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Keras 2.x Projects
Keras2.xProjectsexplainshowtoleveragethepowerofKerastobuildandtrainstate-of-the-artdeeplearningmodelsthroughaseriesofpracticalprojectsthatlookatarangeofreal-worldapplicationareas.Tobeginwith,youwillquicklysetupadeeplearningenvironmentbyinstallingtheKeraslibrary.Througheachoftheprojects,youwillexploreandlearntheadvancedconceptsofdeeplearningandwilllearnhowtocomputeandrunyourdeeplearningmodelsusingtheadvancedofferingsofKeras.Youwilltrainfully-connectedmultilayernetworks,convolutionalneuralnetworks,recurrentneuralnetworks,autoencodersandgenerativeadversarialnetworksusingreal-worldtrainingdatasets.Theprojectsyouwillundertakeareallbasedonreal-worldscenariosofallcomplexitylevels,coveringtopicssuchaslanguagerecognition,stockvolatility,energyconsumptionprediction,fasterobjectclassificationforself-drivingvehicles,andmore.Bytheendofthisbook,youwillbewellversedwithdeeplearninganditsimplementationwithKeras.Youwillhavealltheknowledgeyouneedtotrainyourowndeeplearningmodelstosolvedifferentkindsofproblems.
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- Other Books You May Enjoy
- Summary
- Inverse reinforcement learning
- Mutation
- Selection
品牌：中圖公司
上架時間：2021-07-02 12:36:50
出版社：Packt Publishing
本書數字版權由中圖公司提供，并由其授權上海閱文信息技術有限公司制作發行
- Leave a review - let other readers know what you think 更新時間：2021-07-02 14:37:02
- Other Books You May Enjoy
- Summary
- Inverse reinforcement learning
- Mutation
- Selection
- The fitness function
- Introducing the genetic algorithm
- Genetic programming and evolutionary strategies
- Differentiable neural computer
- Amazon Web Services
- Azure Machine Learning Studio
- Google Cloud ML Engine
- Auto-Keras
- Automated machine learning
- Generative adversarial networks
- Deep belief network
- Restricted Boltzmann machine
- Long short-term memory
- Recurrent neural networks
- Convolutional neural networks
- Deep feedforward network
- Deep learning methods
- What is Next?
- Summary
- Keras deep neural network model
- Data preparation
- Exploratory analysis
- Implementing a DNN to label sentences
- Lemmatization
- Stemming
- Stemming and lemmatization
- Part-of-speech tagger
- Word and sentence tokenize
- Brown corpus
- Corpora
- Getting started with the NLTK
- The Natural Language Toolkit
- GATE
- Apache OpenNLP
- The Stanford NLP Group software
- The Natural Language Toolkit
- Natural language processing tools
- Semantic role labeling
- Syntactic parsing
- Named entity recognition
- Shallow parsing
- Part-of-speech tagging
- Tokenization
- Sentence splitting
- NLP methods
- Sentiment analysis
- Automatic translation
- Automatic summarization
- Question-answering
- Information extraction
- Information retrieval
- NLP applications
- Automatic processing problems
- Pragmatic analysis
- Semantic analysis
- Syntax analysis
- Morphology analysis
- NLP phases
- Natural language processing
- Reuters Newswire Topics Classifier in Keras
- Summary
- Deep Q-learning solution
- Q-learning solution
- The CartPole system
- OpenAI Gym installation and methods
- DQN to control a robot's mobility
- Keras-RL library
- Deep Q-learning
- Q-learning
- Keras DQNs
- Temporal difference learning
- Monte Carlo methods
- Dynamic Programming
- Reinforcement learning algorithms
- Agent-environment interface
- Reinforcement learning basics
- OpenAI Gym
- The environment for controlling robot mobility
- Automatic control
- Fourth-generation robots
- Third-generation robots
- Second-generation robots
- First-generation robots
- Short robotics timeline
- Three laws of robotics
- Robot control overview
- Robot Control System Using Deep Reinforcement Learning
- Summary
- Exploring model results
- Keras model architecture
- Min–max normalization
- The MNIST dataset
- Implementing autoencoder Keras layers to reconstruct handwritten digit images
- The Keras autoencoders model
- The adversarial autoencoder
- The generative adversarial network
- Variational autoencoders
- Autoencoders
- The restricted Boltzmann machine
- Generative neural networks
- Approaches to the problem
- Optical character recognition
- Image recognition
- Image digitization
- Basic concepts of image recognition
- Reconstruction of Handwritten Digit Images Using Autoencoders
- Summary
- Keras LSTM model
- Data splitting
- Data scaling
- Exploratory analysis
- Implementing an LSTM to forecast stock volatility
- Long short-term memory in Keras
- Autoregressive integrated moving average models
- Autoregressive moving average model
- Moving average models
- Autoregressive models
- Time series models
- Estimating the seasonality component
- Estimation of the trend component
- The classical approach to time series
- Time series analysis
- Qualitative methods
- Quantitative methods
- Forecasting methods
- Forecast horizon
- The basics of forecasting
