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대본 생성 및 노래 추천 모델모델 유형: Causal Language Model (AutoModelForCausalLM)양자화 방식: BitsAndBytes를 사용한 4비트 양자화 (4-bit quantization)기능: 대본 생성 및 감정 기반 음악 추천활용: 다양한 장면과 캐릭터 설정에 맞는 대화 생성 & 감정에 적합한 음악 추천학습 데이터https://huggingface.co/datasets/li2017dailydialog/daily_dialog li2017dailydialog/daily_dialog · Datasets at Hugging FaceThe viewer is disabled because this dataset repo requires arbitrary Python code exec..

TensorFlowBasic Optimization with GradientTapeimport h5pyimport numpy as npimport tensorflow as tfimport matplotlib.pyplot as pltfrom tensorflow.python.framework.ops import EagerTensorfrom tensorflow.python.ops.resource_variable_ops import ResourceVariableimport timetrain_dataset = h5py.File('train데이터 경로', "r")test_dataset = h5py.File('test데이터 경로', "r")x_train = tf.data.Dataset.from_tensor_slice..

OptimizationGradient Descentimport numpy as npimport matplotlib.pyplot as pltimport scipy.ioimport mathimport sklearnimport sklearn.datasetsfrom opt_utils_v1a import load_params_and_grads, initialize_parameters, forward_propagation, backward_propagationfrom opt_utils_v1a import compute_cost, predict, predict_dec, plot_decision_boundary, load_datasetfrom copy import deepcopyfrom testCases import ..

Gradient CheckingPackagesimport numpy as npfrom testCases import *from public_tests import *from gc_utils import sigmoid, relu, dictionary_to_vector, vector_to_dictionary, gradients_to_vector%load_ext autoreload%autoreload 2  Dimensional Gradient Checkingforward propagationdef forward_propagation(x, theta): J = theta * x return Jx, theta = 2, 4J = forward_propagation(x, theta)print ("J..

RegularizationPackages# import packagesimport numpy as npimport matplotlib.pyplot as pltimport sklearnimport sklearn.datasetsimport scipy.iofrom reg_utils import sigmoid, relu, plot_decision_boundary, initialize_parameters, load_2D_dataset, predict_decfrom reg_utils import compute_cost, predict, forward_propagation, backward_propagation, update_parametersfrom testCases import *from public_tests ..