첫λ²μ§Έλ‘ ν΄λΉ μ©μ΄κ° μΆννμμ λλ 'νκΈ(μμ΄)'λ‘ λ²μνκ³ , μ΄νλΆν°λ νκΈλ‘λ§ λ²μν©λλ€. (μ. including transposing, indexing, ... => μ μΉ(transposing), μΈλ±μ±(indexing), ...)
- μλ μ©μ΄κ° μ μ νλ©΄ μλ νμ μ©μ΄λ₯Ό μ¬μ©ν©λλ€.
- μ§μ λ μ©μ΄κ° μλ€λ©΄ μλ μ¬μ΄νΈλ₯Ό μ°Έκ³ νμ¬ μ¬μ©ν©λλ€.
- http://www.ktword.co.kr/
- https://github.com/keunwoochoi/machine_learning_eng2kor/blob/master/dictionary.md
μμΌλ©΄ μ μ ν λ¨μ΄λ₯Ό μ¬μ©νκ³ , μλ λͺ©λ‘μ λ΄μ©μ μΆκ°ν©λλ€.
| μλ¬Έ | νκΈ | μμ±μ | μΆκ° μ€λͺ |
|---|---|---|---|
| Acknowledgements | κ°μ¬μ λ§ | λ°μ ν | |
| API endpoint | API μλν¬μΈνΈ | λ°μ ν | μμ°¨ νκΈ° |
| argument | μΈμ | λ°μ ν | |
| Audio | μ€λμ€ | λ°μ ν | ToCμ λΆλ₯λͺ μ λλ€. |
| augmentation | μ¦κ° | μ΄μ¬λ³΅ | |
| autograd | Autograd | ν©μ±μ | λ²μμν¨ |
| Batch Normalization | λ°°μΉ μ κ·ν | λ°μ ν | |
| bias | νΈν₯ | μ΄νλ | |
| convolution | ν©μ±κ³± | κΉνκΈΈ | |
| dataset | λ°μ΄ν°μ | λ°μ ν | μμ°¨ νκΈ° |
| deep neural network | μ¬μΈ΅ μ κ²½λ§ | λ°μ ν | |
| derivative | λν¨μ | λ°μ ν | |
| Drop-out | Drop-out | ν©μ±μ | λ²μμν¨ |
| epoch | μν | λ°μ ν | μμ°¨ νκΈ° |
| evaluation mode | νκ° λͺ¨λ | λ°μ ν | |
| feature | νΉμ§ | λ°±μ ν¬ | |
| feed data through model | λ°μ΄ν°λ₯Ό λͺ¨λΈμ μ 곡 | ||
| Feed-forward network | μμ ν μ κ²½λ§ | λ°μ ν | |
| Generative | μμ± λͺ¨λΈ | λ°μ ν | ToCμ λΆλ₯λͺ μ λλ€. |
| Getting Started tutorial | μμνκΈ° νν λ¦¬μΌ | λ°μ ν | ToCμ Getting Startedλ₯Ό λ»ν©λλ€. |
| gradient | λ³νλ | λ°μ ν | |
| Hyperparameter | νμ΄νΌνλΌλ―Έν° | κΉνμ | μμ°¨ νκΈ° |
| Image | μ΄λ―Έμ§ | λ°μ ν | ToCμ λΆλ₯λͺ μ λλ€. |
| in-place | μ μ리 | νλ¨κ· | |
| instance | μΈμ€ν΄μ€ | λ°μ ν | μμ°¨ νκΈ° |
| instantiate | μμ±νλ€ | λ°μ ν | |
| Layer | κ³μΈ΅ | λ°μ ν | |
| learning rate, lr | νμ΅λ₯ | λ°μ ν | |
| loss | μμ€ | λ°μ ν | |
| matrix | νλ ¬ | λ°μ ν | |
| mean-squared error | νκ· μ κ³±μ€μ°¨ | νλ¨κ· | |
| MelScale | MelScale | ||
| method | λ©μλ | μ₯ν¨μ | μμ°¨ νκΈ° |
| mini-batch | λ―Έλ λ°°μΉ | λ°μ ν | μμ°¨ νκΈ° |
| momentum | λͺ¨λ©ν | λ°μ ν | μμ°¨ νκΈ° |
| normalize | μ κ·ν | νλ¨κ· | |
| NumPy | NumPy | λ°μ ν | λ²μνμ§ μμ |
| One-Hot | One-Hot | ν©μ±μ | λ²μμν¨ |
| Optimizer | μ΅ν°λ§μ΄μ | λ°μ ν | μμ°¨ νκΈ° |
| output | μΆλ ₯ | λ°μ ν | |
| over-fitting | κ³Όμ ν© | ν©μ±μ | |
| parameter | λ§€κ°λ³μ | λ°μ ν | |
| placeholder | νλ μ΄μ€νλ | λ°μ ν | μμ°¨ νκΈ° |
| plotting | λμν | ν©μ±μ | |
| Production (environment, use) | Production | νλ¨κ· | λ²μνμ§ μμ |
| rank 0 | 0-μμ | λ°μ ν | |
| Read later | λ μ½μ거리 | λ°μ ν | |
| recap | μμ½ | λ°μ ν | |
| resample | 리μν | ||
| resizing | ν¬κΈ° λ³κ²½ | λ°μ ν | |
| sampling rate | μνλ§ λ μ΄νΈ | ||
| scenario | μλλ¦¬μ€ | λ°μ ν | μμ°¨ νκΈ° |
| shape | shape | νλ¨κ· | λ²μνμ§ μμ |
| size | ν¬κΈ° | λ°μ ν | |
| Tensor / Tensors | Tensor | λ°μ ν | λ²μνμ§ μμ |
| Text | ν μ€νΈ | λ°μ ν | ToCμ λΆλ₯λͺ μ λλ€. |
| track (computation) history | μ°μ° κΈ°λ‘μ μΆμ νλ€ | λ°μ ν | |
| training | νμ΅ | μ΄νλ | |
| warmstart | λΉ λ₯΄κ² μμνκΈ° | λ°μ ν | Warmstarting Model = λΉ λ₯΄κ² λͺ¨λΈ μμνκΈ° |
| weight | κ°μ€μΉ | λ°μ ν | |
| wrapper | λνΌ | λ°μ ν | μμ°¨ νκΈ° |