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• Leave a review - let other readers know what you think
• Other Books You May Enjoy
• Answers to questions
• Frequently asked questions (FAQs)
• Deep learning for clustering analysis
• Semantic image segmentation

• Leave a review - let other readers know what you think 更新時間：2021-06-18 19:08:39
• Other Books You May Enjoy
• Answers to questions
• Frequently asked questions (FAQs)
• Deep learning for clustering analysis
• Semantic image segmentation
• Capsule networks (CapsNet)
• GANs
• Residual neural networks
• Outlook on emergent DL architectures
• Current trends
• Current trends and outlook
• Movie recommender system using factorization machines
• Using deep reinforcement learning for GridWorld
• Distributed deep learning – video classification using a convolutional-LSTM network
• Stock price prediction using LSTM network
• Real-time object detection using YOLO JavaCV and DL4J
• Image classification using transfer learning
• Sentiment analysis using Word2Vec and the LSTM network
• Image classification using convolutional neural networks
• Cancer type prediction using recurrent type networks
• Titanic survival prediction using MLP and LSTM networks
• Discussion on the completed projects
• Discussion and outlook
• Discussion Current Trends and Outlook
• Answers to questions
• Summary
• Frequently asked questions (FAQs)
• Which one makes more sense ;– ranking or rating?
• Movie rating prediction
• Training and test set preparation
• Converting the dataset into LibFM format
• Movie rating prediction
• Dataset description and exploratory analysis
• Developing a movie recommender system using FMs
• Factorization machines in recommender systems
• The cold-start problem in collaborative-filtering approaches
• The utility matrix
• Model-based collaborative filtering
• Hybrid recommender systems
• Content-based filtering approaches
• Collaborative filtering approaches
• Recommendation approaches
• Recommendation systems
• Developing Movie Recommendation Systems Using Factorization Machines
• Answers to questions
• Summary
• Frequently asked questions (FAQs)
• Playing the GridWorld game
• Network construction and training
• Flattening input for the input layer
• Calculating the reward
• Providing guidance action
• Calculating the action mask
• Calculating agent and goal positions
• Generating the grid
• Developing a GridWorld game using a deep Q-network
• Neural networks as a Q-function
• Introduction to QLearning
• Neural Q-learning
• Utility
• Policy
• Notations in reinforcement learning
• Notation policy and utility for RL
• Playing GridWorld Game Using Deep Reinforcement Learning
• Answers to questions
• Summary
• Frequently asked questions (FAQs)
• Distributed training on AWS deep learning AMI 9.0
• Performance evaluation
• Network creation and training
• Preparing training and test sets
• Simple UI for checking video frames
• Data processing workflow
• Solving the encoding problem
• Preprocessing and feature engineering
• UCF101 – action recognition dataset
• Video classification using convolutional – LSTM
• Distributed training on GPUs with DL4J
• Distributed deep learning across multiple GPUs
• Distributed Deep Learning – Video Classification Using Convolutional LSTM Networks
• Answers to questions
• Summary
• Frequently asked questions (FAQs)
• Evaluating the model
• Restoring the saved model for inferencing
• Network training and saving the trained model
• LSTM network construction
• Preparing the training and test sets
• Data collection and exploratory analysis
• Developing a stock price predictive model
• State-of-the-art automated stock trading
• Stock Price Prediction Using LSTM Network
• Answers to questions
• Summary
• Frequently asked questions (FAQs)
• Step 6 – wrapping up everything and running the application
• Step 5 – Non-max suppression in case of more than one bounding box
• Step 4 – Object detection from image frames
• Step 3 – Feeding generated frames into Tiny YOLO model
• Step 2 – Generating frames from video clips
• Step 1 – Loading a pre-trained YOLO model
• Developing a real-time object detection project
• You Only Look Once (YOLO)
• Object detection from videos
• Convolutional Sliding Window (CSW)
• Object classification localization and detection
• Object detection from images and videos
• Real-Time Object Detection using YOLO JavaCV and DL4J
• Answers to questions
• Summary
• Frequently asked questions (FAQs)
• Making simple inferencing
• Restoring the trained model and inferencing
• Network training and evaluation
• Train and test set preparation
• Architecture choice and adoption
• Dataset collection and description
• Developing an image classifier using transfer learning
• DL4J and transfer learning
• Image classification with pretrained VGG16
• Transfer Learning for Image Classification
• Answers to questions
• Summary
• Frequently asked questions (FAQs)
• Making predictions on sample review texts
• Restoring the trained model and evaluating it on the test set
• Network construction training and saving the model
• Preparing the train and test set using the Word2Vec model
• Sentiment analysis using Word2Vec and LSTM
• Word2Vec pre-trained model
• Description of the sentiment labeled dataset
• Folder structure of the dataset
• Large Movie Review dataset for training and testing
• Datasets and pre-trained model description
• Using Word2Vec for neural word embeddings
• Sentiment analysis is a challenging task
• Sentiment Analysis Using Word2Vec and LSTM Network
