- 전체보기(14)
- 머신러닝(9)
- python(9)
- machine learning(9)
- 파이썬(8)
- MachineLearning(5)
- spark(3)
- Natural language processing(1)
- eigen value(1)
- 빅데이터(1)
- SVM(1)
- NLP(1)
- Support Vector Machine(1)
- 딥러닝(1)
- 군집화(1)
- 빅 데이터(1)
- KNN(1)
- HDFS(1)
- ML(1)
- hadoop(1)
- 인공신경망(1)
- ec2(1)
- 자연어처리(1)
- big data(1)
- Classification(1)
- Data Science(1)
- 데이터사이언스(1)
- 싸이킷(1)
- data(1)
- aws(1)
- pyspark(1)
- scikit(1)
- 분산처리(1)
- bigdata(1)
- Neural Network(1)
- learn(1)
- Deep Learning(1)
- server(1)
Basics of Neural Network - Perceptron Model
Dendrites : inputs going into nucleus Nucleus : does the calculationAxon : passes output to other neurons Neural Networks mimic this model Generalized
Spark Basic Operations
Basic unit of calculation for Spark (It's like an API for controling Spark)a read-only, fault-tolerant partitioned collection of recordsLineage: User
Natural Language Processing
Group news articles by topic compile documents featurize documents compare the features Bag of words : Document represented as vectors \- “Blue House
PCA (Principal Component Analysis)
reduce the number of variables of a data set while preserving as much information as possiblea dimensionality-reduction method which transforms a larg
K Means Clustering
an unsupervised learning algorithm that will attempt to group similar clusters together in the data.Example problems \- Cluster Customers based on Fe
Decision Trees and Random Forests
Nodes \- Split for the value of a certain attributeEdges \- Outcome of a split to next nodeRoot \- The node that performs the first splitLeaves \-
K Nearest Neighbors
Training Algorithm : \- Store all the dataPrediction Algorithm : \- Calculate the distance from x to all the points \- Sort the points in the data b
Bias Variance Trade-off
The bias Variance trade-off is the point where we are just adding noise by adding model complexitytraining error ↓ test error ↑After bias trade-off, t
Using Sckit-Learn
conda install scikit-learnevery algorithm is exposed in scikit-learn via an 'Estimator' First you'll import the model, the general form is : \- from