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목록Research (67)
Shakerato
Full Code: https://www.kaggle.com/pedrolcn/deep-tensorflow-ccn-cross-validationclass TrainBatcher(object): # Class constructor def __init__(self, examples, labels): self.labels = labels self.examples = examples self.index_in_epoch = 0 self.num_examples = examples.shape[0] # mini-batching method def next_batch(self, batch_size): start = self.index_in_epoch self.index_in_epoch += batch_size # When..
Why do we need validation (validation이 필요한 이유)? In order to estimate how well your model has been trained (that is dependent upon the size of your data, the value you would like to predict, input etc) and to estimate model properties (mean error for numeric predictors, classification errors for classifiers, recall and precision for IR-models etc.) - 모델이 학습이 잘 되었는지 측정하기 위해 A set of examples used ..
What is Colab (Colaboratory) ?- https://colab.research.google.com- Colaboratory는 기계 학습 교육 및 연구 보급을 돕기 위해 만든 Google 연구 프로젝트입니다.- Jupyter 노트북 환경에서 클라우드 환경에서의 설치 및 실행이 전혀 필요하지 않습니다. - Colaboratory 노트는 Google 드라이브에 저장되며 Google 문서 또는 스프레드 시트에서와 마찬가지로 공유 할 수 있습니다.- Colaboratory는 무료입니다.- Now you can develop deep learning applications with Google Colaboratory on the free Tesla K80 GPU- using Keras, Tens..
[Code and Explain] https://github.com/tensorflow/models/tree/master/research/object_detection [Download] https://github.com/tensorflow/models [Related Youtube: sentdex] https://www.youtube.com/watch?v=COlbP62-B-U [Anaconda 3, Create Envirnment for tensorflow 1.4] 1. open 'cmd' as administrator 2. conda update -n base conda 3. conda create -n tensorflow1.4 python=3.5 anaconda 4. [tensorflow-gpu 1..
1. https://www.ffmpeg.org/download.html2. Click Windows icon3. Click 'Windows Builds' text4. Download version 3.4.2, 64bit or 32bit, Static5. Extract zip file, copy /bin/ffmpeg.exe to video folder6. Making video clips using this command ffmpeg -i [Input file name] -ss [start time] -to [end time] [output file name] (example)ffmpeg -i video.mp4 -ss 00:00:03.000 -to 00:00:12.000 clip1.mp4
Limited GPU Memory GPU usually has lesser device memory than host memoryThe latest high-end GPU (such as NVIDIA GPU P100)12–16 GB device memoryHost system memory256GBTrend for deep learning models is to have a “deeper and wider” architectureEspecially, RNN needs a lot of memory 1. First Solution: distributed Deep LearningSource: M. Cho et al., "PowerAI DDL", 2017PowerAI DDL provides a unified in..