WEB SERVICE
- Service delivered over the web?
What aout interoperability?
Will a .Net application be able to call the services in the JAR?
WEB SERVICE -W3C DEFINITION
Software system designed to support interoperable machine-to-machine interaction over a network.
3KEYS
- Designed for machine-to-machine(or application-to-application) interaction
- Should b interoperable- Not platform dependent
- Should allow communication over a network
2018년 10월 26일 금요일
Data Science: Logistic Regression in Python
Outline
- What is classification (in the broader context of all ML)? Review
- Linear classifiers in general
- Biological inspiration - neurons and perceptron
- Logistic regression schematic
- Feed-forward mechanism, probabilistic interpretation
- Cross-entropy error function
- Maximum-likelihood
- Gradient descent
- Practical problems:regularizaion, donut problem, XOR problem
- What is classification (in the broader context of all ML)? Review
- Linear classifiers in general
- Biological inspiration - neurons and perceptron
- Logistic regression schematic
- Feed-forward mechanism, probabilistic interpretation
- Cross-entropy error function
- Maximum-likelihood
- Gradient descent
- Practical problems:regularizaion, donut problem, XOR problem
Deep Learning in Python
##Chapter 1: What is a neural network?
Specifically, neurons are the perfect model for a yes / no, true / false, 0 / 1 type of problem. We call this "binary classification" and th machine learnng analogy would be the "logistic regression" algorithm.
The above image is pictorial representation of the logistic regression model. It takes as inmput x1, x2, and x3 which you can imagine as the outputs of other neurons or some other input signal (i.e. the visual receptors in your eyes or the mechanical receptors in your fingertips), and outputs another signal which is a combination of these inputs, weighted by the strength of those input neurons to this output neuron.
Becaise we're going to have to eventually deal with actual numbers and formulas, let's look at how we can calculate y from x.
y = sigmoid(w1*x1 + w2*x2 + w3*x3)
Specifically, neurons are the perfect model for a yes / no, true / false, 0 / 1 type of problem. We call this "binary classification" and th machine learnng analogy would be the "logistic regression" algorithm.
The above image is pictorial representation of the logistic regression model. It takes as inmput x1, x2, and x3 which you can imagine as the outputs of other neurons or some other input signal (i.e. the visual receptors in your eyes or the mechanical receptors in your fingertips), and outputs another signal which is a combination of these inputs, weighted by the strength of those input neurons to this output neuron.
Becaise we're going to have to eventually deal with actual numbers and formulas, let's look at how we can calculate y from x.
y = sigmoid(w1*x1 + w2*x2 + w3*x3)
피드 구독하기:
글 (Atom)