNN layers; Step 5 â#x80;#x93; Preparing the TensorFlow graph; Step 6 â#x80;#x93; Creating a CNN model; Step 7 â#x80;#x93; Running the TensorFlow graph to train the CNN model; Step 8 â#x80;#x93; Model evaluation; Model performance optimization; Number of hidden layers; Number of neurons per hidden layer; Batch normalization; Advanced regularization and avoiding overfitting; Applying dropout operations with TensorFlow; Which optimizer to use?; Memory tuning; Appropriate layer placement; Building the second CNN by putting everything together. Dataset description and preprocessingCreating the CNN model; Training and evaluating the network; Summary; Chapter 4: Popular CNN Model Architectures; Introduction to ImageNet; LeNet; AlexNet architecture; Traffic sign classifiers using AlexNet; VGGNet architecture; VGG16 image classification code example; GoogLeNet architecture; Architecture insights; Inception module; ResNet architecture; Summary; Chapter 5: Transfer Learning; Feature extraction approach; Target dataset is small and is similar to the original training dataset. |
| ctrlnum | (OCoLC)1028218878 |
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| id | ZDB-4-EBA-on1028218878 |
| illustrated | Not Illustrated |
| indexdate | 2025-04-11T08:44:10Z |
| institution | BVB |
| isbn | 9781788394147 1788394143 1788392302 9781788392303 |
| language | English |
| oclc_num | 1028218878 |
| open_access_boolean | |
| owner | MAIN DE-862 DE-BY-FWS DE-863 DE-BY-FWS |
| owner_facet | MAIN DE-862 DE-BY-FWS DE-863 DE-BY-FWS |
| physical | 1 online resource (211 pages) |
| psigel | ZDB-4-EBA FWS_PDA_EBA ZDB-4-EBA |
| publishDate | 2018 |
| publishDateSearch | 2018 |
| publishDateSort | 2018 |
| publisher | Packt Publishing, |
| record_format | marc |
| spelling | Karim, Md. Rezaul, author. Practical Convolutional Neural Networks : Implement advanced deep learning models using Python / Rezaul Karim. Birmingham : Packt Publishing, 2018. 1 online resource (211 pages) text txt rdacontent computer c rdamedia online resource cr rdacarrier Cover; Title Page; Copyright and Credits; Packt Upsell; Contributors; Table of Contents; Preface; Chapter 1: Deep Neural Networks â#x80;#x93; Overview; Building blocks of a neural network; Introduction to TensorFlow; Installing TensorFlow; For macOS X/Linux variants; TensorFlow basics; Basic math with TensorFlow; Softmax in TensorFlow; Introduction to the MNIST dataset ; The simplest artificial neural network; Building a single-layer neural network with TensorFlow; Keras deep learning library overview; Layers in the Keras model; Handwritten number recognition with Keras and MNIST. Retrieving training and test dataFlattened data; Visualizing the training data; Building the network; Training the network; Testing; Understanding backpropagation ; Summary; Chapter 2: Introduction to Convolutional Neural Networks; History of CNNs; Convolutional neural networks; How do computers interpret images?; Code for visualizing an image ; Dropout; Input layer; Convolutional layer; Convolutional layers in Keras; Pooling layer; Practical example â#x80;#x93; image classification; Image augmentation; Summary; Chapter 3: Build Your First CNN and Performance Optimization. CNN architectures and drawbacks of DNNsConvolutional operations; Pooling, stride, and padding operations; Fully connected layer; Convolution and pooling operations in TensorFlow; Applying pooling operations in TensorFlow; Convolution operations in TensorFlow; Training a CNN; Weight and bias initialization; Regularization; Activation functions; Using sigmoid; Using tanh; Using ReLU; Building, training, and evaluating our first CNN; Dataset description; Step 1 â#x80;#x93; Loading the required packages; Step 2 â#x80;#x93; Loading the training/test images to generate train/test set. Step 3- Defining CNN hyperparametersStep 4 â#x80;#x93; Constructing the CNN layers; Step 5 â#x80;#x93; Preparing the TensorFlow graph; Step 6 â#x80;#x93; Creating a CNN model; Step 7 â#x80;#x93; Running the TensorFlow graph to train the CNN model; Step 8 â#x80;#x93; Model evaluation; Model performance optimization; Number of hidden layers; Number of neurons per hidden layer; Batch normalization; Advanced regularization and avoiding overfitting; Applying dropout operations with TensorFlow; Which optimizer to use?; Memory tuning; Appropriate layer placement; Building the second CNN by putting everything