Index
A
B
C
D
E
F
G
H
I
J
K
L
M
N
O
P
Q
- quick-start guide, Anaconda
R
S
- salient (relevant) features / Understanding CNNs and learning feature hierarchies
- samples
- savez function
- scatter matrices
- scikit-learn
- scikit-learn-compatible implementations, of stacking
- scikit-learn estimator API / Understanding the scikit-learn estimator API
- scoring metrics
- seaborn library
- sentiment analysis
- sentiment analysis of IMDb movie reviews, with multilayer RNNs
- sentiment analysis RNN model
- SentimentRNN class
- SentimentRNN class constructor / The SentimentRNN class constructor
- sepal width / Using the majority voting principle to make predictions
- sepal width feature axis / Decision tree learning
- sequence modeling
- sequences
- Sequential Backward Selection (SBS) / Sequential feature selection algorithms
- sequential data
- sequential feature selection algorithms
- shape
- sigmoid function / Logistic regression intuition and conditional probabilities
- signal / Performing a discrete convolution in one dimension
- silhouette analysis / Quantifying the quality of clustering via silhouette plots
- silhouette coefficient / Quantifying the quality of clustering via silhouette plots
- silhouette plots / K-means clustering using scikit-learn
- similarity function / Using the kernel trick to find separating hyperplanes in high-dimensional space
- simple linear regression
- simple majority vote classifier
- simple model
- single-layer neural network / Single-layer neural network recap
- Single Instruction, Multiple Data (SIMD) / An object-oriented perceptron API
- single linkage / Grouping clusters in bottom-up fashion
- slack variables
- Snowball stemmer / Processing documents into tokens
- soft-margin classification / Dealing with a nonlinearly separable case using slack variables
- soft clustering
- soft k-means / Hard versus soft clustering
- softmax function
- sparse-connectivity / Working with multiple input or color channels
- sparse solutions
- spectral clustering / Locating regions of high density via DBSCAN
- SQLite
- sqlite3
- SQLite database
- SQLite Manager
- squared Euclidean distance / K-means clustering using scikit-learn
- stacking / Evaluating and tuning the ensemble classifier
- standardization / Improving gradient descent through feature scaling, Bringing features onto the same scale
- stochastic gradient descent / Large-scale machine learning and stochastic gradient descent, Working with bigger data – online algorithms and out-of-core learning
- stochastic gradient descent (SGD) / Solving regression for regression parameters with gradient descent
- stochastic gradient descent optimization / Single-layer neural network recap
- stop-word removal / Processing documents into tokens
- subgroups
- subsampling / Subsampling
- sum of squared errors (SSE) / Solving regression for regression parameters with gradient descent, K-means clustering using scikit-learn
- Sum of Squared Errors (SSE) / Minimizing cost functions with gradient descent, A geometric interpretation of L2 regularization, Single-layer neural network recap
- supervised data compression
- supervised learning
- Support Vector Machine (SVM) / Tuning hyperparameters via grid search, Random forest regression
- support vector machine (SVM) / Maximum margin classification with support vector machines, Using kernel principal component analysis for nonlinear mappings
- support vector machines
- support vectors / Maximum margin classification with support vector machines
- SVM regressor
- Synthetic Minority Over-sampling Technique (SMOTE) / Dealing with class imbalance
T
U
V
W
X
Z
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