最新章節(jié)

• Summary
• NLP
• Computer vision
• Machine learning
• Mathematical background
• Recommended learning resources

• Summary 更新時間：2021-06-24 18:55:58
• NLP
• Computer vision
• Machine learning
• Mathematical background
• Recommended learning resources
• Uncanny valley effect
• Feedback loops
• Cargo cult
• Magical thinking
• Product design ogres
• Goblins of training
• Batch effects
• Biased data
• Tough data
• Data kobolds
• Machine learning gremlins
• Documentation
• Debugging and visualization
• Privacy and differential privacy
• Benchmarking
• Best practices
• Production
• Porting or deployment for a mobile platform
• Field testing
• Model training evaluation and selection
• Data preprocessing
• Prototype creation
• Make design choices
• Research the data
• Research the existing approaches
• Define the constraints
• Formulate the problem
• Preparatory stage
• Mobile machine learning project life cycle
• Best Practices
• Bibliography
• Summary
• An example of the network compression
• Tools
• Knowledge distillation
• Facebook's approach in Caffe2
• Other approaches
• Reducing precision
• Weights quantization
• Network pruning
• Optimizing for inference
• Lossy compression
• Preventing a neural network from growing big
• CondenseNet
• ShuffleNet
• MobileNets
• SqueezeNet
• Compact CNN architectures
• Lossless compression
• Calculating the size of a convolutional neural network
• Delivering perfect user experience
• Optimizing Neural Networks for Mobile Devices
• Summary
• Choosing a deep learning framework
• YCMatrix
• Upsurge
• NNPACK
• libLBFGS
• LibXtract
• Swix
• STEM
• SigmaSwiftStatistics
• IntuneFeatures
• fmincg-c
• Eigen
• Low-level subroutine libraries
• Tesseract
• OpenFace
• ccv
• OpenCV
• Computer vision
• OpenEars
• TLSphinx
• Speech recognition
• Twitter text
• Word2Vec
• NLP libraries
• XGBoost
• Scikit-learn
• LibSVM
• Keras
• Inference-only libraries
• YCML
• Torch
• tiny-dnn
• TensorFlow
• Shark
• MXNet
• Multilinear-math
• MLKit
• LearnKit
• FANN
• dlib
• Caffe2
• Caffe
• BrainCore
• AIToolbox
• General-purpose machine learning libraries
• Libraries
• Machine learning and AI APIs
• Machine Learning Libraries
• Summary
• Where to go from here?
• Word2Vec friends and relatives
• Putting it all together
• UIReferenceLibraryViewController
• Text-to-speech output
• Word2Vec on iOS
• NSLinguisticTagger and friends
• Voice input
• Chatbot anatomy
• iOS application
• Vector space properties
• Word2Vec in Gensim
• Word2Vec
• Autoencoder neural networks
• Word vector representations
• Distributional semantics hypothesis
• Removing stop words and punctuation
• Named entity recognition (NER)
• Part-of-speech (POS) tagging
• Lemmatization
• Stemming
• Tokenization
• Common NLP approaches and subtasks
• Textual corpuses
• Python NLP libraries
• Word Association game
• NLP in the mobile development world
• Natural Language Processing
• Bibliography
• Summary
• Deploying CNN to iOS
• Visualizing convolution filters
• Converting to Core ML format
• Saving the model in HDF5 format
• Making predictions
• Plotting loss
• Training the network
• Plotting the network structure
• Creating the network
• Data augmentation
• Splitting the data
• Loading the data
• Keras
• Deep learning frameworks
• Environment setup
• Training the CNN for facial expression recognition
• Training the network
• Loss functions
• Batch normalization
• Dropout
• Regularization layers
• Pooling layer
• Nonlinearity layers
• Fully-connected layers
• Convolutional layer
• Input layer
• Building the network
• Convolutions in CNNs
• Convolution operation
• Pooling operation
• Introducing convolutional neural networks
• Introducing computer vision problems
• Understanding users emotions
• Convolutional Neural Networks
• Summary
• BNNS example
• Basic neural network subroutines (BNNS)
• Seeing biological analogies
• Vanishing gradient problem
• Training the network
• Implementing layers in Swift
• Using neurons to build logical functions
• Building a neural layer in Swift
• Building the network
• Rectifier-like activation functions
• Step-like activation functions
• Non-linearity function
• Building the neuron
• What are artificial NNs anyway?