together. Dataset description and preprocessingCreating the CNN model; Training and evaluating the network; Summary; Chapter 4: Popular CNN Model Architectures; Introduction to ImageNet; LeNet; AlexNet architecture; Traffic sign classifiers using AlexNet; VGGNet architecture; VGG16 image classification code example; GoogLeNet architecture; Architecture insights; Inception module; ResNet architecture; Summary; Chapter 5: Transfer Learning; Feature extraction approach; Target dataset is small and is similar to the original training dataset. Target dataset is small but different from the original training dataset. This book helps you master CNN, from the basics to the most advanced concepts in CNN such as GANs, instance classification and attention mechanism for vision models and more. You will implement advanced CNN models using complex image and video datasets. By the end of the book you will learn CNN's best practices to implement smart ConvNet ... Description based on online resource; title from PDF title page (viewed December 01, 2021). Neural networks (Computer science) http://id.loc.gov/authorities/subjects/sh90001937 Computer vision. http://id.loc.gov/authorities/subjects/sh85029549 Machine learning. http://id.loc.gov/authorities/subjects/sh85079324 Python (Computer program language) http://id.loc.gov/authorities/subjects/sh96008834 Neural Networks, Computer https://id.nlm.nih.gov/mesh/D016571 Machine Learning https://id.nlm.nih.gov/mesh/D000069550 Réseaux neuronaux (Informatique) Vision par ordinateur. Apprentissage automatique. Python (Langage de programmation) Information technology: general issues. bicssc Artificial intelligence. bicssc Computers Intelligence (AI) & Semantics. bisacsh Computers Information Technology. bisacsh Computers Image Processing. bisacsh COMPUTERS General. bisacsh Computer vision fast Machine learning fast Neural networks (Computer science) fast Python (Computer program language) fast Sewak, Mohit. Pujari, Pradeep. has work: Practical convolutional neural networks (Text) https://id.oclc.org/worldcat/entity/E39PCFHPqkK8Kj43xBRrtPvXHP https://id.oclc.org/worldcat/ontology/hasWork Print version: Karim, Md. Rezaul. Practical Convolutional Neural Networks : Implement advanced deep learning models using Python. Birmingham : Packt Publishing, ©2018 |
| spellingShingle | Karim, Md. Rezaul Practical Convolutional Neural Networks : Implement advanced deep learning models using Python / Cover; Title Page; Copyright and Credits; Packt Upsell; Contributors; Table of Contents; Preface; Chapter 1: Deep Neural Networks â#x80;#x93; Overview; Building blocks of a neural network; Introduction to TensorFlow; Installing TensorFlow; For macOS X/Linux variants; TensorFlow basics; Basic math with TensorFlow; Softmax in TensorFlow; Introduction to the MNIST dataset ; The simplest artificial neural network; Building a single-layer neural network with TensorFlow; Keras deep learning library overview; Layers in the Keras model; Handwritten number recognition with Keras and MNIST. Retrieving training and test dataFlattened data; Visualizing the training data; Building the network; Training the network; Testing; Understanding backpropagation ; Summary; Chapter 2: Introduction to Convolutional Neural Networks; History of CNNs; Convolutional neural networks; How do computers interpret images?; Code for visualizing an image ; Dropout; Input layer; Convolutional layer; Convolutional layers in Keras; Pooling layer; Practical example â#x80;#x93; image classification; Image augmentation; Summary; Chapter 3: Build Your First CNN and Performance Optimization. CNN architectures and drawbacks of DNNsConvolutional operations; Pooling, stride, and padding operations; Fully connected layer; Convolution and pooling operations in TensorFlow; Applying pooling operations in TensorFlow; Convolution operations in TensorFlow; Training a CNN; Weight and bias initialization; Regularization; Activation functions; Using sigmoid; Using tanh; Using ReLU; Building, training, and evaluating our first CNN; Dataset description; Step 1 â#x80;#x93; Loading the required packages; Step 2 â#x80;#x93; Loading the training/test images to generate train/test set. Step 3- Defining CNN hyperparametersStep 4 â#x80;#x93; Constructing the CNN layers; Step 5 â#x80;#x93; Preparing the TensorFlow graph; Step 6 â#x80;#x93; Creating a CNN model; Step 7 â#x80;#x93; Running the TensorFlow