| Карточка | Таблица | RUSMARC | |
Права на использование объекта хранения
| Место доступа | Группа пользователей | Действие | ||||
|---|---|---|---|---|---|---|
| Локальная сеть Финуниверситета | Все |
|
||||
| Интернет | Читатели |
|
||||
|
Интернет | Анонимные пользователи |
Оглавление
- Title Page
- Copyright and Credits
- Packt Upsell
- Contributors
- Table of Contents
- Preface
- Chapter 1: 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
- Features
- 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
- Chapter 2: 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
- Chapter 3: 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
- Calculating the distance
- Chapter 4: 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
- Chapter 5: 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
- Chapter 6: 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
- Fitting a regression line using the least squares method
- 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
- Gradient descent for multiple linear regression
- Fixing linear regression problems with regularization
- Ridge regression and Tikhonov regularization
- LASSO regression
- ElasticNet regression
- Ridge regression and Tikhonov regularization
- Summary
- Bibliography
- Chapter 7: 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
- Revisiting the classification task
- Chapter 8: Neural Networks
- What are artificial NNs anyway?
- Building the neuron
- Non-linearity function
- Step-like activation functions
- Rectifier-like activation functions
- Non-linearity function
- 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
- Chapter 9: 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
- Chapter 10: 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
- Chapter 11: 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
- Chapter 12: 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
- Optimizing for inference
- An example of the network compression
- Summary
- Bibliography
- Chapter 13: 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
- Preparatory stage
- 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
- Data kobolds
- Recommended learning resources
- Mathematical background
- Machine learning
- Computer vision
- NLP
- Mathematical background
- Summary
- Mobile machine learning project life cycle
- Index
Статистика использования
|
|
Количество обращений: 0
За последние 30 дней: 0 Подробная статистика |