- Stock Volatility Forecasting Using Long Short-Term Memory
- Summary
- Exploring model results
- Keras recurrent neural network model
- Exploratory analysis
- IMDB Movie reviews dataset
- Classifying sentiment in movie reviews using an RNN
- Long short-term memory network
- Elman neural networks
- Hopfield recurrent neural networks
- Recursive neural networks
- Fully recurrent neural networks
- Recurrent neural networks
- Lexicon and semantics analysis
- The next challenges for sentiment analysis
- Sentiment analysis techniques
- Sentiment analysis basic concepts
- Movie Reviews Sentiment Analysis Using Recurrent Neural Networks
- Summary
- Exploring the model's results
- Using Keras in the CNN model
- Data scaling
- Exploratory analysis
- Implementing a CNN for object recognition
- GoogleNet
- VGG Net
- ResNet
- AlexNet
- LeNet-5
- Common CNN architecture
- Structure of a CNN
- Fully connected layer
- Rectified linear units
- Pooling layers
- Convolution layer
- Convolutional neural networks
- Understanding computer vision concepts
- Fashion Article Recognition Using Convolutional Neural Networks
- Summary
- Improving the model performance by removing outliers
- Building a Keras deep neural network model
- Data scaling
- Data visualization
- Exploratory analysis
- Implementing multilayer neural networks in Keras
- Multilayer neural networks
- Rectified linear unit
- Hyperbolic tangent
- Sigmoid
- Unit step activation function
- Types of activation functions
- Weights and biases
- Understanding the structure of neural networks
- Semi-supervised learning
- Unsupervised learning
- Supervised learning
- Learning paradigms
- Architecture of ANNs
- Basic concepts of ANNs
- Concrete Quality Prediction Using Deep Neural Networks
- Summary
- Keras binary classifier
- Data visualization
- Data scaling
- Handling missing data in Python
- Exploratory analysis
- Pattern recognition using a Keras neural network
- Bayes' theorem
- Bayesian decision theory
- Support vector machine
- K-nearest neighbors
- Discriminant analysis
- Gaussian mixture models
- Naive Bayes algorithm
- Classification algorithms
- Different types of classification
- Basics of classification problems
- Heart Disease Classification with Neural Networks
- Summary
- Multiple linear regression model
- Neural network Keras model
- Data splitting
- Exploratory analysis
- Neural networks for regression using Keras
- Multiple linear regression concepts
- Creating a linear regression model
- Different types of regression
- Basic regression concepts
- Defining a regression problem
- Modeling Real Estate Using Regression Analysis
- Summary
- Keras functional API model architecture
- The Keras sequential model architecture
- Model fitting in Keras
- Keras installation and configuration
- Installing the backend engine
- Optional dependencies
- Installation
- CNTK
- Theano
- TensorFlow
- Keras backend options
- Introduction to Keras
- Getting Started with Keras
- 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
- Packt.com
- Why subscribe?
- About Packt
- Keras 2.x Projects
- Copyright and Credits
- Title Page
- coverpage
- coverpage
- Title Page
- Copyright and Credits
- Keras 2.x Projects
- About Packt
- Why subscribe?
- Packt.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 Keras
- Introduction to Keras
- Keras backend options
- TensorFlow
- Theano
- CNTK
- Installation
- Optional dependencies
- Installing the backend engine
- Keras installation and configuration
- Model fitting in Keras
- The Keras sequential model architecture
- Keras functional API model architecture
- Summary
- Modeling Real Estate Using Regression Analysis
- Defining a regression problem
- Basic regression concepts
- Different types of regression
- Creating a linear regression model
- Multiple linear regression concepts
- Neural networks for regression using Keras
- Exploratory analysis
- Data splitting
- Neural network Keras model
- Multiple linear regression model
- Summary
- Heart Disease Classification with Neural Networks
- Basics of classification problems
- Different types of classification
- Classification algorithms
- Naive Bayes algorithm
- Gaussian mixture models
- Discriminant analysis
- K-nearest neighbors
- Support vector machine
- Bayesian decision theory
- Bayes' theorem
- Pattern recognition using a Keras neural network
- Exploratory analysis
- Handling missing data in Python
- Data scaling
- Data visualization
- Keras binary classifier
- Summary
- Concrete Quality Prediction Using Deep Neural Networks
- Basic concepts of ANNs
- Architecture of ANNs
- Learning paradigms
- Supervised learning
- Unsupervised learning
- Semi-supervised learning
- Understanding the structure of neural networks
- Weights and biases
- Types of activation functions
- Unit step activation function
- Sigmoid
- Hyperbolic tangent
- Rectified linear unit
- Multilayer neural networks
