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#RL#reinforcement learning#Kafka#Reinforcement#MDP#PyTorch#Deep Learning#dp#개발환경#3dvision#pytorch3d#wandb기초#vqvae#lstmvae#Monocular depth estimation#wandb#AutoML#MDE#model optmization#deep learning 논문#efficiency model#mobilenet v2#deptwiseconv#mobilenetv1#ubuntu20.04#actor-critic#policy gradient#numerical pde#fvm#blackjack with Reinforcement learning#blackjack with python#Monte Carlo vs Dynamic Programming#rl blackjack#Monte Carlo#MC vs DP#RL Cliff#RL example#QLearning#Sarsa vs QLearning#Cliff walking#sarsa vs Q#temporal diffrence#reinfocement#policy improve#MobileNet#RTX3080#TF2#PDE#logstickRegression vs navie Bayes#generative vs discriminative#logstick Regression#Discriminative#Generative#Q-learning#Sarsa#policy evaluation#Monte Carlo method#ml auto#r-cnn#vae#kafka offset#kafka log#jar on window#window service#nssm#doesn't work#spark submit#producer as connector#kafka custom connector#kafka connector#consumer not work#consumer#kafka infra#sparkstreaming#spark exception#spark tip#task not serializable#spark write#not sirializable#tflite#QUANTIZATION#Naive Bayes#automation#Dynamic Programming#논문 리뷰#scala#sweep#fdm#Policy#blackjack#Machine Learning#CONCATENATE#Hook#JVM#bash#Computer Vision#FEM#producer#connector#Torch#auto#ML#SH#crontab#spark#YOLO#TD#cnn#3D#jar#SSD#Java#MC#LOG