• Neural Networks
• Summary
• Bias-variance trade-off
• Choosing the regression model for your problem
• Handling dates
• Predicting user intents
• Cost function
• Training the logistic regression
• The prediction part of logistic regression
• Implementing logistic regression in Swift
• Logistic regression
• Linear classifier
• Revisiting the classification task
• Linear Classifier and Logistic Regression
• Bibliography
• Summary
• ElasticNet regression
• LASSO regression
• Ridge regression and Tikhonov regularization
• Fixing linear regression problems with regularization
• Understanding and overcoming the limitations of linear regression
• Normal equation for multiple linear regression
• Feature-wise standardization
• Linear algebra operations
• Training multiple regression
• Gradient descent for multiple linear regression
• Implementing multiple linear regression in Swift
• Multiple linear regression
• Feature standardization
• Feature scaling
• Forecasting the future with simple linear regression
• Using gradient descent for function minimization
• Where to use GD and normal equation
• Fitting a regression line using the least squares method
• Introducing simple linear regression
• Understanding the regression task
• Linear Regression and Gradient Descent
• Bibliography
• Summary
• Building an adaptable user experience
• The pros and cons of Apriori
• Running Apriori on real-world data
• Running Apriori
• Implementing Apriori in Swift
• The Apriori algorithm
• Finding frequent item sets
• Generating all possible rules
• Decomposing the problem
• Conviction association measures
• Lift association measures
• Confidence association measures
• Supporting association measures
• Using association measures to assess rules
• Defining data structures
• Seeing association rules
• Association Rule Learning
• Summary
• Image segmentation using k-means
• K-means++
• K-means clustering – problems
• Choosing the number of clusters
• Clustering objects on a map
• Assignment step
• Update step
• Implementing k-means in Swift
• K-means clustering
• Unsupervised learning
• K-Means Clustering
• Bibliography
• Summary
• Utilizing state transitions
• Trees to speed up the inference
• Hardware acceleration
• Smarter time series chunking
• More data sources
• Probabilistic interpretation
• Improving our solution
• KNN cons
• KNN pros
• Reasoning in high-dimensional spaces
• Choosing a good k
• Balanced dataset
• Cold start problem
• Recognizing human motion using KNN
• Implementing KNN in Swift
• Understanding the KNN algorithm
• People motion recognition using inertial sensors
• Using instance-based models for classification and clustering
• Implementing DTW in Swift
• DTW
• Calculating the distance
• K-Nearest Neighbors Classifier
• Summary
• Decision tree learning pros and cons
• Calculating the confusion matrix
• Evaluating performance of the model on iOS
• Importing the Core ML model into an iOS project
• Random forest accuracy evaluation
• Training the random forest
• Ensemble learning random forest
• Exporting the model for iOS
• Core ML features
• Introducing Core ML
• Implementing first machine learning app in Swift
• Confusion matrix
• K-fold cross-validation
• Precision recall and F1-score
• Evaluating performance of the model with data
• Combinatorial entropy
• Building a tree automatically from data
• How decision tree learning works
• Understanding model capacity trade-offs
• Tuning hyperparameters
• Evaluating accuracy
• Making predictions
• Tree visualization
• Training the decision tree classifier
• Decision trees everywhere
• Splitting the data
• One-hot encoding
• Separating features from labels
• Converting categorical variables
• Data preprocessing
• Exploratory data analysis
• Loading the dataset
• Machine learning for extra-terrestrial life explorers
• Time to practice
• IPython notebook crash course
• Setting up a machine learning environment
• Tools
• Prototyping the first machine learning app
• Machine learning toolbox
• Classification – Decision Tree Learning
• Bibliography
• Summary
• Understanding mobile platform limitations
• Mobile versus server-side ML
• Mathematical optimization – how learning works
• Reinforcement learning
• Unsupervised learning
• Supervised learning
• Types of ML algorithms
• Choosing a model
• Data preprocessing
• Getting the dataset
• Choosing a good set of features
• Types of features
• Features
• Getting to know your data
• Using ML to build smarter iOS applications
• Other applications of ML
• Natural language processing (NLP)
• Computer vision
• Digital signal processing (DSP)
• Applications of ML
• What is ML ?
• The motivation behind ML
• What is AI?
• Getting Started with Machine 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 reviewers
• About the author
• Contributors
• PacktPub.com
• Why subscribe?
