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TensorFlow Machine Learning Projects
TensorFlowhastransformedthewaymachinelearningisperceived.TensorFlowMachineLearningProjectsteachesyouhowtoexploitthebenefits—simplicity,efficiency,andflexibility—ofusingTensorFlowinvariousreal-worldprojects.Withthehelpofthisbook,you’llnotonlylearnhowtobuildadvancedprojectsusingdifferentdatasetsbutalsobeabletotacklecommonchallengesusingarangeoflibrariesfromtheTensorFlowecosystem.Tostartwith,you’llgettogripswithusingTensorFlowformachinelearningprojects;you’llexploreawiderangeofprojectsusingTensorForestandTensorBoardfordetectingexoplanets,TensorFlow.jsforsentimentanalysis,andTensorFlowLitefordigitclassification.Asyoumakeyourwaythroughthebook,you’llbuildprojectsinvariousreal-worlddomains,incorporatingnaturallanguageprocessing(NLP),theGaussianprocess,autoencoders,recommendersystems,andBayesianneuralnetworks,alongwithtrendingareassuchasGenerativeAdversarialNetworks(GANs),capsulenetworks,andreinforcementlearning.You’lllearnhowtousetheTensorFlowonSparkAPIandGPU-acceleratedcomputingwithTensorFlowtodetectobjects,followedbyhowtotrainanddeveloparecurrentneuralnetwork(RNN)modeltogeneratebookscripts.Bytheendofthisbook,you’llhavegainedtherequiredexpertisetobuildfull-fledgedmachinelearningprojectsatwork.
目錄(203章)
倒序
- coverpage
- Title Page
- Dedication
- About Packt
- Why subscribe?
- Packt.com
- Contributors
- About the authors
- About the reviewers
- 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
- Overview of TensorFlow and Machine Learning
- What is TensorFlow?
- The TensorFlow core
- Tensors
- Constants
- Operations
- Placeholders
- Tensors from Python objects
- Variables
- Tensors generated from library functions
- Obtaining variables with the tf.get_variable()
- Computation graph
- The order of execution and lazy loading
- Executing graphs across compute devices – CPU and GPGPU
- Placing graph nodes on specific compute devices
- Simple placement
- Dynamic placement
- Soft placement
- GPU memory handling
- Multiple graphs
- Machine learning classification and logistic regression
- Machine learning
- Classification
- Logistic regression for binary classification
- Logistic regression for multiclass classification
- Logistic regression with TensorFlow
- Logistic regression with Keras
- Summary
- Questions
- Further reading
- Using Machine Learning to Detect Exoplanets in Outer Space
- What is a decision tree?
- Why do we need ensembles?
- Decision tree-based ensemble methods
- Random forests
- Gradient boosting
- Decision tree-based ensembles in TensorFlow
- TensorForest Estimator
- TensorFlow boosted trees estimator
- Detecting exoplanets in outer space
- Building a TFBT model for exoplanet detection
- Summary
- Questions
- Further reading
- Sentiment Analysis in Your Browser Using TensorFlow.js
- Understanding TensorFlow.js
- Understanding Adam Optimization
- Understanding categorical cross entropy loss
- Understanding word embeddings
- Building the sentiment analysis model
- Pre-processing data
- Building the model
- Running the model on a browser using TensorFlow.js
- Summary
- Questions
- Digit Classification Using TensorFlow Lite
- What is TensorFlow Lite?
- Classification Model Evaluation Metrics
- Classifying digits using TensorFlow Lite
- Pre-processing data and defining the model
- Converting TensorFlow model to TensorFlow Lite
- Summary
- Questions
- Speech to Text and Topic Extraction Using NLP
- Speech-to-text frameworks and toolkits
- Google Speech Commands Dataset
- Neural network architecture
- Feature extraction module
- Deep neural network module
- Training the model
- Summary
- Questions
- Further reading
- Predicting Stock Prices using Gaussian Process Regression
- Understanding Bayes' rule
- Introducing Bayesian inference
- Introducing Gaussian processes
- Choosing kernels in GPs
- Choosing the hyper parameters of a kernel
- Applying GPs to stock market prediction
- Creating a stock price prediction model
- Understanding the results obtained
- Summary
- Questions
- Credit Card Fraud Detection using Autoencoders
- Understanding auto-encoders
- Building a fraud detection model
- Defining and training a fraud detection model
- Testing a fraud detection model
- Summary
- Questions
- Generating Uncertainty in Traffic Signs Classifier Using Bayesian Neural Networks
- Understanding Bayesian deep learning
- Bayes' rule in neural networks
- Understanding TensorFlow probability variational inference and Monte Carlo methods
- Building a Bayesian neural network
- Defining training and testing the model
- Summary
- Questions
- Generating Matching Shoe Bags from Shoe Images Using DiscoGANs
- Understanding generative models
- Training GANs
- Applications
- Challenges
- Understanding DiscoGANs
- Fundamental units of a DiscoGAN
- DiscoGAN modeling
- Building a DiscoGAN model
- Summary
- Questions
- Classifying Clothing Images using Capsule Networks
- Understanding the importance of capsule networks
- Understanding capsules
- How do capsules work?
- The dynamic routing algorithm
- CapsNet for classifying Fashion MNIST images
- CapsNet implementation
- Understanding the encoder
- Understanding the decoder
- Defining the loss function
- Training and testing the model
- Reconstructing sample images
- Limitations of capsule networks
- Summary
- Making Quality Product Recommendations Using TensorFlow
- Recommendation systems
- Content-based filtering
- Advantages of content-based filtering algorithms
- Disadvantages of content-based filtering algorithms
- Collaborative filtering
- Hybrid systems
- Matrix factorization
- Introducing the Retailrocket dataset
- Exploring the Retailrocket dataset
- Pre-processing the data
- The matrix factorization model for Retailrocket recommendations
- The neural network model for Retailrocket recommendations
- Summary
- Questions
- Further reading
- Object Detection at a Large Scale with TensorFlow
- Introducing Apache Spark
- Understanding distributed TensorFlow
- Deep learning through distributed TensorFlow
- Learning about TensorFlowOnSpark
- Understanding the architecture of TensorFlowOnSpark
- Deep delving inside the TFoS API
- Handwritten digits using TFoS
- Object detection using TensorFlowOnSpark and Sparkdl
- Transfer learning
- Understanding the Sparkdl interface
- Building an object detection model
- Summary
- Generating Book Scripts Using LSTMs
- Understanding recurrent neural networks
- Pre-processing the data
- Defining the model
- Training the model
- Defining and training a text-generating model
- Generating book scripts
- Summary
- Questions
- Playing Pacman Using Deep Reinforcement Learning
- Reinforcement learning
- Reinforcement learning versus supervised and unsupervised learning
- Components of Reinforcement Learning
- OpenAI Gym
- Creating a Pacman game in OpenAI Gym
- DQN for deep reinforcement learning
- Applying DQN to a game
- Summary
- Further Reading
- What is Next?
- Implementing TensorFlow in production
- Understanding TensorFlow Hub
- TensorFlow Serving
- TensorFlow Extended
- Recommendations for building AI applications
- Limitations of deep learning
- AI applications in industries
- Ethical considerations in AI
- Summary
- Other Books You May Enjoy
- Leave a review - let other readers know what you think 更新時間：2021-06-10 19:16:06
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