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NN03-320
DUO MXTEND MULTIBAND ANTENNA
- 제조업체
- Mfr.부분 #NN03-320
- 패키지
- 데이터시트
- 재고 있음2395
100% 원래 &새로운
배송 준비 24 시간
365 일 보장
RFQ 및더 많은 할인
사양
| Supplier | Ignion |
| Package | Tape & Reel (TR),Cut Tape (CT) |
| Series | DUO mXTEND™ |
| ProductStatus | Active |
| RFFamily/Standard | Navigation |
| FrequencyGroup | Ultra Wide Band |
| Frequency(Center/Band) | 1.561GHz, 1.575GHz, 1.606GHz, 2.45GHz, 3.5GHz, 3.7GHz, 3.4Ghz, 4.0GHz, 4.5GHz, 5.0GHz , 5.5GHz, 6.1GHz, 6.6GHz, 7.1GHz, 7.7GHz, 8.2GHz, 8.7GHz, 9.2GHz, 9.8GHz, 10.3GHz |
| FrequencyRange | 1.56GHz ~ 10.6GHz |
| AntennaType | Chip |
| NumberofBands | 3 |
| VSWR | 2.5 |
| Gain | 2.6dBi |
| Termination | Solder |
| MountingType | Surface Mount |
| Height(Max) | 0.079 (2.00mm) |
개요
Description
NN03-320 is likely a course code rather than a standard name. If it’s an Introduction to Neural Networks course, the exact syllabus depends on the institution. A typical outline might include:
- Basic concepts: neuron model, activation functions, feedforward nets
- Training: backpropagation, gradient descent, loss functions
- Architectures: MLPs, CNNs, RNNs
- Learning issues: overfitting, regularization, hyperparameter tuning
- Data handling: preprocessing, normalization, datasets
- Evaluation: metrics, validation, cross-validation
- Tools: introductory use of ML frameworks (e.g., TensorFlow, PyTorch)
- Projects: small NN design/implementation on a dataset
Prereqs often include linear algebra, calculus, and programming.
For exact details (learning outcomes, credits, schedule), check your institution’s catalog or syllabus for NN03-320, or provide the university name and term and I can help locate the specific description.
- Basic concepts: neuron model, activation functions, feedforward nets
- Training: backpropagation, gradient descent, loss functions
- Architectures: MLPs, CNNs, RNNs
- Learning issues: overfitting, regularization, hyperparameter tuning
- Data handling: preprocessing, normalization, datasets
- Evaluation: metrics, validation, cross-validation
- Tools: introductory use of ML frameworks (e.g., TensorFlow, PyTorch)
- Projects: small NN design/implementation on a dataset
Prereqs often include linear algebra, calculus, and programming.
For exact details (learning outcomes, credits, schedule), check your institution’s catalog or syllabus for NN03-320, or provide the university name and term and I can help locate the specific description.
Package
NN03-320 could refer to multiple items from different manufacturers. Please specify the product category or vendor. I can then state the exact package type (e.g., reel, tray, pouch, bottle) and its dimensions.
Equivalent
I don’t recognize NN03-320 unambiguously. Please tell me the manufacturer or the chip’s function (e.g., regulator, MOSFET, TVS, amplifier), package markings, or share the datasheet/image. With that I can list direct equivalents (matching electrical specs, pinout and package) quickly.
배송
배송 방법We
DHL, FedEx, TNT, UPS 또는 선택한 다른 운송업체를 통해 글로벌 배송 서비스를 제공합니다.
배송 요금 참조 (DHL/FedEx):
DHL은: 배송 비용은 $25-$45 (0.5kg)에서 2-5 영업일의 추정적 배달 시간이 있습니다.
FedEx는: 배송 비용은 $25-$40 (0.5kg)에서 3-7 영업일의 추정적 배달 시간을 가지고 있습니다.
UPS는: 배송 비용은 $25-$45 (0.5kg)에서 3-7 영업일의 추정적 배달 시간을 가지고 있습니다.
TNT는: 배송 비용은 $25-$65 (0.5kg)에서 3-7 영업일의 추정적 배달 시간을 가지고 있습니다.
EMS는: 배송 비용은 $30-$50 (0.5kg)에서 7-15 영업일의 추정적 배달 시간을 제공합니다.
등록된 항공우편: 배송 비용은 $2-$4 (0.1kg), 예상 배달 시간 5-20 영업일.
지불
Payment Methods
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1. PayPal
2. 신용/직불 카드
3. 전선 전송
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