graph to train the CNN model; Step 8 â#x80;#x93; Model evaluation; Model performance optimization; Number of hidden layers; Number of neurons per hidden layer; Batch normalization; Advanced regularization and avoiding overfitting; Applying dropout operations with TensorFlow; Which optimizer to use?; Memory tuning; Appropriate layer placement; Building the second CNN by putting everything together. Dataset description and preprocessingCreating the CNN model; Training and evaluating the network; Summary; Chapter 4: Popular CNN Model Architectures; Introduction to ImageNet; LeNet; AlexNet architecture; Traffic sign classifiers using AlexNet; VGGNet architecture; VGG16 image classification code example; GoogLeNet architecture; Architecture insights; Inception module; ResNet architecture; Summary; Chapter 5: Transfer Learning; Feature extraction approach; Target dataset is small and is similar to the original training dataset. Neural networks (Computer science) http://id.loc.gov/authorities/subjects/sh90001937 Computer vision. http://id.loc.gov/authorities/subjects/sh85029549 Machine learning. http://id.loc.gov/authorities/subjects/sh85079324 Python (Computer program language) http://id.loc.gov/authorities/subjects/sh96008834 Neural Networks, Computer https://id.nlm.nih.gov/mesh/D016571 Machine Learning https://id.nlm.nih.gov/mesh/D000069550 Réseaux neuronaux (Informatique) Vision par ordinateur. Apprentissage automatique. Python (Langage de programmation) Information technology: general issues. bicssc Artificial intelligence. bicssc Computers Intelligence (AI) & Semantics. bisacsh Computers Information Technology. bisacsh Computers Image Processing. bisacsh COMPUTERS General. bisacsh Computer vision 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/sh85029549 http://id.loc.gov/authorities/subjects/sh85079324 http://id.loc.gov/authorities/subjects/sh96008834 https://id.nlm.nih.gov/mesh/D016571 https://id.nlm.nih.gov/mesh/D000069550 |
| title | Practical Convolutional Neural Networks : Implement advanced deep learning models using Python / |
| title_auth | Practical Convolutional Neural Networks : Implement advanced deep learning models using Python / |
| title_exact_search | Practical Convolutional Neural Networks : Implement advanced deep learning models using Python / |
| title_full | Practical Convolutional Neural Networks : Implement advanced deep learning models using Python / Rezaul Karim. |
| title_fullStr | Practical Convolutional Neural Networks : Implement advanced deep learning models using Python / Rezaul Karim. |
| title_full_unstemmed | Practical Convolutional Neural Networks : Implement advanced deep learning models using Python / Rezaul Karim. |
| title_short | Practical Convolutional Neural Networks : |
| title_sort | practical convolutional neural networks implement advanced deep learning models using python |
| title_sub | Implement advanced deep learning models using Python / |
| topic | Neural networks (Computer science) http://id.loc.gov/authorities/subjects/sh90001937 Computer vision. http://id.loc.gov/authorities/subjects/sh85029549 Machine learning. http://id.loc.gov/authorities/subjects/sh85079324 Python (Computer program language) http://id.loc.gov/authorities/subjects/sh96008834 Neural Networks, Computer https://id.nlm.nih.gov/mesh/D016571 Machine Learning https://id.nlm.nih.gov/mesh/D000069550 Réseaux neuronaux (Informatique) Vision par ordinateur. Apprentissage automatique. Python (Langage de programmation) Information technology: general issues. bicssc Artificial intelligence. bicssc Computers Intelligence (AI) & Semantics. bisacsh Computers Information Technology. bisacsh Computers Image Processing. bisacsh COMPUTERS General. bisacsh Computer vision fast Machine learning fast Neural networks (Computer science) fast Python (Computer program language) fast |
| topic_facet | Neural networks (Computer science) Computer vision. Machine learning. Python (Computer program language) Neural Networks, Computer Machine Learning Réseaux neuronaux (Informatique) Vision par ordinateur. Apprentissage automatique. Python (Langage de programmation) Information technology: general issues. Artificial intelligence. Computers Intelligence (AI) & Semantics. Computers Information Technology. Computers Image Processing. COMPUTERS General. Computer vision Machine learning |
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