• Packt Upsell
• Title Page
• coverpage

• coverpage
• Title Page
• Packt Upsell
• Why subscribe?
• PacktPub.com
• Contributors
• About the author
• 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
• Getting Started with Machine Learning
• What is AI?
• The motivation behind ML
• What is ML ?
• Applications of ML
• Digital signal processing (DSP)
• Computer vision
• Natural language processing (NLP)
• Other applications of ML
• Using ML to build smarter iOS applications
• Getting to know your data
• Features
• Types of features
• Choosing a good set of features
• Getting the dataset
• Data preprocessing
• Choosing a model
• Types of ML algorithms
• Supervised learning
• Unsupervised learning
• Reinforcement learning
• Mathematical optimization – how learning works
• Mobile versus server-side ML
• Understanding mobile platform limitations
• Summary
• Bibliography
• Classification – Decision Tree Learning
• Machine learning toolbox
• Prototyping the first machine learning app
• Tools
• Setting up a machine learning environment
• IPython notebook crash course
• Time to practice
• Machine learning for extra-terrestrial life explorers
• Loading the dataset
• Exploratory data analysis
• Data preprocessing
• Converting categorical variables
• Separating features from labels
• One-hot encoding
• Splitting the data
• Decision trees everywhere
• Training the decision tree classifier
• Tree visualization
• Making predictions
• Evaluating accuracy
• Tuning hyperparameters
• Understanding model capacity trade-offs
• How decision tree learning works
• Building a tree automatically from data
• Combinatorial entropy
• Evaluating performance of the model with data
• Precision recall and F1-score
• K-fold cross-validation
• Confusion matrix
• Implementing first machine learning app in Swift
• Introducing Core ML
• Core ML features
• Exporting the model for iOS
• Ensemble learning random forest
• Training the random forest
• Random forest accuracy evaluation
• Importing the Core ML model into an iOS project
• Evaluating performance of the model on iOS
• Calculating the confusion matrix
• Decision tree learning pros and cons
• Summary
• K-Nearest Neighbors Classifier
• Calculating the distance
• DTW
• Implementing DTW in Swift
• Using instance-based models for classification and clustering
• People motion recognition using inertial sensors
• Understanding the KNN algorithm
• Implementing KNN in Swift
• Recognizing human motion using KNN
• Cold start problem
• Balanced dataset
• Choosing a good k
• Reasoning in high-dimensional spaces
• KNN pros
• KNN cons
• Improving our solution
• Probabilistic interpretation
• More data sources
• Smarter time series chunking
• Hardware acceleration
• Trees to speed up the inference
• Utilizing state transitions
• Summary
• Bibliography
• K-Means Clustering
• Unsupervised learning
• K-means clustering
• Implementing k-means in Swift
• Update step
• Assignment step
• Clustering objects on a map
• Choosing the number of clusters
• K-means clustering – problems
• K-means++
• Image segmentation using k-means
• Summary
• Association Rule Learning
• Seeing association rules
• Defining data structures
• Using association measures to assess rules
• Supporting association measures
• Confidence association measures
• Lift association measures
• Conviction association measures
• Decomposing the problem
• Generating all possible rules
• Finding frequent item sets
• The Apriori algorithm
• Implementing Apriori in Swift
• Running Apriori
• Running Apriori on real-world data
• The pros and cons of Apriori
• Building an adaptable user experience
• Summary
• Bibliography
• Linear Regression and Gradient Descent
• Understanding the regression task
• Introducing simple linear regression
• Fitting a regression line using the least squares method
• Where to use GD and normal equation
• Using gradient descent for function minimization
• Forecasting the future with simple linear regression
• Feature scaling
• Feature standardization
• Multiple linear regression
• Implementing multiple linear regression in Swift
• Gradient descent for multiple linear regression
• Training multiple regression
• Linear algebra operations
• Feature-wise standardization
• Normal equation for multiple linear regression
• Understanding and overcoming the limitations of linear regression
• Fixing linear regression problems with regularization
• Ridge regression and Tikhonov regularization
• LASSO regression
• ElasticNet regression
• Summary
• Bibliography
• Linear Classifier and Logistic Regression
• Revisiting the classification task
• Linear classifier
• Logistic regression
• Implementing logistic regression in Swift
• The prediction part of logistic regression
• Training the logistic regression
• Cost function
• Predicting user intents
• Handling dates
• Choosing the regression model for your problem
• Bias-variance trade-off
• Summary
• Neural Networks
• What are artificial NNs anyway?