• Answers to questions
• Summary
• Frequently asked questions (FAQs)
• Wrapping everything up by executing the main() method
• Submission file generation
• Scoring the model
• Network construction
• Training evaluating and saving the trained CNN models
• Preparing the ND4J dataset
• Image feature extraction
• Extracting image metadata
• Image preprocessing
• Workflow of the overall project
• Removing invalid images
• Description of the dataset
• Problem description
• Multi-label image classification using CNNs
• Fully connected layer (dense layer)
• Pooling and padding operations
• Convolutional operations
• CNN architecture
• Image classification and drawbacks of DNNs
• Multi-Label Image Classification Using Convolutional Neural Networks
• Answers to questions
• Summary
• Frequently asked questions (FAQs)
• Evaluating the model
• Network training
• LSTM network construction
• Dataset preparation
• Recurrent and LSTM networks
• Dataset preparation for training
• Cancer type prediction using an LSTM network
• Evaluating the model
• Network training
• Output layer
• Hidden layer 2
• Hidden layer 1
• Multilayer perceptron network construction
• Titanic survival revisited with DL4J
• Preparing programming environment
• Cancer genomics dataset description
• Deep learning in cancer genomics
• Cancer Types Prediction Using Recurrent Type Networks
• Answers to FAQs
• Summary
• Frequently asked questions (FAQs)
• Evaluating the MLP classifier
• Training MLP classifier
• Feature engineering and input dataset preparation
• Configuring the programming environment
• Problem description
• Deep learning from a disaster – Titanic survival prediction
• Cloud-based platforms for DL
• Deep learning frameworks
• DL frameworks and cloud platforms
• Capsule networks
• Generative adversarial networks
• Residual neural networks
• Emergent architectures
• Recurrent neural networks
• Convolutional neural networks
• Autoencoders
• Deep belief networks
• Multilayer Perceptron
• Deep neural networks
• Neural network architectures
• Activation functions
• Weight optimization
• Weights and biases
• Forward and backward passes
• ANNs and the backpropagation algorithm
• How does an ANN learn?
• A brief history of ANNs
• Biological neurons
• Artificial Neural Networks
• How did DL take ML into next level?
• Delving into deep learning
• Putting ML tasks altogether
• Reinforcement learning
• Unsupervised learning
• Supervised learning
• Working principles of ML algorithms
• A soft introduction to ML
• Getting Started with Deep Learning
• Reviews
• Get in touch
• Conventions used
• Download the color images
• Download the example code files
• To get the most out of this book
• What this book covers
• Who this book is for
• Preface
• Packt is searching for authors like you
• About the reviewer
• About the author
• Contributors
• PacktPub.com
• Why subscribe?
• Packt Upsell
• 版權信息
• 封面

• 封面
• 版權信息
• Packt Upsell
• Why subscribe?
• PacktPub.com
• Contributors
• About the author
• About the reviewer
• Packt is searching for authors like you
• Preface
• Who this book is for
• What this book covers
• To get the most out of this book
• Download the example code files
• Download the color images
• Conventions used
• Get in touch
• Reviews
• Getting Started with Deep Learning
• A soft introduction to ML
• Working principles of ML algorithms
• Supervised learning
• Unsupervised learning
• Reinforcement learning
• Putting ML tasks altogether
• Delving into deep learning
• How did DL take ML into next level?
• Artificial Neural Networks
• Biological neurons
• A brief history of ANNs
• How does an ANN learn?
• ANNs and the backpropagation algorithm
• Forward and backward passes
• Weights and biases
• Weight optimization
• Activation functions
• Neural network architectures
• Deep neural networks
• Multilayer Perceptron
• Deep belief networks
• Autoencoders
• Convolutional neural networks
• Recurrent neural networks
• Emergent architectures
• Residual neural networks
• Generative adversarial networks
• Capsule networks
• DL frameworks and cloud platforms
• Deep learning frameworks
• Cloud-based platforms for DL
• Deep learning from a disaster – Titanic survival prediction
• Problem description
• Configuring the programming environment
• Feature engineering and input dataset preparation
• Training MLP classifier
• Evaluating the MLP classifier
• Frequently asked questions (FAQs)
• Summary
• Answers to FAQs
• Cancer Types Prediction Using Recurrent Type Networks
• Deep learning in cancer genomics
• Cancer genomics dataset description
• Preparing programming environment
• Titanic survival revisited with DL4J
• Multilayer perceptron network construction
• Hidden layer 1
• Hidden layer 2
• Output layer
• Network training
• Evaluating the model
• Cancer type prediction using an LSTM network
• Dataset preparation for training
• Recurrent and LSTM networks
• Dataset preparation
• LSTM network construction
• Network training
• Evaluating the model
• Frequently asked questions (FAQs)
• Summary
• Answers to questions
• Multi-Label Image Classification Using Convolutional Neural Networks
• Image classification and drawbacks of DNNs
• CNN architecture
• Convolutional operations
• Pooling and padding operations
• Fully connected layer (dense layer)
• Multi-label image classification using CNNs
• Problem description
• Description of the dataset
• Removing invalid images
• Workflow of the overall project
• Image preprocessing
• Extracting image metadata
• Image feature extraction
• Preparing the ND4J dataset