- Implementing multilayer neural networks in Keras
- Exploratory analysis
- Data visualization
- Data scaling
- Building a Keras deep neural network model
- Improving the model performance by removing outliers
- Summary
- Fashion Article Recognition Using Convolutional Neural Networks
- Understanding computer vision concepts
- Convolutional neural networks
- Convolution layer
- Pooling layers
- Rectified linear units
- Fully connected layer
- Structure of a CNN
- Common CNN architecture
- LeNet-5
- AlexNet
- ResNet
- VGG Net
- GoogleNet
- Implementing a CNN for object recognition
- Exploratory analysis
- Data scaling
- Using Keras in the CNN model
- Exploring the model's results
- Summary
- Movie Reviews Sentiment Analysis Using Recurrent Neural Networks
- Sentiment analysis basic concepts
- Sentiment analysis techniques
- The next challenges for sentiment analysis
- Lexicon and semantics analysis
- Recurrent neural networks
- Fully recurrent neural networks
- Recursive neural networks
- Hopfield recurrent neural networks
- Elman neural networks
- Long short-term memory network
- Classifying sentiment in movie reviews using an RNN
- IMDB Movie reviews dataset
- Exploratory analysis
- Keras recurrent neural network model
- Exploring model results
- Summary
- Stock Volatility Forecasting Using Long Short-Term Memory
- The basics of forecasting
- Forecast horizon
- Forecasting methods
- Quantitative methods
- Qualitative methods
- Time series analysis
- The classical approach to time series
- Estimation of the trend component
- Estimating the seasonality component
- Time series models
- Autoregressive models
- Moving average models
- Autoregressive moving average model
- Autoregressive integrated moving average models
- Long short-term memory in Keras
- Implementing an LSTM to forecast stock volatility
- Exploratory analysis
- Data scaling
- Data splitting
- Keras LSTM model
- Summary
- Reconstruction of Handwritten Digit Images Using Autoencoders
- Basic concepts of image recognition
- Image digitization
- Image recognition
- Optical character recognition
- Approaches to the problem
- Generative neural networks
- The restricted Boltzmann machine
- Autoencoders
- Variational autoencoders
- The generative adversarial network
- The adversarial autoencoder
- The Keras autoencoders model
- Implementing autoencoder Keras layers to reconstruct handwritten digit images
- The MNIST dataset
- Min–max normalization
- Keras model architecture
- Exploring model results
- Summary
- Robot Control System Using Deep Reinforcement Learning
- Robot control overview
- Three laws of robotics
- Short robotics timeline
- First-generation robots
- Second-generation robots
- Third-generation robots
- Fourth-generation robots
- Automatic control
- The environment for controlling robot mobility
- OpenAI Gym
- Reinforcement learning basics
- Agent-environment interface
- Reinforcement learning algorithms
- Dynamic Programming
- Monte Carlo methods
- Temporal difference learning
- Keras DQNs
- Q-learning
- Deep Q-learning
- Keras-RL library
- DQN to control a robot's mobility
- OpenAI Gym installation and methods
- The CartPole system
- Q-learning solution
- Deep Q-learning solution
- Summary
- Reuters Newswire Topics Classifier in Keras
- Natural language processing
- NLP phases
- Morphology analysis
- Syntax analysis
- Semantic analysis
- Pragmatic analysis
- Automatic processing problems
- NLP applications
- Information retrieval
- Information extraction
- Question-answering
- Automatic summarization
- Automatic translation
- Sentiment analysis
- NLP methods
- Sentence splitting
- Tokenization
- Part-of-speech tagging
- Shallow parsing
- Named entity recognition
- Syntactic parsing
- Semantic role labeling
- Natural language processing tools
- The Natural Language Toolkit
- The Stanford NLP Group software
- Apache OpenNLP
- GATE
- The Natural Language Toolkit
- Getting started with the NLTK
- Corpora
- Brown corpus
- Word and sentence tokenize
- Part-of-speech tagger
- Stemming and lemmatization
- Stemming
- Lemmatization
- Implementing a DNN to label sentences
- Exploratory analysis
- Data preparation
- Keras deep neural network model
- Summary
- What is Next?
- Deep learning methods
- Deep feedforward network
- Convolutional neural networks
- Recurrent neural networks
- Long short-term memory
- Restricted Boltzmann machine
- Deep belief network
- Generative adversarial networks
- Automated machine learning
- Auto-Keras
- Google Cloud ML Engine
- Azure Machine Learning Studio
- Amazon Web Services
- Differentiable neural computer
- Genetic programming and evolutionary strategies
- Introducing the genetic algorithm
- The fitness function
- Selection
- Mutation
- Inverse reinforcement learning
- Summary
- Other Books You May Enjoy
- Leave a review - let other readers know what you think 更新時間：2021-07-02 14:37:02