• Building the neuron
• Non-linearity function
• Step-like activation functions
• Rectifier-like activation functions
• Building the network
• Building a neural layer in Swift
• Using neurons to build logical functions
• Implementing layers in Swift
• Training the network
• Vanishing gradient problem
• Seeing biological analogies
• Basic neural network subroutines (BNNS)
• BNNS example
• Summary
• Convolutional Neural Networks
• Understanding users emotions
• Introducing computer vision problems
• Introducing convolutional neural networks
• Pooling operation
• Convolution operation
• Convolutions in CNNs
• Building the network
• Input layer
• Convolutional layer
• Fully-connected layers
• Nonlinearity layers
• Pooling layer
• Regularization layers
• Dropout
• Batch normalization
• Loss functions
• Training the network
• Training the CNN for facial expression recognition
• Environment setup
• Deep learning frameworks
• Keras
• Loading the data
• Splitting the data
• Data augmentation
• Creating the network
• Plotting the network structure
• Training the network
• Plotting loss
• Making predictions
• Saving the model in HDF5 format
• Converting to Core ML format
• Visualizing convolution filters
• Deploying CNN to iOS
• Summary
• Bibliography
• Natural Language Processing
• NLP in the mobile development world
• Word Association game
• Python NLP libraries
• Textual corpuses
• Common NLP approaches and subtasks
• Tokenization
• Stemming
• Lemmatization
• Part-of-speech (POS) tagging
• Named entity recognition (NER)
• Removing stop words and punctuation
• Distributional semantics hypothesis
• Word vector representations
• Autoencoder neural networks
• Word2Vec
• Word2Vec in Gensim
• Vector space properties
• iOS application
• Chatbot anatomy
• Voice input
• NSLinguisticTagger and friends
• Word2Vec on iOS
• Text-to-speech output
• UIReferenceLibraryViewController
• Putting it all together
• Word2Vec friends and relatives
• Where to go from here?
• Summary
• Machine Learning Libraries
• Machine learning and AI APIs
• Libraries
• General-purpose machine learning libraries
• AIToolbox
• BrainCore
• Caffe
• Caffe2
• dlib
• FANN
• LearnKit
• MLKit
• Multilinear-math
• MXNet
• Shark
• TensorFlow
• tiny-dnn
• Torch
• YCML
• Inference-only libraries
• Keras
• LibSVM
• Scikit-learn
• XGBoost
• NLP libraries
• Word2Vec
• Twitter text
• Speech recognition
• TLSphinx
• OpenEars
• Computer vision
• OpenCV
• ccv
• OpenFace
• Tesseract
• Low-level subroutine libraries
• Eigen
• fmincg-c
• IntuneFeatures
• SigmaSwiftStatistics
• STEM
• Swix
• LibXtract
• libLBFGS
• NNPACK
• Upsurge
• YCMatrix
• Choosing a deep learning framework
• Summary
• Optimizing Neural Networks for Mobile Devices
• Delivering perfect user experience
• Calculating the size of a convolutional neural network
• Lossless compression
• Compact CNN architectures
• SqueezeNet
• MobileNets
• ShuffleNet
• CondenseNet
• Preventing a neural network from growing big
• Lossy compression
• Optimizing for inference
• Network pruning
• Weights quantization
• Reducing precision
• Other approaches
• Facebook's approach in Caffe2
• Knowledge distillation
• Tools
• An example of the network compression
• Summary
• Bibliography
• Best Practices
• Mobile machine learning project life cycle
• Preparatory stage
• Formulate the problem
• Define the constraints
• Research the existing approaches
• Research the data
• Make design choices
• Prototype creation
• Data preprocessing
• Model training evaluation and selection
• Field testing
• Porting or deployment for a mobile platform
• Production
• Best practices
• Benchmarking
• Privacy and differential privacy
• Debugging and visualization
• Documentation
• Machine learning gremlins
• Data kobolds
• Tough data
• Biased data
• Batch effects
• Goblins of training
• Product design ogres
• Magical thinking
• Cargo cult
• Feedback loops
• Uncanny valley effect
• Recommended learning resources
• Mathematical background
• Machine learning
• Computer vision
• NLP
• Summary 更新時間：2021-06-24 18:55:58