Notice
Recent Posts
Recent Comments
Link
| 일 | 월 | 화 | 수 | 목 | 금 | 토 |
|---|---|---|---|---|---|---|
| 1 | 2 | 3 | 4 | 5 | 6 | 7 |
| 8 | 9 | 10 | 11 | 12 | 13 | 14 |
| 15 | 16 | 17 | 18 | 19 | 20 | 21 |
| 22 | 23 | 24 | 25 | 26 | 27 | 28 |
| 29 | 30 |
Tags
- Kubernetes
- Benchmarks
- yml
- slow query
- Debezium
- Database
- Leaderboard
- hammerDB
- eks
- Salting
- propogation
- keda
- Kafka
- traceId
- blue-green
- istio
- zset
- 0 replica
- minreplica
- logback
- spring boot
- Grafana
- virtualservice
- Strimzi
- 동등성
- MSSQL
- SW 마에스트로
- docket
- 스프링부트
- Helm
Archives
- Today
- Total
김태오
Automatic Differentiation 본문
A general way of automatically constructing a procedure for computer derivatives numerically
- a mathematical tool that allows us to compute the gradients of complex functions, including those with multiple inputs and outputs.
* this is not a function or a formula, it is a mathematical procedure.
Most of the times, it makes a computation graph.
a computation graph is a graphical representation of a mathematical computation, and is often used in machine learning to represent neural networks and enable automatic differentiation.
The two types of computations
'ML' 카테고리의 다른 글
| Polynomial Regression (0) | 2023.04.23 |
|---|---|
| Gradient Descent vs. Normal Equation (0) | 2023.04.23 |
| Learning Modes in ML (0) | 2023.04.22 |
| Linear Regression (0) | 2023.04.22 |
| Linear / Polynomial Regression Model (0) | 2023.04.05 |
'ML' Related Articles
more
