분류 전체보기244 Pytorch-Simple learning import pandas as pd import torch import torch.nn as nn import torch.optim as optim from torch.utils.data import DataLoader, TensorDataset # 데이터 불러오기 및 전처리 data = pd.read_csv('gpascore.csv') data = data.dropna() y_data = data['admin'].values x_data = [] for _, row in data.iterrows(): x_data.append([row['gre'], row['gpa'], row['rank']]) # 텐서로 변환 x_tensor = torch.FloatTensor(x_data) y_tensor = torc.. 2024. 1. 26. ZoeDepth: 상대적 깊이와 메트릭 깊이 결합 (공식 구현) ZoeDepth: 상대적 깊이와 메트릭 깊이 결합 (공식 구현) https://github.com/isl-org/ZoeDepth GitHub - isl-org/ZoeDepth: Metric depth estimation from a single image Metric depth estimation from a single image. Contribute to isl-org/ZoeDepth development by creating an account on GitHub. github.com arXiv 2023. [Paper] [Github] Gabriela Ben Melech Stan, Diana Wofk, Scottie Fox, Alex Redden, Will Saxton, Jean Yu, Estelle.. 2024. 1. 24. Keras-simple deep learning 기본적인 사용법의 샘플 import pandas as pd data = pd.read_csv('gpascore.csv') #data.isnull().sum() #빈간갯수? #data.fillna(100)#빈칸을 100을줌 #print(#data['gpa']) #열의값 #print(#data['gpa'].min()) #열의 최저값 #print(#data['gpa'].count()) #열의 갯수 data.dropna()#빈칸, na를 정리해줌 y데이터=data['admin'].values#y=정답 x데이터=[] #x=입력값 배열선언 for i, rows in data.iterrow(): #한 행씩 x데이터.append([rows['gre'],rows['gpa'],rows['rank']])# 추가 [640,1.. 2024. 1. 23. MAGNeT MAGNeT is a text-to-music and text-to-sound model capable of generating high-quality audio samples conditioned on text descriptions. It is a masked generative non-autoregressive Transformer trained over a 32kHz EnCodec tokenizer with 4 codebooks sampled at 50 Hz. Unlike prior work, MAGNeT doesn't require neither semantic token conditioning nor model cascading, and it generates all 4 codebooks us.. 2024. 1. 21. 이전 1 ··· 14 15 16 17 18 19 20 ··· 61 다음
