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Recurrent Neural Networks with Python Quick Start Guide
Developersstruggletofindaneasy-to-followlearningresourceforimplementingRecurrentNeuralNetwork(RNN)models.RNNsarethestate-of-the-artmodelindeeplearningfordealingwithsequentialdata.Fromlanguagetranslationtogeneratingcaptionsforanimage,RNNsareusedtocontinuouslyimproveresults.ThisbookwillteachyouthefundamentalsofRNNs,withexampleapplicationsinPythonandtheTensorFlowlibrary.Theexamplesareaccompaniedbytherightcombinationoftheoreticalknowledgeandreal-worldimplementationsofconceptstobuildasolidfoundationofneuralnetworkmodeling.YourjourneystartswiththesimplestRNNmodel,whereyoucangraspthefundamentals.Thebookthenbuildsonthisbyproposingmoreadvancedandcomplexalgorithms.Weusethemtoexplainhowatypicalstate-of-the-artRNNmodelworks.Fromgeneratingtexttobuildingalanguagetranslator,weshowhowsomeoftoday'smostpowerfulAIapplicationsworkunderthehood.Afterreadingthebook,youwillbeconfidentwiththefundamentalsofRNNs,andbereadytopursuefurtherstudy,alongwithdevelopingskillsinthisexcitingfield.
目錄(97章)
倒序
- coverpage
- Title Page
- 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
- Introducing Recurrent Neural Networks
- What is an RNN?
- Comparing recurrent neural networks with similar models
- Hidden Markov model
- Recurrent neural network
- Understanding how recurrent neural networks work
- Basic neural network overview
- Obtaining data
- Encoding the data
- Building the architecture
- Training the model
- Evaluating the model
- Key problems with the standard recurrent neural network model
- Summary
- External links
- Building Your First RNN with TensorFlow
- What are you going to build?
- Introduction to TensorFlow
- Graph-based execution
- Eager execution
- Coding the recurrent neural network
- Generating data
- Building the TensorFlow graph
- Training the RNN
- Evaluating the predictions
- Summary
- External links
- Generating Your Own Book Chapter
- Why use the GRU network?
- Generating your book chapter
- Obtaining the book text
- Encoding the text
- Building the TensorFlow graph
- Training the network
- Generating your new text
- Summary
- External links
- Creating a Spanish-to-English Translator
- Understanding the translation model
- What is an LSTM network?
- Understanding the sequence-to-sequence network with attention
- Building the Spanish-to-English translator
- Preparing the data
- Constructing the TensorFlow graph
- Training the model
- Predicting the translation
- Evaluating the final results
- Summary
- External links
- Building Your Personal Assistant
- What are we building?
- Preparing the data
- Creating the chatbot network
- Training the chatbot
- Building a conversation
- Summary
- External links
- Improving Your RNN Performance
- Improving your RNN model
- Improving performance with data
- Selecting data
- Processing data
- Transforming data
- Improving performance with tuning
- Grid search
- Random search
- Hand-tuning
- Bayesian optimization
- Tree-structured Parzen Estimators (TPE)
- Optimizing the TensorFlow library
- Data processing
- Improving data loading
- Improving data transformation
- Performing the training
- Optimizing gradients
- Summary
- External links
- Other Books You May Enjoy
- Leave a review - let other readers know what you think 更新時間：2021-06-10 18:50:53
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