TAG CLOUD
- 3d
- API12F
- DCA
- DPR
- DUC
- DrillingInfo
- EIA
- ESP
- Enverus
- Gaussian
- H2S
- Huber regression
- L1 norm
- L2 norm
- L2 norm squared
- OLS
- P&ID
- PID loop
- ProMax
- RANSAC regression
- RVP
- SRP
- TheilSen regression
- VRU
- basins
- blowcase
- cashflow
- casing drawdown
- central tendency
- co-occurrence matrix
- completion
- confidence interval
- data-mining
- decline-curve-analysis
- dew-point
- downsizing
- drilling
- economics
- electric motor
- electric valve
- emissions
- energy-shortage
- flash
- gas
- gas engine
- gas-sample
- gear ratio
- heater
- heaters
- horizontals
- hypotheses testing
- learning rate
- level control valve
- level switch
- linear regression
- lube oil
- machine learning
- measurements
- negative sampling
- neural network
- nlp
- non-Gaussian
- non-normal
- non-parametric
- normal
- normality test
- oil
- oil-and-gas
- outliers
- pneumatic valve
- pressure control
- process engineering
- production
- recent-trend
- recycle
- robust linear regression
- screw compressor
- separator
- separator drawdown
- separators
- shutdown
- simulations
- skip-gram
- solenoid valve
- spatial median
- statistics
- stochastic gradient descent
- storage-tank
- suction control valve
- tank
- temperature control
- thermostat valve
- transmitters
- vapor recovery
- vector space model
- verticals
- visualization
- window size
- word vectors
- word2vec
Optimize Computational Efficiency of Skip-Gram with Negative Sampling
When training your NLP model with Skip-Gram, the very large size of vocabs imposes high computational cost on your machine. Since the original Skip-Gram model is unable to handle this high cost, we use an alternative, called Negative Sampling.
Demystifying Neural Network in Skip-Gram Language Modeling
The past couple of years, neural networks in Word2Vec have nearly taken over the field of NLP, thanks to their state-of-art performance. But how much do you understand about the algorithm behind it? This post will crack the secrets behind neural net in Word2Vec.
Understanding Multi-Dimensionality in Vector Space Modeling
How does word vectors in Natural Language Processing capture meaningful relationships among words? How can you quantify those relationships? Addressing these questions starts from understanding the multi-dimensional nature of NLP applications.