• Training evaluating and saving the trained CNN models
• Network construction
• Scoring the model
• Submission file generation
• Wrapping everything up by executing the main() method
• Frequently asked questions (FAQs)
• Summary
• Answers to questions
• Sentiment Analysis Using Word2Vec and LSTM Network
• Sentiment analysis is a challenging task
• Using Word2Vec for neural word embeddings
• Datasets and pre-trained model description
• Large Movie Review dataset for training and testing
• Folder structure of the dataset
• Description of the sentiment labeled dataset
• Word2Vec pre-trained model
• Sentiment analysis using Word2Vec and LSTM
• Preparing the train and test set using the Word2Vec model
• Network construction training and saving the model
• Restoring the trained model and evaluating it on the test set
• Making predictions on sample review texts
• Frequently asked questions (FAQs)
• Summary
• Answers to questions
• Transfer Learning for Image Classification
• Image classification with pretrained VGG16
• DL4J and transfer learning
• Developing an image classifier using transfer learning
• Dataset collection and description
• Architecture choice and adoption
• Train and test set preparation
• Network training and evaluation
• Restoring the trained model and inferencing
• Making simple inferencing
• Frequently asked questions (FAQs)
• Summary
• Answers to questions
• Real-Time Object Detection using YOLO JavaCV and DL4J
• Object detection from images and videos
• Object classification localization and detection
• Convolutional Sliding Window (CSW)
• Object detection from videos
• You Only Look Once (YOLO)
• Developing a real-time object detection project
• Step 1 – Loading a pre-trained YOLO model
• Step 2 – Generating frames from video clips
• Step 3 – Feeding generated frames into Tiny YOLO model
• Step 4 – Object detection from image frames
• Step 5 – Non-max suppression in case of more than one bounding box
• Step 6 – wrapping up everything and running the application
• Frequently asked questions (FAQs)
• Summary
• Answers to questions
• Stock Price Prediction Using LSTM Network
• State-of-the-art automated stock trading
• Developing a stock price predictive model
• Data collection and exploratory analysis
• Preparing the training and test sets
• LSTM network construction
• Network training and saving the trained model
• Restoring the saved model for inferencing
• Evaluating the model
• Frequently asked questions (FAQs)
• Summary
• Answers to questions
• Distributed Deep Learning – Video Classification Using Convolutional LSTM Networks
• Distributed deep learning across multiple GPUs
• Distributed training on GPUs with DL4J
• Video classification using convolutional – LSTM
• UCF101 – action recognition dataset
• Preprocessing and feature engineering
• Solving the encoding problem
• Data processing workflow
• Simple UI for checking video frames
• Preparing training and test sets
• Network creation and training
• Performance evaluation
• Distributed training on AWS deep learning AMI 9.0
• Frequently asked questions (FAQs)
• Summary
• Answers to questions
• Playing GridWorld Game Using Deep Reinforcement Learning
• Notation policy and utility for RL
• Notations in reinforcement learning
• Policy
• Utility
• Neural Q-learning
• Introduction to QLearning
• Neural networks as a Q-function
• Developing a GridWorld game using a deep Q-network
• Generating the grid
• Calculating agent and goal positions
• Calculating the action mask
• Providing guidance action
• Calculating the reward
• Flattening input for the input layer
• Network construction and training
• Playing the GridWorld game
• Frequently asked questions (FAQs)
• Summary
• Answers to questions
• Developing Movie Recommendation Systems Using Factorization Machines
• Recommendation systems
• Recommendation approaches
• Collaborative filtering approaches
• Content-based filtering approaches
• Hybrid recommender systems
• Model-based collaborative filtering
• The utility matrix
• The cold-start problem in collaborative-filtering approaches
• Factorization machines in recommender systems
• Developing a movie recommender system using FMs
• Dataset description and exploratory analysis
• Movie rating prediction
• Converting the dataset into LibFM format
• Training and test set preparation
• Movie rating prediction
• Which one makes more sense ;– ranking or rating?
• Frequently asked questions (FAQs)
• Summary
• Answers to questions
• Discussion Current Trends and Outlook
• Discussion and outlook
• Discussion on the completed projects
• Titanic survival prediction using MLP and LSTM networks
• Cancer type prediction using recurrent type networks
• Image classification using convolutional neural networks
• Sentiment analysis using Word2Vec and the LSTM network
• Image classification using transfer learning
• Real-time object detection using YOLO JavaCV and DL4J
• Stock price prediction using LSTM network
• Distributed deep learning – video classification using a convolutional-LSTM network
• Using deep reinforcement learning for GridWorld
• Movie recommender system using factorization machines
• Current trends and outlook
• Current trends
• Outlook on emergent DL architectures
• Residual neural networks
• GANs
• Capsule networks (CapsNet)
• Semantic image segmentation
• Deep learning for clustering analysis
• Frequently asked questions (FAQs)
• Answers to questions
• Other Books You May Enjoy
• Leave a review - let other readers know what you think 更新時間：2021-06-18 19:08:39